CWEX (Crop/Wind-Energy Experiment): Measurements and numerical modeling
of the interaction between crop production and wind power
by
Daniel Andrew Rajewski
A dissertation submitted to the graduate faculty
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Major: Meteorology
Program of Study Committee:
Eugene S. Takle, Major Professor
William A. Gallus, Jr.
Raymond W. Arritt
William J. Gutowski
S. Elwynn Taylor
Iowa State University
Ames, Iowa
2013
Copyright © Daniel Andrew Rajewski, 2013. All rights reserved.
ii
DEDICATION
I dedicate this work to our Lord and Savior Jesus Christ, king of the universe and the
Lord of turbulence and of stability. I remember also our heavenly mother who is present
in the wind guiding us along to her Son. Ave Maris Stella! The rushing wind of the
Holy Spirit is ever upon the earth and all creation awaits the full realization of renewal.
Maranatha! May these academic inspirations be used by all to promote the dignity of all
of God’s creation while improving the common good to prepare for the full realization
of His kingdom. I offer a prayer of praise and thanksgiving in the words of Saint Francis
of Assisi:
Canticle of Brother Sun
Most High Almighty Good Lord, Yours are praise, glory, honor and all blessings; To
You alone! Most High, do they belong, and no man is worthy of speaking Your Name!
Be praised, Lord, with all Your creatures, and above all our Brother Sun, who gives us
the day by which You light our way, and who is beautiful, radiant and with his great
splendor is a symbol to us of You, O Most High!
And be praised, Lord, for our Sister Moon and the Stars. You created them in the
heavens bright, precious and beautiful!
And be praised, Lord, for our Brother the Wind and for the air and the clouds and for fair
weather and for all other through which You sustain Your creatures.
And be praised, Lord, for our Sister Water, so useful, and humble, and chaste!
And be praised, my Lord, for our Brother Fire, through whom You light up the night and
who is handsome, joyful, robust, and strong!
And be praised, my Lord, for our Sister, Mother Earth, who supports and carries us and
produces the diverse fruits and colorful flowers and trees!
Praise and bless the Lord and give thanks to Him and serve Him with great humility!
Be praised, my Lord, for our Sister, bodily Death from whom no living man can escape!
Woe only to those who die in mortal sin; but blessed are those who have done Your most
holy will; for the second death can cause them no harm! Amen.
iii
TABLE OF CONTENTS
Page
DEDICATION ..........................................................................................................
ii
LIST OF FIGURES ...................................................................................................
vi
LIST OF TABLES ....................................................................................................
xiii
NOMENCLATURE ..................................................................................................
xix
ACKNOWLEDGEMENTS ......................................................................................
xx
ABSTRACT .............................................................................................................. xxiii
CHAPTER 1.
GENERAL INTRODUCTION ......................................................
1
Introduction
..................................................................................................
Dissertation Organization ....................................................................................
Literature Review ................................................................................................
References ........................................................................................................
Figures
....................................................................................................
1
2
3
18
29
CHAPTER 2. CWEX: CROP/WIND-ENERGY EXPERIMENT:
OBSERVATIONS OF SURFACE-LAYER, BOUNDARY-LAYER
AND MESOSCALE INTERACTIONS WITH A WIND FARM
Abstract
........................................................................................................
Introduction ........................................................................................................
Site Description ...................................................................................................
CWEX measurement design ...............................................................................
Detection of turbine-induced surface flux differences ........................................
Turbine wake influences on wind and turbulence profiles: a case study, night
of 16-17 July 2011 ..............................................................................................
Turbine influences on fluxes of heat and carbon dioxide ...................................
Remaining science questions and future campaigns ..........................................
Acknowledgements .............................................................................................
References ........................................................................................................
Figures
........................................................................................................
Tables
........................................................................................................
30
30
31
33
34
36
41
42
44
47
47
51
60
iv
Page
CHAPTER 3. CHANGES IN FLUXES OF HEAT, H2O, AND CO2 CAUSED BY
A LARGE WIND FARM
Abstract
........................................................................................................
Introduction ........................................................................................................
Materials and Methods ........................................................................................
Theory/Calculation ..............................................................................................
Results
........................................................................................................
Discussion .......................................................................................................
Conclusion ........................................................................................................
Acknowledgements .............................................................................................
References ........................................................................................................
Figures
........................................................................................................
Tables
........................................................................................................
66
67
70
71
76
83
85
86
87
91
98
CHAPTER 4. AIR TEMPERATURE AND HEAT FLUX MEASUREMENTS
WITHIN TWO LINES OF TURBINES IN A LARGE
OPERATIONAL WIND FARM
Abstract
........................................................................................................
Introduction ........................................................................................................
Materials and Methods ........................................................................................
Analyses procedures ............................................................................................
Results
........................................................................................................
Discussion .......................................................................................................
Conclusion ........................................................................................................
Acknowledgements .............................................................................................
References ........................................................................................................
Figures
........................................................................................................
Tables
........................................................................................................
108
108
108
111
112
116
124
126
127
127
131
139
CROP AND TILLAGE ROUGHNESS SENSITIVITIES TO
WIND POWER .............................................................................
Abstract
........................................................................................................
Introduction ........................................................................................................
Materials and Methods ........................................................................................
Theory/Calculations ............................................................................................
Results
........................................................................................................
Concluding remarks ............................................................................................
Acknowledgements .............................................................................................
References ........................................................................................................
Figures
........................................................................................................
Tables
........................................................................................................
66
CHAPTER 5.
154
154
155
160
162
166
173
174
174
178
186
v
Page
CHAPTER 6.
GENERAL CONCLUSIONS ........................................................ 190
General discussion............................................................................................... 190
Recommendations for Future Research .............................................................. 194
References ........................................................................................................ 197
vi
LIST OF FIGURES
Page
CHAPTER 1.
Figure 1. Overview schematic of several hypothetical influences of wind
energy on crop agriculture.
29
Figure 2. Overview schematic of several hypothetical influences of
agricultural crop production on wind energy.
29
CHAPTER 2.
Figure 1. Overlay of the wind farm boundaries with an expanded view of the
measurement locations for CWEX-10 and CWEX-11.
51
Figure 2. Climatological 10-m wind roses of the Marshalltown airport for
the months of (a) January and (b) July.
.
52
Figure 3. Wind roses for (a) CWEX-10 6.5-m winds at the reference flux tower
(NLAE 1), (b) CWEX-11 10-m winds at the reference flux tower
(NCAR 1), and (c) CWEX-11 80-m winds from the upwind wind cube
(WC 68). Dashed lines denote wind directions for turbine wakes on
downwind stations .
53
Figure 4. CWEX-10 differences (downwind – upwind) of normalized wind speed
and normalized TKE, 9-m air temperature, and uncorrected sensible heat
flux as functions of upwind flux tower thermal stability (z/L0): for the
westerly no-wake case (a),(d),(g),and (j); for the SW B2 turbine wake case
(b),(e),(h),and (k); and for the SSE case between the wakes of turbine B2
and B3 (c),(f),(i),and (l).
54
vii
Figure 5. Contours of wind speed from (a) WC 68, (b) WC 49, and (c) calculated
difference in wind speed attributed to the wind turbine wake effect.
Overlay with a solid black line is for the top of the rotor height and the
dashed black line indicates the hub height.
56
Figure 6. Time-height cross sections of (a) upwind TKE profile, (b) downwind
TKE profile, and (c) difference between (a) and (b). Overlay with a solid
line is for the top of the rotor height and the dashed line indicates the hub
height.
57
Figure 7. Differences during the night of 16-17 July 2011 for (a) wind speed
(b) TKE, (c) air temperature, and (d) sensible heat flux. Note that at
the ISU tower wind speed and temperature are collected at the 8-m level
while the NCAR tower wind speed and temperature are observed at 10 m
Time-height cross sections of (a) upwind TKE profile, (b) downwind
TKE profile, and (c) difference between (a) and (b). Overlay with a solid
line is for the top of the rotor height and the dashed line indicates the hub
height.
58
Figure 8. Comparison of differences in 30-min averaged fluxes of sensible
heat (a-b), latent heat (c-d), and CO2 (e-f) between NCAR 3 and
NCAR 1 for a southerly wind case on 18 July 2011 and for a transition
from southerly to northwesterly direction on 2 Aug 2011. NCAR 3
10-m wind direction vectors are overlaid for each image. Dashed lines
in (b), (d), and (f) denote the period of cloudiness during the transition
of winds from southerly to northwesterly on the early afternoon of 2
Aug 2011.
59
viii
CHAPTER 3.
Figure 1. Conceptual model of wind turbine wake flow and the corresponding
effect on crop fluxes.
91
Figure 2. Energy budget closure trend line and correlation coefficient (R2) of
(Rnet-G vs. H+E+Fc) among the upwind and downwind flux stations:
NLAE 1-NLAE 2 and NCAR 1 and NCAR 3.
92
Figure 3. Graphical representation of ‘wake’ wind direction sectors for the
downwind flux tower overlaid on the CWEX-10/11 measurement
locations. These directional categories are represented in the flux
difference directional day and nighttime composites.
93
Figure 4. Wind direction-sector accumulated downwind-upwind differences in
sensible heat flux (H) for the DAY and the NIGHT case. The
downwind-upwind mean difference and standard deviations of the
difference in H are overlaid for each directional composite and distance
from an individual turbine.
Figure 5. As in Figure 4 but for latent heat flux (E).
94
95
Figure 6. Wind direction-sector accumulated downwind-upwind differences in CO2
Gross Primary Production (GPP) for the DAY case and net ecosystem
respiration (Re) for the NIGHT case. The downwind-upwind mean
difference and standard deviations of the differences in CO2 flux (fc) are
overlaid for each directional composite category and distance from an
individual turbine.
96
ix
Figure 7. Mean and standard deviations of the downwind-upwind differences in u*
for the DAY and the NIGHT cases of directional composite category
and distance from an individual turbine.
97
CHAPTER 4.
Figure 1. Wind direction-sector accumulated downwind-upwind differences in
air temperature for the DAY-NEUTRAL case for the (a) NLAE flux
stations and (b) NCAR flux stations. The downwind-upwind mean
difference and standard deviations of the difference in temperature
are overlaid for each directional composite category and distance
from an individual turbine.
131
Figure 2. Wind direction-sector accumulated downwind-upwind differences in
air temperature for the DAY-UNSTABLE case for the (a) NLAE
flux stations and (b) NCAR flux stations. The downwind-upwind
mean difference and standard deviations of the difference in
temperature are overlaid for each directional composite category and
distance from an individual turbine.
132
Figure 3. Wind direction-sector accumulated downwind-upwind differences in
air temperature for the NIGHT-STABLE case for the (a) NLAE flux
stations and (b) NCAR flux stations. The downwind-upwind mean
difference and standard deviations of the difference in temperature are
overlaid for each directional composite category and distance from
an individual turbine.
Figure 4. Wind direction-sector accumulated downwind-upwind differences in
sensible heat flux (H) for the DAY-NEUTRAL case for the (a) NLAE
133
x
flux stations and (b) NCAR flux stations. The downwind-upwind
mean difference and standard deviations of the difference in H are
overlaid for each directional composite category and distance from
an individual turbine.
134
Figure 5. Wind direction-sector accumulated downwind-upwind differences in
sensible heat flux (H) for the DAY-UNSTABLE case for the (a) NLAE
flux stations and (b) NCAR flux stations. The downwind-upwind
mean difference and standard deviations of the difference in H are
overlaid for each directional composite category and distance from
an individual turbine.
135
Figure 6. Wind direction-sector accumulated downwind-upwind differences in
sensible heat flux (H) for the NIGHT-STABLE case for the (a) NLAE
flux stations and (b) NCAR flux stations. The downwind-upwind
mean difference and standard deviations of the difference in H are
overlaid for each directional composite category and distance from
an individual turbine.
136
Figure 7. S-SSE NIGHT-STABLE case of the NLAE downwind-upwind mean
differences and standard deviations of differences in (a) 9-m air
temperature, (b) normalized wind speed with respect to the nacelle speed,
(c) sonic heat flux, and (d) normalized TKE with respect to the upwind flux
tower TKE (TKE0) according to the variability of the nacelle speed.
Nacelle speed bins are centered on the ±0.5 m s-1 mark of integer category
from 4 to 12 m s-1.
Figure 8. NIGHT-STABLE case illustrating the variability of mean differences
and standard deviations of differences according to 10-m wind direction
137
xi
at the NCAR 2 station. Directional bins are centered on the ±5° interval
for each 10° bin. In (a) Downwind-Upwind differences of the wind cube
data (WC 49-WC 68) at 40m, 80m, and 120m are overlaid on to the NCAR
downwind-upwind differences and the ISU downwind-upwind differences.
In (b) turbine-added turbulence intensity is determined among all
downwind stations from the NCAR and ISU stations. In (c)
downwind-upwind NCAR station differences of 10-m air temperature.
In (d) downwind-upwind NCAR and ISU station differences of 6.5-m
sonic heat flux. Blue X’s denote the centerline position of the B1, B2,
and B3 turbine wakes at the NCAR 2 station for wind directions near
225°, 180°, and 135° respectively.
138
CHAPTER 5.
Figure 1. (a) rated power curve for the GE 1.5 MW SLE and XLE turbine
and (b) calculated power coefficient using the GE 1.5 MW SLE
power curve.
178
Figure 2. Seasonal changes in wind speed category determined from monthly
average wind speed maps from the Iowa Energy Center and
changes in roughness according to a typical field tillage and
planting schedule for corn and soybean with plowed soil, mulch till,
and no-till surfaces during the off-season.
179
Figure 3. Nightly progression of the low-level wind speed profile for the YSU
(cyan) MYJ (red) QNSE (green), MYNN2.5 (blue) and mean of the
schemes (black). Snapshots correspond to 2000 (LST) in (a),
2200 (LST) in (b), 0200 (LST) in (c), 0700 (LST) in (d),
0900 (LST) in (e), and 1100 (LST) in (f).
180
xii
Figure 4. As in Figure 3 but zoomed in on the 40 to 120-m rotor layer for no-till
surface (solid thick line), soybean surface (thick dashed line),
and corn surface (thin line with X stiple).
181
Figure 5. Normalized power change from the plowed soil condition according to
the rotor layer stability for a geostrophic speeds of 6 m s-1 for (a) no-till,
(b) soybean, and (c) maize roughness and for a geostrophic speed of
10 m s-1 for (d) no-till, (e) soybean, and (c) maize PBL and ensemble
colored boxes same as thick colored lines of Figure 3.
182
Figure 6. Normalized power difference from plowed soil condition and
accumulated power difference for a 48-hr period to analyze PBL
scheme sensitivity to roughness for geostrophic wind speed categories
of (a) 6 m s-1, (b) 10 m s-1, and (c) 16 m s-1 . Standard deviations of the
PBL ensemble averaged quantities are denoted with black error bars.
183
Figure 7. Daily net revenue change from plowed soil condition vs. roughness
for each PBL scheme and the ensemble mean for speed categories
of (a) 6 m s-1, (b) 10 m s-1, and (c) 16 m s-1 and annual net revenue
change for (d) climatological wind year. The standard deviations
of the PBL ensemble averaged quantities are denoted with black
error bars.
184
Figure 8. Combination scenarios of (a) annual revenue for the climatological
wind year for multiple crop type corn (C) and soybean (S) and for
tillage methods (plow, mulch, and no-till) and for (b) the 30-yr lifetime
of the wind farm for various sequence differences of crop rotation and
tilling methods as in (a). The standard deviation of the PBL ensemble
averaged quantities are denoted with black error bars.
185
xiii
LIST OF TABLES
Page
CHAPTER 2.
Table 1 Instrumentation type, sensor height, and location for flux
stations operating during CWEX-10 and CWEX-11. More
detailed specifications of each sensor in the following
footnotes #1-8.
60
Table 2. CWEX-10 means and standard deviations (in parentheses) of the
differences (downwind – upwind) in normalized wind speed for upwind
flux tower thermal stability (z/L0) categories: unstable, neutral, and stable
for the westerly no-wake case; for the SW B2 turbine wake case; and for
the SSE gap case between the wakes of turbine B2 and B3.
Notable differences are indicated with double asterisks.
62
Table 3 CWEX-10 means and standard deviations (in parentheses) of
differences (downwind – upwind) in normalized TKE for upwind flux
tower thermal stability (z/L0) categories: unstable, neutral, and stable for
the westerly no-wake case; for the SW B2 turbine wake case; and for the
SSE gap case between the wakes of turbine B2 and B3. Notable
differences are indicated with double asterisks.
63
xiv
Table 4. CWEX-10 means and standard deviations (in parentheses) of the
differences (downwind – upwind) of 9-m air temperature for upwind
flux tower thermal stability (z/L0) categories: unstable, neutral, and
stable for the westerly no-wake case; for the SW B2 turbine wake
case; and for the SSE gap case between the wakes of turbine B2
and B3. Notable differences are indicated with double asterisks.
64
Table 5. CWEX-10 means and standard deviations (in parentheses) of the
differences (downwind – upwind) of uncorrected sensible heat flux for
upwind flux tower thermal stability (z/L0) categories: unstable, neutral,
and stable for the westerly no-wake case; for the SW B2 turbine wake case;
and for the SSE gap case between the wakes of turbine B2 and B3.
Notable differences are indicated with double asterisks.
65
CHAPTER 3.
Table 1. Wind direction sectors corresponding to the turbine wake or gap (between
turbine) flow for the B1 to B3 turbines on the leading line of turbines at the
wind farm. Composites of these direction sectors are included for when the
turbines were operational or offline. The number of observations in the
DAY and NIGHT cases is included.
.
98
Table 2. Means, standard deviations, the 95% upper and lower confidence interval of
the mean difference, and the skewness and kurtosis of the difference
distributions for (a) DAY and (b) NIGHT downwind-upwind sensible
heat flux differences (H) for each direction composite as presented in
Table 1.
99
Table 3. Same as in Table 2 but for downwind-upwind latent heat flux
differences (E) for each direction composite as presented in Table 1.
101
xv
Table 4. Same as in Table 3 but for downwind-upwind CO2 flux differences (fc)
for each direction composite as presented in Table 1.
103
Table 5. Tests of statistical significance (t-test, and Wilcoxon-pair) for the means of
the flux differences of sensible heat flux (H) for each of the comparison
categories tested in the DAY case (a) and the NIGHT case (b). Statistical
significance is identified by bold red labeling if the mean differences are
not equal according to a probability threshold at or exceeding 95%.
105
Table 6. Same as in Table 5 but for latent heat flux (E).
106
Table 7. Same as in Table 5 but for CO2 flux (fc).
107
CHAPTER 4.
Table 1. Wind direction sectors corresponding to the turbine wake or gap
(between turbine) flow for the B1 to B3 turbines on the leading line of
turbines at the wind farm for (a) NLAE station differences and for (b)
NCAR station differences. Composites of these direction sectors are
included for when the turbines were operational or offline. The number
of observations, (N) in each of the DAY-NEUTRAL, DAY-UNSTABLE
and NIGHT-STABLE conditions is included.
.
139
Table 2. Nacelle speed categories and upwind flux tower Monin Obukhov lengths
(L) of for the S-SSE NIGHT-STABLE condition to determine
relationships with surface flux differences with turbine speed.
Sample sizes are also provided for each category. Nacelle speed
bins are centered on the ±0.5 m s-1 mark of each integer category from
4 to 12 m s-1.
140
xvi
Table 3. 10-m wind direction sectors at the NCAR 2 flux station corresponding
to the turbine wake centerline or edge position (on periphery of wake) for
the B1 to B3 turbines on the leading line of turbines at the wind farm for
the NIGHT-STABLE case.
Directional bins are centered on the ±5°
interval for each 10° bin composite. The number of observations,
(N) is included for each direction sector.
141
Table 4. Means and the 95% upper and lower confidence interval of the
mean differences in air temperature of the downwind-upwind
difference distributions for DAY-NETURAL cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds of
two null cases of westerly wind and turbines offline.
142
Table 5. Means and the 95% upper and lower confidence interval of the
mean differences in air temperature of the downwind-upwind
difference distributions for DAY-UNSTABLE cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds of
two null cases of westerly wind and turbines offline.
Table 6. Means and the 95% upper and lower confidence interval of the
mean differences in air temperature of the downwind-upwind
difference distributions for NIGHT-STABLE cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds of
144
xvii
two null cases of westerly wind and turbines offline.
146
Table 7. Means and the 95% upper and lower confidence interval of the
mean differences in sonic heat flux of the downwind-upwind
difference distributions for DAY-NETURAL cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds
of two null cases of westerly wind and turbines offline.
148
Table 8. Means and the 95% upper and lower confidence interval of the
mean differences in sonic heat flux of the downwind-upwind
difference distributions for DAY-UNSTABLE cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds
of two null cases of westerly wind and turbines offline.
150
Table 9. Means and the 95% upper and lower confidence interval of the
mean differences in sonic heat flux of the downwind-upwind
difference distributions for NIGHT-STABLE cases among the
(a) NLAE flux tower stations and (b) NCAR flux tower stations.
Significant differences are indicated in light red highlight when the
confidence interval of the test condition does not share the bounds
of two null cases of westerly wind and turbines offline.
152
xviii
CHAPTER 5.
Table 1. Average (maximum) hub height speeds for each PBL scheme and the
ensemble averages for geostrophic speed categories from
6 m s-1 to 16 m s-1.
186
Table 2. Comparison of model surface roughness (z0) to characteristic roughness
type in a cropland agricultural setting.
187
Table 3. Average power (a) and maximum power (b) for the 48-hr simulations for
each PBL scheme and speed category for the baseline smooth soil
condition (z0=0.005 m).
188
Table 4. Tillage crop type combination for three tillage methods
(plow, mulch, no-till) and two crops corn (C) and soybean (S) to
evaluate the effect of seasonal change of roughness on the annual
production of wind power.
189
Table 5. Tillage and crop-type combinations for two or three year
sequences corresponding to the effect of roughness over an
assumed 30-yr lifetime of the wind farm.
190
xix
NOMENCLATURE
CU
University of Colorado
CWEX
Crop/Wind-energy Experiment
D
turbine rotor diameter (e.g. 74 m)
DOE
Department of Energy
GPP
Gross Primary Production
LES
Large Eddy simulation
LLJ
Low Level Jet
NLAE
National Laboratory for Agriculture and the Environment
NCAR
National Center for Atmospheric Research
NREL
National Renewable Energy Laboratory
PBL
Planetary Boundary Layer
Re
Ecosystem respiration
SCADA
Super control advisory data acquisition
SLE
Super long extended
TKE
Turbulent Kinetic Energy
XLE
Extra long extended
WRF
Weather Research and Forecasting (Model)
xx
ACKNOWLEDGEMENTS
This dissertation work was possible only with the support and guidance of many
work colleagues and contacts outside of academia. I first recognize all my committee
members: Dr. Raymond Arritt, Dr. William Gallus, Dr. William Gutowski and Dr. S.
Elwynn Taylor, and my major professor Dr. Eugene Takle for all their assistance in
refining my research efforts especially during and after the preliminary oral exam.
I
recognize the contributions of my adviser in so many steps taken to develop this
dissertation product. With the help of Moti Segal, Gene gave me the experience of
learning how to plan and conduct a new research idea. They welcomed my participation
in the proposal building process to begin the CWEX measurement program in 2009 and
promote additional field campaigns in 2010-2011, which lead to my analyses of the data.
I thank Gene for the opportunities to continue developing good research skills and for
his example in the fine art of communicating the science to the wider community in
professional and non-academic circles.
He also led the best public relations effort to
begin taking measurements on the wind farm, and demonstrated the proper repertoire
with land owners/farm operations managers/wind farm operators for the duration of the
CWEX study. I have had a unique opportunity to be part of this research team, which
included and has continued collaboration with outstanding experts from multiple
research and academic institutions. I thank Gene for his continued patience and time in
the dissemination of the research results and the clear interpretation of an ever more
complex conceptual model of wind-turbine/wind farm flow developed from this
research.
I recognize Moti for his assistance in beginning the modifications to the WRF model,
which allowed me to test the sensitivities of roughness on wind power that I present in
this work. Moti devoted several hours of debugging and referencing code resources to
assist in this modeling effort. He also gave preliminary guidance in the first stage of the
CWEX measurements in 2009 and 2010.
Finally I recognize him for his helpful
connections in acquiring preliminary results from the San Gorgoinio wind farm from Dr.
xxi
Neil Kelley and most important his introduction of Russell Doorenbos to the field
planning and preparation of CWEX.
Russ has graciously offered his services in
transportation to the wind farm, scouting the land, and helping take measurements. In
addition to Gene, he has continued building a healthy repertoire with local landowners,
agricultural managers, and the wind farm operator. In his participation of this research, I
have observed several unique events of fog dissipation at the wind farm and other cloudscale peculiarities around wind turbines.
His wiling use of personal guidance
positioning systems, cameras, and other equipment has greatly assisted in the
measurement campaigns. Russ has also developed high quality graphics and animations
for use in publications and for easier interpretation of the data. I also acknowledge him
as a member of the CWEX team of co-authors who has provided helpful insights and
edits in the publications and in the proofreading of one of the dissertation chapters.
I acknowledge several other colleagues for the planning and conducting of the
CWEX-10/11 campaigns. Dr. Julie Lundquist (from the University of Colorado and the
National Renewable Energy Laboratory) and her students Michael Rhodes and Matthew
Aitken have given me an introduction to LiDAR wind profiling instruments and how to
use the data with the surface fluxes that were measured above the corn canopy. The CU
team has been a vital resource in developing new science and refining my understanding
of turbine wakes and the surface impact of turbines.
Dr. John Prueger, Dr. Jerry
Hatfield, Forrest Goodman, and Richard Pfeifier from the National Laboratory for
Agriculture and the Environment provided instrumentation and data recovery support to
the 2010 field study. I also thank John for his assistance in providing our team with a
better understanding of turbulence spectra and wavelets, which unfortunately not
reported in this dissertation work, will be in an upcoming publication. I recognize Dr.
Steven Oncley, Dr. Thomas Horst, Gordon MacLean, and Kurt Knudson from the
National Center for Atmospheric Research for their many hours of preparation and
deployment, instrumentation upkeep, maintenance, phone and email exchanges, and
special maintenance visits to the site in 2011. The NCAR team has also been a valuable
asset for learning post-experiment data correction methods and data file entry. These
xxii
techniques have saved much time and effort in analysis of the CWEX-10/11
measurements. I thank fellow student Kristopher Spoth as his role in the CWEX-11
measurements and the follow-on years of developing good data post-processing
techniques. His efforts in improving (creating) old (new) data scripts have also been a
great assistance to the analysis efforts.
I additionally recognize several former/current graduate students who provided time
or physical labor to the project. I thank Adam Deppe for his assistance in operating the
WRF model and for applying the code to my needs with the vertical resolution that he
used in his research work. He also assisted in the measurement campaigns in 2009 and
2010. Dr. Eric Aligo provided assistance with debugging and running WRF.
I
appreciate all the computing services given by Daryl Herzmann and Dave Flory.
I
recognize many other undergraduate/graduate students for their assistance in deployment
of CWEX-10/11: Kristy Carter, James Cayer, Brandon Fisel, Mallory Flowers, Matthew
Lauridsen, Lars Mattison, Shannon Patton, Arin Peters, Aaron Rosenberg, Renee
Walton, and Catum Whitfield.
I thank James Cayer for his help in interpreting the
LiDAR data analysis of CWEX-11.
I am grateful to the open connections made with the landowners, farm managers, and
the wind farm site managers over 2009-2012. Additional recognition is given to the
owner of the wind farm meteorology data for providing the turbine SCADA data for the
analyses.
Without their cooperation and compliance to our requests for launching
CWEX, none of these results reported here would come from field measurements.
I
thank all my friends and ‘mentors’ outside of my department or outside of the ISU
community for their support and interest in my research efforts. I recognize my sister,
Monica in her editing of portion of this dissertation. I appreciate the love support and
prayers of my family and friends during this time of completing my journey as a Ph D
candidate.
Finally I thank my Creator, Savior, Counselor in One who has opened the
doors in making all this work possible and for the test of patience and gift of time to
complete such a long, intricate investigation into “seeing” the invisible swirls of
turbulence that are present both inside and outside of wind farms.
xxiii
ABSTRACT
The current expansion of wind farms in the U.S. Midwest promotes an alternative
renewable energy portfolio to conventional energy sources derived from fossil fuels.
The construction of wind turbines and large wind farms within several millions of
cropland acres creates a unique interaction between two unlike energy sources: electric
generation by wind and bio-fuel production derived from crop grain and plant tissues.
Wind turbines produce power by extracting mean wind speed and converting a portion
of the flow to turbulence downstream of each rotor. Turbine-scale turbulence modifies
fluxes of momentum, heat, moisture, and other gaseous constituents (e.g. carbon
dioxide) between the crop canopy and the atmospheric boundary layer. Conversely,
crop surfaces and tillage elements produce drag on the hub-height wind resource, and the
release of sensible and latent heat flux from the canopy or soil influences the wind speed
profile.
The Crop-Wind Energy Experiment (CWEX) measured crop fluxes at several
locations within the leading line of turbines in a large operational wind farm and overall
turbines promote canopy mixing of wind speed, temperature, moisture, and carbon in
both the day and night. Turbine-generated perturbations of these fluxes are dependent
on several factors influencing the turbine operation (e.g. wind speed, wind direction,
stability, orientation of surrounding turbines within a wind park) and the cropland
surface (e.g. crop type and cultivar, planting density, chemical application, and soil
composition and drainage qualities). Crop management and tillage practices also modify
the wind resource. Significant power reduction for turbines surrounded by a tall crop
(e.g. corn) is less likely in the summer because hub height winds are weak. Outside of
the growing season, mulch till or least tilled (no-till) surface promotes some
enhancement to the wind resource particularly during the night when a low level jet is
near the turbine rotor layer. Additional strategies are proposed for optimizing the
synergy between crop and wind power.
1
CHAPTER 1. INTRODUCTION
General Introduction
The wind energy industry has undergone rapid expansion in the last decade. Large
wind farms are installed on several thousand if not millions of cropland acres of the U.S.
Midwest.
The rise in domestic energy production facilitates meeting the U.S.
Department of Energy goal of 2030 goal of 20% electric generation from renewable
resources (2008a). Several states are on target to increase wind power generation ten to
forty times the installed capacity of the previous five to ten years. The Central and Great
Plains regions of the U.S. feature a rich area of wind. Westward blocking of flow
alongside the Rocky Mountains and the nighttime interaction of the terrain with inertial
oscillation generate a low-level jet (LLJ) of air. This nighttime LLJ is favorable for not
only wind but also supports agricultural crop production through moisture transport and
precipitation of Mesoscale Convective Systems (MCSs). Agricultural seed production is
a major component of renewable bio-ethanol and biodiesel fuel resources.
Recent
technologies with crop biomass material (crop seeds and post-harvest residues [xylem
and leaves]) allow more efficient and cost-effective cellulosic ethanol production. As
crop biomass and wind power have become major contributors in a domestic energy
portfolio, there is little to no understanding of the interaction between these two energy
resources. This dissertation investigates (1) the impact of wind turbines and large wind
farms on crop microclimate and (2) the impact of crop agricultural management
decisions on wind power.
Determination of this synergy and development of new strategies are imperative for
optimizing economic gain for both crop biomass and wind energy industries. Studying
atmospheric interactions of wind turbines and the crop surface provides a better
understanding of the physical processes within the lower levels of the planetary
boundary layer (PBL). This dissertation addresses the interconnection of agricultural
croplands, wind farms, and the boundary layer using a combination of analyses from two
main research tools. In situ crop flux measurements were taken in the Crop/Windenergy Experiment (CWEX) within an operational scale wind farm. Data from this
2
inaugural field program highlights wind turbine impacts on crop microclimate. To
address possible effects of crop surfaces on wind power and the wind resource, a
simplified numerical model is developed. The model determines surface sensitivities of
tillage and crop type to the daytime and nighttime power resource in a late growing
season or post-harvest period.
Dissertation Organization
In the remainder of this chapter, a two-part literature review is presented. The first
section describes wind turbine and wind farm interaction from the role of power,
aerodynamics, and discussion of scalar fluxes in a closing framework. This framework
is used to extrapolate several impacts of wind turbines wakes within wind farms and
effects of turbines and wind farms on the crop microclimate. The second part of the
review links surface crop heterogeneities to influence on the wind energy resource and
atmospheric conditions that impede optimal electrical production.
Chapter 2 addresses
the specific scope of turbine/wind farm impacts on crops in the CWEX introductory
paper published in the Bulletin of the American Meteorological Society. Chapter 3 is
taken from a manuscript prepared for Agricultural and Forest Meteorology and
investigates the effects wind turbines/wind farms modify crop energy and CO2 fluxes. A
companion paper for Chapter 4 focuses on single turbine and double line of turbine
impacts on temperature and heat flux. Chapter 5 offers a conceptual evaluation of how
crop surface roughness regulates hub-height speeds and other turbine layer
characteristics (e.g. wind speed and directional shear, and thermal stratification), which
influence turbine performance. A summary and other concluding recommendations are
described in Chapter 6.
3
Literature Review
Wind turbine/wind farm impact on crop agriculture
History of wind farm development
This section offers a basic understanding of turbines, turbine wakes, and wind
farms over the last thirty years. Improved technologies of blade and hub design over this
same period have led to an increase in on-shore and offshore wind power in several
regions across major continents (Europe, Asia, and North America). Isolated wind
turbine experiments in wind tunnel and field-testing confirm a basic understanding of
flow around blades and a 1st-order representation of flow downstream of small rotors
(summarized by Vermeer et al. 2003). A large array of 15-m hub height with 23-m rotor
diameter turbines were installed in the San Gorgonio pass in California as the first
industry scale wind turbines in the early 1980s. Kelley (1989) determined preliminary
differences in wind speed and turbulence intensity from the upwind and downwind
edges of the 41-line wind farm. Isolated turbine wake studies followed in Europe (e.g.
Helmis et al. 1995; Högström et al. 1988; Kambezidis et al. 1990; Magnusson and
Smedman 1994,1999; Papadopoulos et al. 1995; Whale et al. 1996) and the U.S.
(Connell and George 1982; Kelley 1985, 1989).
Field experiments in the 1990’s by
Dr. Rebecca Barthelmie of the Danish Risøe Institute, guided the development of
modern operational-scale wind farms. In Iowa in the late 1990’s, a variety of tax
incentives supported the construction of the first wind farm in the Midwest near Storm
Lake.
Several isolated turbines and small wind farms were installed by local
communities and near surrounding lakeshore lines in the following years. Several areas
of the U.S. Midwest began wind farm construction in the first full decade of the 21st
century. Wind farm expansion continues in advance of the DOE renewable energy
goals.
Investigation of turbine wakes
Each turbine produces a zone of reduced wind speed and enhanced turbulence,
defined as a ’wake’. Turbine wakes from the first leading lines of turbines impact power
4
production for turbines farther downstream within a wind farm. Wakes are documented
at hub-height between the top and bottom blade pass (i.e. for an operation-scale 1.5 MW
turbine, a standard hub height is 80-m with the rotor depth between 40 to 120 m). The
Danish Risøe group investigates offshore wind farm wakes from both a few lines of
turbines (Barthelmie et al. 1996, 1999, 2003, 2007a) and large wind farms (e.g.
Barthelmie et al. 2004, 2009, 2010). The field measurements from the tower mast and
ship sodar agree on the monotonic decrease in the velocity deficit and wake turbulence
downstream of each turbine line proposed in past studies
(Ainslee 1988; Frandsen et
al. 1996; Magnusson and Smedman 1994; Taylor 1990).
Ainslee (1988) summarizes how a wake spreads out from one turbine. The
minimum velocity behind the turbine is reached at distance of 1-2 D downstream. This
is related to the relaxation of the pressure gradient from the rotor. Individual blade tip
vortices from the turbine decay at 2-3 D downstream. The strong sheared zone outside
the rotor swept area develops into a core vortex structure at a distance of 3-5 D
downstream. A single wake decays according to a Gaussian-like profile. The velocity
deficit is gradually reduced as the wake moves downstream and interacts with nonturbine ambient scales of turbulence.
Near the surface there is an over speeding
‘shadow’ effect downwind of 1-2 D from the tower base. At the rotor layer height flow
acceleration occurs in the airflow between two turbines (Whale et al. 1996). Increased
ambient turbulence and wind speed reduce the tower shadow over-speeding effect and
decrease an outward directional reflection of wind approaching the rotor (Helmis et al.
1995). Christiansen and Hasager (2005, 2006) with synthetic aperture radar (SAR)
measurements from satellite and aircraft confirm that the wake turbulence distorts ocean
waves at the downwind edge of the Horns Rev wind farm. The SAR-detected wakes
validate the effect of wind farm distance on wake decay and the recovery of the velocity
and turbulence intensity to reference upwind conditions.
Offshore studies from the Horns Rev and Nysted wind farms calibrate wind
siting software composed of single column-steady state flow models (e.g.
ECN
WAKEFARM, GHWindFarmer, and Risoe WAsP). Additional computational fluid
5
dynamics (CFD) modeling (e.g. CENER, NTUA, CRES) with a 2-D and 3-D
understanding of wake flow characterization has also been applied to the Horns Rev and
Nysted measurements. However, these codes poorly depict the power performance
change across a wind farm such as when the wind direction is moving diagonally across
a square shaped array of turbines (Barthelmie et al 2004, 2007b; Rados et al. 2001).
Single column models under predict the power losses in the middle of the wind farm,
whereas CFD simulations favor an over prediction of wake turbulence effects on power
production.
Although CFD simulations over predict power losses, the variety of
approaches in parameterization of turbine wakes offers a better understanding of turbine
and wind farm wake impacts for a steady-state atmospheric neutral flow condition (e.g.
Sanderse et al. 2011).
3-D parameterizations of rotating turbines within a large eddy
simulation (LES) apply to recent tests of on-shore wind farm configurations (Cabezón et
al. 2009; Calaf et al. 2011; Churchfield et al. 2010,2012; Jimenez et al. 2007; Lu and
Porte-Agél 2011; Migoya et al. 2007; Politis et al. 2012.
Recent wind tunnel studies contribute to a better understanding of steady state
conditions of wind farm wake aerodynamics and effect on scalar fluxes (e.g. Cal et al.
2010; Chamorro and Porte-Agél 2009, 2010a, 2010b, 2011; Markfort et al. 2012, Zhang
et al 2012a, 2012b, 2013). These depictions of wakes offer a finer scale of aerodynamic
interaction with turbine components. However, the simulations are limited in practical
application to the atmospheric behavior of the diurnal cycle, which does not follow a
steady state neutral, unstable, or stable pattern).
Several atmospheric factors govern
wind turbine wakes and these are next described in detail.
Ambient factors controlling wake aerodynamics
The profile characteristics of wind speed, wind direction, and temperature and
moisture additionally influence turbine blade stresses and loading.
The impact on
power and turbine performance are attributed to several factors: (1) turbinemanufactured specifications of power and thrust, (2) upwind hub height wind speed (3),
upwind wind direction, and (4) atmospheric stability.
6
(1) Turbine designed specifications
Each manufactured turbine has a different configuration of tower, blades and
nacelle components to design a structure that will produce electricity at an installed
(rated) production (e.g. 1.5 MW annual production) Blades are designed with certain
aerodynamic properties to meet these rated requirements. Rigidity, texture, pitch, and
chord angle are important considerations in determining optimal lift on the blades for
maximum power generation and blade structural reliability. Variations of flow speed on
the blade change the wake generated behind the turbine rotor. Wind turbines operate on
stall-regulated or variable-pitch generators and for both of these configurations variable
blade-rotation speeds will change the intensity of the rotor-generated turbulence. At
speeds below the rated power both generator configurations offer similar performance.
However, for the stall-regulated generator, a very high speeds the blade design decreases
power output to safeguard the structural integrity of the turbine (Muljadi et al. 1996,
2012). In the variable-pitch system, the blade pitch is increased when the hub speed is
above the rated speed (e.g. 11-14 m s-1) to keep blade stresses low and maintain the
same power output up until the cut-out speed (around 22-25 m s-1) . Lift and drag on the
blades is related to the turbulence produced by the blade tips and root span of the blade.
(2) Hub-height wind speed
The ambient hub-height wind speed determines the rate of blade rotation to keep
a steady power production at the installed capacity (e.g. 1.5 MW).
Each turbine
produces power when the wind speed is large enough to rotate both the blades and the
mechanical gears inside the turbine hub.
This cut-in speed is around 3-4 m s-1
depending upon turbine models. Near the cut-in speed, the blade rotation requires a
large amount of thrust as the blades are turned less into the wind. This high thrust
correspondingly produces a large amount of turbulence along the blade span and the
blade tips (Vermeer et al. 2003). These blade vortices are the visible evidence for the
turbine wake. Only a few wind tunnel and small scale testing of turbines (e.g. Medici
and Alfredson 2006; Nemes et al. 2012; Yang et al. 2011) have confirmed the exact
7
structural differences of the turbulence within the wake. The wake vortex rotates in the
cross-wind component downstream from the turbine in the opposite direction of the rotor
rotation (Connell and George 1982). Around the rotor, high generation of turbulence is
produced by the speed shear difference between the rotor swept area and the undisturbed
flow field (e.g. Helmis et al. 1995; Högström et al. 1988; Papadopoulous et al. 1995).
This turbulence also moves downstream in the direction of the counter-rotating swirls
produced by the shedding of eddies from each blade tip and blade span.
Numerical simulations (Fletcher and Brown 2010; Larsen 2007; Sanderse et al.
2009) demonstrate the strong dependence of hub height wind speed to wake expansion
intensity (laterally and vertically) and generation/dissipation characteristics (e.g. distance
to merging of turbulent filaments from each blade, separation distance of the rotating
swirls traveling within the wake, and dissipation distance of the entire wake). Large
spatial and temporal anisotropies are noted in turbine wakes (Crespo and Hernandez
1996; Gomez-Elvira et al. 2005). This poses a continuing challenge in simulation of
individual turbine wakes and multiple interacting wakes. The characteristics of the
upstream wake are important in determining power losses and the structure of successive
wakes from turbines farther downstream in the wind park.
Several field measurements around isolated turbines (Helmis et al. 1995;
Högström et al. 1988; Magnusson and Smedman 1999; Whale et al. 1996) indicate
strong variations in the wake velocity deficit and turbulence intensity for lower or higher
hub-height speeds and therefore higher or lower thrust coefficients. As the turbine
produces power under higher speeds, it is understood that the amount of turbulence
generated by the turbine will also reach peak intensity. Adams (2007) parameterized the
effect of a large wind farm in Northwest Manitoba by using a conservation of energy
approach between thrust, electrical production, small gearbox inefficiencies and the
remaining portion of turbine-influenced momentum was transferred to turbulence.
Based on this conceptual study, it is plausible that turbine turbulent generation should be
highest under low to moderate speeds (between the cut-in and rated speed) and gradually
decrease in intensity as higher speeds require less turbine thrust to maintain the rated-
8
power.
Högström et al. (1988) and Kambezidis et al. (1990) found turbine-generated
turbulence significantly less than turbulence on the edges of the wake indicating a zone
of high speed shear. Shear generation of turbulence increases for higher wind speeds.
This effect forms from the static pressure field around the upwind and downwind side of
the rotor (Ainslee 1988; Taylor. 1990).
(3) Wind direction
The angle of approach of the wind to a turbine within a wind farm also has
implications on the characteristics of the wake. In the wake centerline there is little
rotation and turbulence intensity is lower than on the outer edges of the helical vortex
structure (Kambezidis et al. 1990).
In both power measurements from offshore wind
farms (Barthelmie et al. 2009, 2010) and corresponding numerical simulations (e.g.
Calaf et al. 2011) the direction at which wakes move across a wind farm array will
influence the power resource for the turbines father downstream in the wind park.
Turbine wakes from the leading turbine line move downstream, intersect the second line
of turbine wakes, and enhance the overall wake structure moving into the additional
lines of turbines. However, when turbine wakes have passed through multiple lines of
turbines lateral merging of the wakes occurs and this diffusion of the wake is favored
when the ambient wind direction is oriented at an oblique angle from the wind farm
array. In the power analyses of Barthelmie et al. (2009, 2010) turbine wakes from the
Horns Rev and Nysted wind farm studies are presumed to expand from the rotor
according to a 5° to 15° window. The expansion factor of the wakes is also dependent
on turbine thrust and therefore hub-height wind speed.
Power is substantially reduced (40-60%) in the 2nd through 4th lines of turbines
when the center lines of wakes are feeding into succeeding lines of turbines (e.g.
Frandsen et al. 2006). As previously mentioned oblique flow angles with orientation to
the array of the wind park increases lateral merging of wakes. The intersection of
multiple wakes facilitates greater power recovery at a shorter distance 4-5 turbine lines
vs. 7 lines when turbine wakes are symmetrically aligned. (A quasi-steady turbine
boundary layer is presumed to form in which the wake layer thoroughly mixes into the
9
boundary layer above the rotor and at the surface (Barthelmie et al. 2009; Calaf et al.
2011; Frandsen et al. 2006; Meneveau 2012; Meyers and Meneveau 2012). This new
type of surface-turbine-boundary layer is known to occur downstream of at least seven
lines of aligned turbines within a wind park and corresponds to the point where the
power drop levels off (Barthelmie et al. 2004, 2009).
The effect occurs at a shorter
distance within a wind park when there is a staggered arrangement of turbines, which
facilitates more lateral mixing of turbine wakes (e.g. Sanderse et al. 2009; Troldborg et
al. 2011).
(4) Atmospheric stability
Similar to a pollutant plume, turbine wakes may be treated as a vector and
meander within the larger scale flow fields outside the influence of wind turbines/wind
farms.
In the daytime, wakes can expand according to isotropic properties of the
ambient turbulence as in neutral conditions and intersect with the surface at 10 D or so
downstream of the parent turbine (e.g. Frandsen et al. 2006). A sunny period with
conditions of low wind speed may favor thermally unstable conditions where the wake is
observed to meander from the center line position and may also reach the surface at a
much closer distance (2-5 D) from the turbine (Medici and Alfredson 2008). Wake
meandering and non-neutral conditions remain a challenge to simulate (Lange et al.
2003).
At night, the ambient scales of turbulence in the stable boundary layer are
approximately tens of meters or less. Therefore, the turbine sized eddies (50-100 m)
may remain lofted above the surface for a considerable portion of the overnight hours.
Strong cooling of the surface also induces shears in speed and direction within the rotor
layer so that the wake exhibits sheared characteristics (Chamorro and Port-Agél 2009;
Lu and Porte-Agél 2011).
The structure of the wake, wake merging, and wake decay
are dependent on the efficiency of the ambient scales of turbulence in re-establishing
quasi-equilibrium to the flow and this mixing of energy is also related to the intensity of
the wind speed. In the absence of sheared flow, nocturnal turbulence is quite low and
turbine wakes require more distance (beyond several tens of D) for the flow to return to
10
near ambient levels of wind speed and turbulence (e.g. Magnusson and Smedman 1994).
However, with the presence of a sheared wind profile, wake dissipation across the blade
top and bottom pass height is enhanced by the strong asymmetry in speed and in
direction with height. Small temporal and spatial scales in nocturnal turbulence present
an additional challenge to wake characterization. Simulations of stable boundary layer
evolution (e.g. Zhou and Chow 2011a) depict different structures of ambient turbulence
with and without a LLJ, with quiescent boundary layers or with residual turbulence
above the surface. Intermittency of turbulence is a key issue in the understanding of
individual wakes and complex wind-farm wake interactions (Dvorak et al. 2012).
Intermittency in nocturnal turbulence is related to the low-level wind and temperature
profile which depend on the strength of the wind speed and the height above the surface
of the speed maximum (Pichugina and Banta 2010; Sun et al. 2012).
Surface impacts by wakes
Surface measurements within operational scale wind farms are few.
The
atmospheric stability determines whether a turbine wake remains elevated or at what
downwind distance the wake intersects the surface. In both wind tunnel and numerical
models, most field measurements and simulated wind farm results are restricted to the
turbine layer and above.
Surface impacts on temperature, heat flux, and the
aerodynamics were measured in isolated turbines in Greece, Denmark, Sweden, Spain,
and the aforementioned San Gorgonio wind farm. Högström et al. (1988) determined
roughly a doubling of heat flux and other triple-moment heat-momentum correlation
terms during a period of waked flow from the Näsudden 2MW turbine. Similar results
are alluded to with smaller 50 kW turbines (Kambezidis et al. 1990; Papadopoulos et al.
1995; Whale et al. 1996) by measuring a linear increase in the temperature structure
coefficient, CT2. This coefficient traditionally used to relate turbulent heat flux from
temperature scintillometry is increased by 10-20% and denotes isotropic mixing during
the periods when a turbine wake was near the instrumentation.
11
Baidya Roy and Traiteur (2010) report from the 1987 nighttime measurements of
the San Gorgonio wind farm that wind turbines warm the downwind edge of the farm by
at least 0.5 °C.
The wind farm also features an aggregate 100 W m-2 of accumulated
warming for much of the nighttime (personal communication, N.E. Kelley, 2008).
Remote sensing studies by Zhou et al. (2012a, 2012b) and Walsh-Thomas et al. (2013)
depict up to 0.5-0.75 °C of nighttime warming in the West Texas region and about 1.02.0 °C of warming in the San Gorgonio pass, respectively. In addition to conceptual
studies, several LES codes depict warmer near-surface temperatures of 0.5 to 1.0 °C.
These temperatures occur at a distance of less than 5 D downstream from turbines lines
in large wind farm arrays. This effect is also measured in wind tunnel configurations
(Markfort et al. 2012; Zhang et al 2012b, 2013).
Differences in surface heat flux attributed to turbine forcing are sensitive to the
diurnal cycle, the location from a turbine and/or location within the wind farm.
Chamorro and Porte-Agél simulated up to 60 Wm-2 higher heat flux downwind of a wind
turbine array. They attribute this warming to the wake turbulence lasting for a longer
distance and higher intensity in a stably stratified layer (2010).
For a convective
boundary layer wind tunnel configuration Zhang et al. (2013) depicts a 10-15 W m-2
downward counter gradient flux difference from ambient levels on the left side of the
rotor and an upward flux 10-15 W m-2 difference on the right side of the rotor. The
rotation of the flux difference is not evident when the turbines are arranged in a
staggered grid. Calaf et al. (2011) and Lu and Porte-Agél (2011) demonstrate that the
change in heat flux is related to the ratio of mixing within the turbine layer to that
occurring below the turbines. For fully developed wind-farm flow, a ‘sheet’ of turbine
wakes reduces the vertical heat flux exchange by 15% from the undisturbed reference
flux in nighttime conditions. In addition, lower (higher) daytime (nighttime) humidity
and enhanced daytime evapotranspiration are depicted in meso-scale and global scale
parameterized influences of wind farms (Adams 2007; Baidya Roy et al. 2004, Baidya
Roy 2011; Cevarich and Baidya Roy 2013; Fitch et al. 2012, 2013a, 2013b; Keith et al.
2004; Kirk-Davidoff and Keith 2008; Wang and Prinn 2010).
12
When describing turbine impacts on crops, it is therefore feasible to envision a
situation where turbine-rotor turbulence will give the largest impact on a crop canopy.
This condition should occur when hub height speeds are at and above the cut-in speed
but less than the rated speed. Slow to moderate hub-height speeds (e.g. between 4-10 m
s-1) occur over much of the growing season in both day and night periods.
In the
daytime, hub-height speeds are near or below the cut-in speed during the early morning a
few hours after sunrise. For a typical day free of clouds and precipitation, a low or nopower status in the early morning coincides with the breakup of the nighttime
temperature inversion. Low speed air from the surface is transported upward by warm
convective eddies during this period.
Wind turbines remain in a low/no power
generation until these eddies have reached the top of the boundary layer and replenish
high speeds into the wind farm at some time between the late morning and early
afternoon (e.g. Stull 1988). The direction of the wind influences the orientation of the
wake with the surface. The mixing at the edges of the wake can affect the crop rather
than when the centerline position of the wake is overhead of the canopy. Atmospheric
stability determines whether the wake interacts with the crop canopy and at what
distance downstream from the turbine. Turbine impacts on crop microclimate could be
highly variable depending on the type of growing season (dry vs. wet) and according to
the time of day/night.
Figure 1 illustrates the conceptual role of the wide variety of
impacts turbines and large wind farms may have on crop microclimate.
Crop agriculture impact on wind power
Field management variations in surface fluxes
The sensitivity of the wind power resource to crop management practices is
presented with (1) crop surface heterogeneities impact on the surface energy balance and
fluxes to the atmosphere and (2) the corresponding response of these flux changes over
the entire boundary layer profile for both day and night conditions.
Surface roughness (z0) and tillage influence turbulent exchanges of momentum,
heat, moisture, and other aerosol constituents (e.g. carbon dioxide (CO2)) with the
13
boundary layer. The roughness elements create drag on the wind profile and therefore
directly affect hub-height wind speed. In both the growing season and the off-season
periods, crop canopies and tillage indirectly modify the hub height wind through diabatic
regulation of the wind speed profile (e.g. Stull 1988). A rougher surface indicates more
turbulent mixing, which may cool (warm) the surface in the daytime (nighttime). A
smooth plowed soil offers the least resistance to the momentum drag. The increased
radiation on a surface with no residue enhances soil evaporation and warms the surface.
Mulch tillage, which covers the majority of a soil, increases surface reflectivity and
thereby reduces sensible heating. Vertical mulch (i.e. strip till or no-till practices) offer
less soil disruption and less erosion. The added roughness may change reflectivity and
heat exchanges over small but defined areas around the crop residue (e.g. Horton et al.
1994, 1996).
A vegetative canopy contains additional aspects of taller roughness
elements, and diabatic regulation of the surface heat flux. Exchanges of heat and water
above the canopy are dependent on atmospheric conductance (according to the
properties of ambient relative humidity, wind speed, and solar radiation), the phenology
of the crop, and the available moisture source within the soil profile (e.g. Campbell and
Norman 1998).
Variations in soil temperature, textural composition, and micronutrient
composition also influence the exchanges of subsoil moisture transport to the crop and
the atmosphere.
Management strategies of crop cultivar, planting density, and
frequency and type of chemical application (e.g. fertilizers, herbicides, pesticides and
fungicide) also influence crop response to fluxes of heat, water, and CO2.
These
management practices are closely dependent on the crop type, cultivar and properties of
the soil including drainage quality (ISU 2005). The increased cultivation of bio-fuel
crops (e.g. corn, sweet sorghum, switch grass, miscanthus) to facilitate domestic energy
independence from fossil fuels also may affect millions of acres of cropland where wind
farms have been constructed.
Recent years of agricultural floods, drought, and other
impacts of climate change on the crop growing season may increase uncertainty to the
wind resource (Klink 2007; Pryor et al. 2009). Delays in the scheduling of planting,
14
replanting, or harvesting a crop and delays in the seasonal maximum (April/October) or
minimum (July-August) wind speeds may present conflicting impacts on the wind
resource.
Figure 2 illustrates the umbrella of crop-related impact on wind power
availability.
Assessment of wind forecasts: advances and needed improvements
Accurate wind forecasts of hub-height wind speed, wind shear and thermal
stability within the rotor layer are necessary to improve power optimization of wind
farms and facilitate future wind-farm development. A 1% underestimation of the wind
resource may result into several million dollars in lost revenue over the 20-30 yr lifetime
of a wind farm (U.S. DOE 2008b). Therefore, forecasting improvements are obvious
economic benefit to the wind industry. Wind forecasts in the U.S. and many other
regions of the world feature two major regimes of daytime and nighttime PBL behavior
and one transition zone each between the day and night periods. The timing and
intensity of a low-level ramp up or ramp down in wind speed can be related to the
evolution of the wind profile during a transition phase between daytime unstable/neutral
conditions to nighttime stable/neutral conditions or vice versa (Deppe et al. 2013).
In the U.S. Midwest, the wind profile is categorized by a homogenous mixed
layer in the daytime period caused by the effect of surface frictional drag and the
diabatic regulation of the wind profile from surface fluxes of sensible and latent heat. In
the nighttime, the cooling of the air immediately above the surface erodes the vertical
mixing between the surface and the lower portions of the daytime boundary layer. An
imbalance of forces (coriolis, pressure gradient and friction) from the daytime profile
develops from the lack of surface friction. This causes the winds, several hundred meters
above the surface to develop an over-speeding or jet-like profile (Blackadar 1957). The
height and intensity at which the jet forms are related to the strength of the boundary
layer top wind speed (i.e. geostrophic speed) from the previous afternoon (Davies 2000).
This LLJ profile may be located within or above the turbine layer depending on the
geostrophic speed and the characteristics of the evolution of the jet during the night. A
15
decaying LLJ aloft of the turbines layer can create a rapid transfer of energy to the
surface. A jet that has a balance of strong speed shear and equally modest thermal
stratification allows for the jet to persist or intensify for a substantial portion of the night
(e.g. Sun et al. 2012). Bonner et al. (1968) and Stensrud (1996) have classified classic
LLJs according to several properties and these studies note that jets occur in any time of
season and may translate to the surface more than once during the night.
The jet intensity near the rotor layer is characterized by the shear exponent ()
from the power law profile of the wind speed. A larger shear value portrays a sharp
change in wind speeds between the top and bottom blade-pass height.
Irwin (1979) and
Zoumakis (1984) determined that increasing the surface roughness (z0) will intensify the
shear profile in the turbine layer. Storm and Basu (2010), Storm et al. (2009), Walter et
al. (2009) depict shear exponents within a turbine layer often higher than the wind
industry standard threshold of ≤0.2. Nighttime periods may cause adverse shear often
associated with Kelvin-Helmholtz instability generated when the turbine-layer
stratification (Gradient Richardson number Ri or Monin Obukhov stability parameter z/L
) is near neutral (|Ri| <0.02 or |z/L| <0.05) and the frictional velocity (u*) between the top
and bottom blade pass is at or above 1.5 m s-1 (Kelley et al. 2004).
Numerical
simulations by Sathe and Bierbooms (2007) and Sim et al. (2009) also depict a high
turbine blade loading response to strong stably stratified flow conditions.
Additional
simulations suggest that the presence of a LLJ within a wind farm may or may not
change conditions of high turbulence associated with Kelvin-Helmholtz wave breaking
near the turbine rotor layer. This intermittency of turbulence bursting events occur for
strongly stable conditions when Ri is near 0.3 (Zhou and Chow 2011b).
Barthelmie (1999) and Emeis (2010) confirm the role of higher surface
roughness in decreasing both the daytime and nighttime wind resource for steady state
conditions of unstable, neutral or stable stratification. None of these studies offers any
prediction for a wind profile evolution from a typical diurnal cycle. Wharton and
Lundquist (2012) demonstrate from tower measurements, turbine nacelle anemometry,
and remote sensing techniques (SODAR and LiDAR) in wind farm in the Northwest
16
U.S. that the nighttime period offers the highest power production period but at the
expense of significant loading conditions on turbine components. There is a tradeoff of
slightly higher wind speed (5% increase in power generation) to an increased loading on
blades when turbines are constructed at higher levels into the PBL to harness fast wind
speed. Walter et al. (2009) compare Weibull distributions from one location in Texas
and in Indiana, input the data into a loading model, and determine that power losses are
minimal within high shear conditions when hub height speeds are above the rated speed.
Conversely, when the hub speed is below the rated speed, the strong shear conditions
indicate production loses of around 1-3% because of the blade bending/twisting forces.
Storm and Basu (2010) comment that there is a large uncertainty in nighttime
wind power and wind shear resource characterization using fully 3-D operational
configurations of WRF (Weather Research and Forecasting model). Wind profiles are
simulated differently among several Planetary Boundary Layer (PBL) modeling schemes
and interdependent scheme behavior is a source of large variation in forecasted wind
speed and wind direction.
Among PBL schemes closures using non-local (local)
turbulence parameterizations depict daytime (nighttime) directional shear profiles that
are closer to existing observations from the Great Plains.
Nighttime speed shear,
however, is overestimated (underestimated) by local (non-local) turbulence closure
schemes. Local closure schemes (e.g. YSU, MYJ, QNSE, and MYNN) depict slightly
higher nighttime winds speeds than schemes using non-local closure (e.g. YSU and
ACM2) but both depict a negative bias to observed profiles in flat terrain in coastal and
non-coastal areas (Draxl et al. 2010,2012; Hahmann et al. 2011; Shin and Hong 2011).
Daytime speeds are usually overestimated by PBL schemes and this may imply more
sensitive scaling of surface heterogeneities to the wind profile. These schemes indicate
less agreement to profile data when there is weak forcing after a cold front passage
(Cheng et al. 2013). The local closure QNSE has higher validation for areas of complex
terrain when drainage flows are possible (Wang et al. 2011).
With verification from
met-tower data in a wind farm in North-west Iowa and wind profile measurements from
the Atmospheric Radiation Measurement (ARM) program (Deppe et al 2013; Gallus
17
and Deppe 2011) developed some bias-corrected improvements to wind speed forecasts
using a 12 km parent 4 km child domain in WRF to evaluate an ensemble of PBL
schemes. Increasing the vertical resolution of the lowest grid points below and up to the
blade top height led to 2% improvement in the forecast and a better representation of
LLJ profiles. Hub-height wind speed biases are smaller with the non-local turbulence
closure schemes than with the other local closure schemes. Traituer et al. (2012) also
used a 3-km domain over the Central Plains to improve short term forecasting of wind
speeds. A PBL suite ensemble of single column models were added to develop training
periods in determining bias corrections to the wind based on surface stability, wind
speed and other metrics.
The single column model approach in WRF, developed by Hacker et al. (2007),
offers a simple method to investigate boundary layer parameterization sensitivities to
surface variations in roughness, vegetative fraction, soil moisture and temperature.
Several LES studies demonstrate that surface heterogeneities of crop roughness,
conductance, soil type, and soil moisture alter boundary layer clouds and induce/modify
localized circulations (Alapaty et al. 1997; Couralt et al. 2007; Zhong and Duran 1997).
Hacker (2010) also demonstrated that convection and the wind resource are both
sensitive to soil moisture perturbations. Soil moisture at or near the permanent wilting
point is the most critical aspect of changing convection in light wind speed scenarios
with weak synoptic forcing. However, 10-m wind speed and turbulence kinetic energy
is reduced after such a convective event when the soil is wet. A similar effect was tested
for roughness modifying sensible and latent heat flux but no information about the effect
on the boundary layer profile is offered.
High-resolution simulations of 80-m wind speed over an existing wind farm
demonstrate similarity to the elevation contours on a terrain map (C.S. Anderson,
personal communication, 2012).
Accuracy in wind forecasts can occur with some
increase in model resolution. However, with the approach to simulate the atmospheric
motions at finer scales (few km to hundreds of m) it is more likely that the
heterogeneities of crop surfaces will be of greater influence to the wind resource.
A
18
better physical understanding of crop canopy flows and boundary layer wind profile will
facilitate the wind industry in both siting and construction of new wind farms and
optimization techniques of existing wind farms.
Field measurements are needed to
calibrate any modeling techniques implored in wind forecast assessment and wind
resource characterization.
The CWEX study described in the next chapter begins this
interpretation of crop canopy, wind turbine/wind farm, and boundary layer and
mesoscale interactions from several measurement platforms at the surface and within the
PBL.
References
Adams M, Keith D (2007) A wind farm parameterization for WRF. 8th WRF Users
Workshop, June 2007, Boulder, CO. (Available online at
http://www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/5-5_Adams.pdf)
Ainslie JF (1988) Calculating the flowfield in the wake of wind turbines. J. Wind Eng.
and Indust. Aerodyn. 27:213–224.
Alapaty K, Raman S, Niyogi DS (1997) Uncertainty in the specification of surface
characteristics: A study of prediction errors in the boundary layer. Bound.-Layer
Meteorol. 82:475–502.
Baidya Roy, S, Pacala SW, and Walko RL (2004) Can large wind farms affect local
meteorology? J. Geophys. Res., 109, D19 101, doi: 10.1029/2004JD004763.
-----, Traiteur JJ (2010) Impacts of wind farms on surface air temperatures. Proc. Natl
Acad. of Sci. 107:17899–17904. doi: 10.1073/pnas.1000493107
-----,(2011) Simulating impacts of wind farms on local hydrometeorology. J. Wind Eng.
and Indust. Aerodyn. 99:491–498. doi: 10.1016/j.jweia.2010.12.013
Barthelmie RJ, Courtney MS, Højstrup J, Larsen SE (1996) Meteorological aspects of
offshore wind energy: Observations from the Vindeby wind farm. J. Wind Eng. and
Indust. Aerodyn. 62:191–211.
Barthelmie RJ (1999) The effects of atmospheric stability on coastal wind climates.
Meteorol. Appl. 6:39–47.
-----, Folkerts L, Ormel FT, et al. (2003) Offshore wind turbine wakes measured by
SODAR. J. Atmos. and Ocean. Tech. 20:466–477.
19
-----, Larsen G, Pryor S, et al. (2004) ENDOW (efficient development of offshore wind
farms): modelling wake and boundary layer interactions. Wind Energy 7:225–245.
-----, Frandsen ST, Nielsen MN, et al. (2007a) Modelling and measurements of power
losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore
wind farm. Wind Energy 10:517–528. doi: 10.1002/we.238.
-----, Rathmann O, Frandsen ST, et al. (2007b) Modelling and measurements of wakes in
large wind farms. Journal of Physics: Conference Series 75:012049. doi:
10.1088/1742-6596/75/1/012049.
-----, Hansen K, Frandsen ST, et al. (2009) Modelling and measuring flow and wind
turbine wakes in large wind farms offshore. Wind Energy 12:431–444. doi:
10.1002/we.348.
-----, Pryor SC, Frandsen ST, et al. (2010) Quantifying the Impact of Wind Turbine
Wakes on Power Output at Offshore Wind Farms. J. Atmos. Oceanic. Tech.
27:1302–1317. doi: 10.1175/2010JTECHA1398.1.
Blackadar, A K (1957) Boundary layer wind maximum and their significance for the
growth of nocturnal inversions. Bull. Amer. Meteor Soc., 38, 283–290.
Bonner, WD (1968) Climatology of the low level jet. Monthly Weath. Rev. 96: 833850.
Cabezón D, Sanz J, Martí I, Crespo A (2009) CFD modelling of the interaction between
the Surface Boundary Layer and rotor wake. Comparison of results obtained with
different turbulence. European Wind Energy Conference and Exhibition.
Cal RB, Lebrón J, Castillo L, et al. (2010) Experimental study of the horizontally
averaged flow structure in a model wind-turbine array boundary layer. J. of Renew.
and Sustain. Energy 2:013106. doi: 10.1063/1.3289735.
Calaf M, Parlange MB, Meneveau C (2011) Large eddy simulation study of scalar
transport in fully developed wind-turbine array boundary layers. Phys. of Fluids
23:126603. doi: 10.1063/1.3663376.
Campbell GS, Norman JM (1998) An Introduction to Environmental Biophysics. 2nd ed.
Springer –Verlag, 286 pp.
Cevarich M, Baidya Roy S (2013) Impact of wind farms on surface and air
temperatures. Fourth Conf. on Weath., Clim., and the New Energy Econ. Paper
7.1, Tuesday, 8 January, Austin, TX.
20
Chamorro LP, Porté-Agel F (2009) A Wind-Tunnel Investigation of Wind-Turbine
Wakes: Boundary-Layer Turbulence Effects. Bound.-Layer Meteorol. 132:129–149.
doi: 10.1007/s10546-009-9380-8.
-----,(2010a) Effects of Thermal Stability and Incoming Boundary-Layer Flow
Characteristics on Wind-Turbine Wakes: A Wind-Tunnel Study. Bound.-Layer
Meteorol. 136:515–533. doi: 10.1007/s10546-010-9512-1.
-----,(2010b) Surface Heterogeneity Effects on the Atmospheric Boundary Layer:
Parameterizations and Applications to Wind Energy. University of Minnesota, 2010.
Major: Civil Engineering.
-----, Arndt REA, Sotiropoulos F (2011) Turbulent Flow Properties Around a Staggered
Wind Farm. Bound.-Layer Meteorol. 141:349–367. doi: 10.1007/s10546-011-96496.
Cheng WY, Liu Y, Liu Y, et al. (2013) The impact of model physics on numerical wind
forecasts. Renewable Energy 55:347–356. doi: 10.1016/j.renene.2012.12.041.
Christiansen MB, Hasager CB (2005) Wake effects of large offshore wind farms
identified from satellite SAR. Remote Sensing of Environment 98:251–268. doi:
10.1016/j.rse.2005.07.009.
-----, (2006) Using airborne and satellite SAR for wake mapping offshore. Wind Energy
9:437–455. doi: 10.1002/we.196.
Churchfield, MJ et al. (2010) Wind Energy-Related Atmospheric Boundary Layer
Large-Eddy Simulation Using OpenFOAM: Preprint. In 19th Symposium on
Boundary Layers and Turbulence, Keystone, CO, Amer. Meteor. Soc., 1-26.
[Available online at
https://ams.confex.com/ams/19Ag19BLT9Urban/techprogram/paper_172636.htm.]
-----, S. Lee, J. Michalakes, and P. J. Moriarty (2012) A Numerical Study of the Effects
of Atmospheric and Wake Turbulence on Wind Turbine Dynamics. Submitted and
accepted to Journal of Turbulence.
Connell JR, George RL, (1982) The wake of the MOD-OA wind turbine two rotor
diameters downwind on 3rd December 1981. Report no. PNL-4210, Pacific
Northwest Laboratory, Battelle.
Courault D, Drobinski P, Brunet Y, et al. (2007) Impact of surface heterogeneity on a
buoyancy-driven convective boundary layer in light winds. Bound.-Layer Meteorol.
124:383–403. doi: 10.1007/s10546-007-9172-y.
21
Crespo A., Hernández, J., (1996) Turbulence characteristics in wind-turbine wakes. J.
Wind Eng. Indust. Aerodyn. 61:71–85.
Davies PA, (2000) Development and mechanisms of the nocturnal jet. Meteorol. Appl.
7:239–246.
Deppe AJ, Gallus WA, Takle ES (2013) A WRF Ensemble for Improved Wind Speed
Forecasts at Turbine Height. Weather and Forecasting 28:212–228. doi:
10.1175/WAF-D-11-00112.1.
Draxl C, Hahmann AN, Pena Diaz A, et al. (2010) Validation of boundary-layer winds
from WRF mesoscale forecasts with applications to wind energy forecasting.
-----, Giebel G (2012) Evaluating winds and vertical wind shear from Weather Research
and Forecasting model forecasts using seven planetary boundary layer schemes.
Wind Energy n/a–n/a. doi: 10.1002/we.1555.
Dvorak MJ, Zhou B, Wiersema D, Chow FK (2012). Detection of nocturnal coherent
turbulence in the US Great Plains and effects on wind turbine fatigue. American
Geophysical Union Fall Meeting, San Francisco, California. Paper A24E. 4
December 2012.
Emeis S (2009) A simple analytical wind park model considering atmospheric stability.
Wind Energy 13:459–469. doi: 10.1002/we.367.
Fitch AC, Olson JB, Lundquist JK, et al. (2012) Local and Mesoscale Impacts of Wind
Farms as Parameterized in a Mesoscale NWP Model. Month. Weath. Rev.
140:3017–3038. doi: 10.1175/MWR-D-11-00352.1.
-----, Lundquist JK ,Olson JB (2013a) Mesoscale Influences of Wind Farms throughout a
diurnal cycle. Accepted for publication in Month. Weath. Rev.
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00185.1.
----- , Olson JB, Lundquist JK (2013b) Representation of wind farms in climate models.
Accepted for publication in J. Climate http://dx.doi.org/10.1175/JCLI-D-12-00376.
Fletcher TM, Brown RE (2010) Simulation of wind turbine wake interaction using the
vorticity transport model. Wind Energy 13:587–602. doi: 10.1002/we.379.
Frandsen F, Chacón, L Crespo, A, Eenvoldsen, P, Goméz-Elvira, R, Hernández, J,
Højstrup, J, Manuel, F, Thomsen, K, and Sorensen, P, (1996). Final report on
measurements on and modeling of offshore wind farms. Riso National Laboratory
document Riso-R-903-(EN), 100pp.
22
-----, Barthelmie R, Pryor S, et al. (2006) Analytical modelling of wind speed deficit in
large offshore wind farms. Wind Energy 9:39–53. doi: 10.1002/we.189.
Gallus Jr WA, Deppe AJ (2011) Wind ramp events at an Iowa wind farm: A climatology
and evaluation of WRF ensemble forecast skill. Conference presentation 91st Annual
meeting of the Amer. Meteorol. Soc., Jan 2011, Seattle, Washington.
Gómez-Elvira R, Crespo A, Migoya E, et al. (2005) Anisotropy of turbulence in wind
turbine wakes. J. Wind Eng. Indust. Aerodyn. 93:797–814. doi:
10.1016/j.jweia.2005.08.001.
Hacker JP (2010) Spatial and Temporal Scales of Boundary Layer Wind Predictability in
Response to Small-Amplitude Land Surface Uncertainty. J. Atmos. Sci. 67:217–233.
doi: 10.1175/2009JAS3162.1.
Hahmann AN, Draxl C, Peña A, Nielsen JR (2011) Simulating the Vertical Structure of
the Wind with the WRF Model. 12th Annual WRF Users’ Workshop.
Helmis CG, Papadopoulos KH, Asimakopoulos DN, et al. (1995) An experimental study
of the near-wake structure of a wind turbine operating over complex terrain. Solar
Energy 54:413–428.
Högström, U, Asimakopoulos DN, Kambezidis H, Helmis, CG, and Smedman, A (1988)
A field study of the wake behind a 2 MW wind turbine. Atmos. Environ., 22, 803–
820. http://dx.doi.org/10.1016/0004-6981(88)90020-0.
Horton R, Kluitenberg GJ, Bristow KL, (1994). Surface crop residue effects on the soil
surface energy balance (PW Unger, Ed.). In Managing Agricultural Residues
(pp.143-162). Boca Raton, Lewis Publishers.
-----, Bristow KL, Kluitenberg GJ, Sauer TJ (1996) Crop residue effects on surface
radiation and energy balance – Review. Theor. Appl. Climatol. 54:27-37.
Iowa State University Extension (2005) Resource conservation practices: Tillage
management and soil organic matter. Publication PM1901 April 2005. Accessed
Spring 2012. Available online
[http://www.extension.iastate.edu/Publications/PM1901I.pdf]
Irwin, JS (1979). A theoretical variation of the wind profile power-law exponent as a
function of surface roughness and stability. Atmos. Environ., 13:191-194.
Jacobs, EW, Kelley, ND, McKenna, HE, Birkenheuer NJ (1984). Wake characteristics
of the MOD-2 wind turbine at Medicine Bow, Wyoming. Presentation for Fourth
23
ASME Wind Energy Symposium, Dallas, Texas; 18-20 Feburary 1985. Solar
Energy Research Institute report SERI/TP-214-2567.
Jimenez A, Crespo A, Migoya E, Garcia J (2008) Large-eddy simulation of spectral
coherence in a wind turbine wake. Environ. Res. Letters 3:015004. doi:
10.1088/1748-9326/3/1/015004.
Keith DW, DeCarolis JF, Denkenberger DC, et al. (2004) The influence of large-scale
wind power on global climate. Proceedings of the national academy of sciences of
the United States of America 101:16115–16120.
Kelley ND, McKenna HE, Hemphill RR, et al. (1985) Acoustic noise associated with the
MOD-1 wind turbine: its source, impact, and control. US Department of Energy
Kelley ND (1989) An initial look at the dynamics of the microscale flow field within a
large wind farm in response to variations in the natural inflow. Wind Power ’89
Conference and Exposition publication, San Francisco, California, 24-27 September
1989. Solar Energy Research Institute publication SERI/TP-257-3591.
-----, Shirazi M, Jager D, Wilde S, Adams J, Buhl M, Sullivan P, Patton E. (2004) Lamar
low-level jet project interim report. National Renewable Energy Laboratory technical
report NREL/TP-500-34593.
-----, Jonkman BJ, Scott GN (2006) The Great Plains turbulence environment: its
origins, impact and simulation. National Renewable Energy Laboratory technical
report Conference paper NREL/CP-500-400176 for AWEA 2006 WindPower
Conference, Pittsburgh, Pennsylvania, 4-7 June 2006.
Kirk-Davidoff DB, Keith DW (2008) On the Climate Impact of Surface Roughness
Anomalies. J. Atmos. Sci. 65:2215–2234. doi: 10.1175/2007JAS2509.1.
Kambezidis HD, Asimakopoulos DN, Helmis CG (1990) Wake measurements behind a
horizontal-axis 50 kW wind turbine. Solar & wind technology 7:177–184.
Klink K (2007) Atmospheric Circulation Effects on Wind Speed Variability at Turbine
Height. Journal of Applied Meteorology and Climatology 46:445–456. doi:
10.1175/JAM2466.1
Lange B, Waldl H-P, Guerrero AG, et al. (2003) Modelling of offshore wind turbine
wakes with the wind farm program FLaP. Wind Energy 6:87–104.
Larsen GC (2007) Dynamic wake meandering modeling. Risø National Laboratory,
Roskilde
24
Lu H, Porté-Agel F (2011) Large-eddy simulation of a very large wind farm in a stable
atmospheric boundary layer. Physics of Fluids 23:065101. doi: 10.1063/1.3589857
Magnusson M., Smedman, AS (1994) Influence of atmospheric stability on wind turbine
wakes. Wind Eng., 18:139–151.
-----,(1999) Air flow behind wind turbines. J. Wind Eng. and Industr. Aerodyn., 80:169–
189.
Markfort CD, Zhang W, Porté-Agel F (2012) Turbulent flow and scalar transport
through and over aligned and staggered wind farms. Journal of Turbulence 13:N33.
doi: 10.1080/14685248.2012.709635
Migoya E, Crespo A, García J, et al. (2007) Comparative study of the behavior of windturbines in a wind farm. Energy 32:1871–1885. doi: 10.1016/j.energy.2007.03.012
Medici D, Alfredsson PH (2006) Measurements on a wind turbine wake: 3D effects and
bluff body vortex shedding. Wind Energy 9:219–236. doi: 10.1002/we.156
-----, (2008) Measurements behind model wind turbines: further evidence of wake
meandering. Wind Energy 11:211–217. doi: 10.1002/we.247
Meneveau C (2012) The top-down model of wind farm boundary layers and its
applications. Journal Turb. 13:N7. doi: 10.1080/14685248.2012.663092
Meyers J, Meneveau C (2012) Optimal turbine spacing in fully developed wind farm
boundary layers. Wind Energy 15:305–317. doi: 10.1002/we.469
Muljadi E, Butterfield CP, Buhl Jr ML (1996) Effects of turbulence on power generation
for variable-speed wind turbines. National Renewable Energy Lab., Golden, CO
(United States)
-----, Singh M, Gevorgian V (2012) Fixed-Speed and Variable-Slip Wind Turbines
Providing Spinning Reserves to the Grid.
Nemes A, Sherry M, Jacono123 DL, et al. (2012) Generation, evolution and breakdown
of helical vortex wakes.
Papadopoulos KH, Helmis CG, Soilemes AT, et al. (1995) Study of the turbulent
characteristics of the near-wake field of a medium-sized wind turbine operating in
high wind conditions. Solar energy 55:61–72.
25
Pichugina, Y L, and Banta, R. M, (2010) Stable boundary layer depth from highresolution measurements of the mean wind profile. J. Appl. Meteor. Climatol., 49,
20–35.
-----, RM Banta, and N.D. Klley, 2005: Application of high resolution doppler lidar data
for wind energy assessment. Conf. presentation. [Available online at:
http://ams.confex.com/ams/pdfpapers/86691.pdf].
Politis ES, Prospathopoulos J, Cabezon D, et al. (2012) Modeling wake effects in large
wind farms in complex terrain: the problem, the methods and the issues. Wind
Energy 15:161–182. doi: 10.1002/we.481
Pryor SC, Barthelmie RJ, Young DT, et al. (2009) Wind speed trends over the
contiguous United States. J. Geophys. Res. doi: 10.1029/2008JD011416
Rados, K; Larsen, G; Barthelmie, R; Schlez, W; Lange, B. Schepers, G.; Hegberg, T.;
Magnusson, M. Comparison of wake models with data for offshore windfarms. Wind
Eng. 2001, 25, 271–280.
Sanderse, B (2009) Aerodynamics of wind turbine wakes. Energy Research Center of
the Netherlands (ECN), ECN-E–09-016, Petten, The Netherlands, Tech. Rep
-----, Pijl SP, Koren B (2011) Review of computational fluid dynamics for wind turbine
wake aerodynamics. Wind Energy 14:799–819. doi: 10.1002/we.458
Sathe A, Bierbooms W (2007) Influence of different wind profiles due to varying
atmospheric stability on the fatigue life of wind turbines . J. Phys.: Conf. Ser., 75
012056
Shin HH, Hong S-Y (2011) Intercomparison of Planetary Boundary-Layer
Parametrizations in the WRF Model for a Single Day from CASES-99. BoundaryLayer Meteorology 139:261–281. doi: 10.1007/s10546-010-9583-z
Sim C, Basu S, Manuel L (2009) The Influence of Stable Boundary Layer Flows on
Wind Turbine Fatigue Loads. Proceedings of the 47th AIAA Aerospace Sciences
Meeting Including The New Horizons Forum and Aerospace Exposition. Orlando,
FL, USA
Stensrud DJ (1996) Importance of low-level jets to climate: A review. J. Clim., 9:16981711.
26
Storm B, Dudhia J, Basu S, et al. (2009) Evaluation of the Weather Research and
Forecasting model on forecasting low-level jets: implications for wind energy. Wind
Energy 12:81–90. doi: 10.1002/we.288
-----, Basu S (2010) The WRF Model Forecast-Derived Low-Level Wind Shear
Climatology over the United States Great Plains. Energies 3:258–276. doi:
10.3390/en3020258
Stull, R., (1988) An Introduction to Boundary Layer Meteorology. Kluwer Academic
Publishers, 666 pp.
Sun, J, Mahrt, L, Banta, RM, Pichigina, Y. L. (2012) Turbulence regimes and turbulence
intermittency in the stable boundary layer during CASES-99. J. Atmos. Sci., 69,
338–351. doi: http://dx.doi.org/10.1175/JAS-D-11-082.1
Tardiff, R and JP Hacker, 2006: Description of the WRF-1d planetary boundary layer
model. [Available online at
http://sisla06.samsi.info/compmod/climate/group4/josh3.pdf]
Taylor GJ (1990) Wake Measurements on the Nibe Wind-Turbines in Denmark. CEC
Contract EN3W.0039, UK National Power Report ESTB/L/0158/R90.
Traiteur JJ, Callicutt DJ, Smith M, Roy SB (2012) A Short-Term Ensemble Wind Speed
Forecasting System for Wind Power Applications. J. Appl. Meteorol. and Climat.
51:1763–1774. doi: 10.1175/JAMC-D-11-0122.1
Troldborg N, Larsen GC, Madsen HA, H.K.S.; Sørensen, JN, Mikkelsen, R (2011)
Numerical simulations of wake interaction between two wind turbines at various
inflow conditions. Wind Energy, 14, 859–876 doi: 10.1002/we.433.
U.S. DOE Office of Energy Efficiency and Renewable Energy (2008a): 20% Wind
Energy by 2030: Increasing Wind Energy's Contribution to U.S. Electricity Supply,
248 pp.; NREL Report No. TP-500-41869; DOE/GO-102008-2567. (Available
online at http://www.nrel.gov/docs/fy08osti/41869.pdf).
-----,(2008b) USDOE Workshop Report: Research Needs for Wind Resource
Characterization. June 2008. [Available online at
http://www.nrel.gov/ce/wrc_workshop/main.cfm].
Vermeer LJ, Sørensen JN, Crespo A (2003) Wind turbine wake aerodynamics. Prog.
Aero. Scien. 39:467–510. doi: 10.1016/S0376-0421(03)00078-2
27
Walsh-Thomas JM, Cervone G, Agouris P, Manca G (2012) Further evidence of impacts
of large-scale wind farms on land surface temperature. Renewable and Sustainable
Energy Reviews 16:6432–6437. doi: 10.1016/j.rser.2012.07.004
Walter K, Weiss CC, Swift AHP, et al. (2009) Speed and Direction Shear in the Stable
Nocturnal Boundary Layer. Journal of Solar Energy Eng. 131:011013. doi:
10.1115/1.3035818
Wang C, Prinn RG (2010) Potential climatic impacts and reliability of very large-scale
wind farms. Atmos. Chem. Phys. 10:2053–2061. doi: 10.5194/acp-10-2053-2010
-----, Jin S, Hu J, et al (2011) Comparing different boundary layer schemes of WRF by
simulation the low-level wind over complex terrain. Artificial Intelligence,
Management Science and Electronic Commerce (AIMSEC), 2011 2nd International
Conference on. pp 6183–6188
Whale J, Papadopoulos KH, Anderson CG, et al. (1996) A study of the near wake
structure of a wind turbine comparing measurements from laboratory and full-scale
experiments. Solar energy 56:621–633.
Wharton S, Lundquist JK (2012) Assessing atmospheric stability and its impacts on
rotor-disk wind characteristics at an onshore wind farm. Wind Energy 15:525–546.
doi: 10.1002/we.483
Yang Z, Sarkar P, Hu H (2011) Visualization of the tip vortices in a wind turbine wake.
Journal of Visualization 15:39–44. doi: 10.1007/s12650-011-0112-z.
Zhang W, Markfort CD, Porté-Agel F (2012a) Wind-Turbine Wakes in a Convective
Boundary Layer: A Wind-Tunnel Study. Bound.-Layer Meteorol. 146:161–179. doi:
10.1007/s10546-012-9751-4
-----, (2012b) Near-wake flow structure downwind of a wind turbine in a turbulent
boundary layer. Exp Fluids 52:1219–1235. doi:10.1007/s00348-011-1250-8.
-----,(2013) Experimental study of the impact of large-scale wind farms on land–
atmosphere exchanges. Environ. Res. Letters 8:015002. doi: 10.1088/17489326/8/1/015002.
Zhong S, Doran JC (1997) A study of the effects of spatially varying fluxes on cloud
formation and boundary layer properties using data from the Southern Great Plains
Cloud and Radiation Testbed. Journal of climate 10:327–341.
28
Zhou B, Chow FK (2011a) Large-Eddy Simulation of the Stable Boundary Layer with
Explicit Filtering and Reconstruction Turbulence Modeling. Journal of the
Atmospheric Sciences 68:2142–2155. doi: 10.1175/2011JAS3693.1
-----,(2011b) Turbulence Modeling for the Stable Atmospheric Boundary Layer and
Implications for Wind Energy. Flow, Turb. Combust. 88:255–277. doi:
10.1007/s10494-011-9359-7
Zhou L, Tian Y, Baidya Roy S, et al. (2012a) Impacts of wind farms on land surface
temperature. Nature Clim. Change. doi: 10.1038/nclimate1505.
-----, Tian Y, Baidya Roy S, et al. (2012b) Diurnal and seasonal variations of wind farm
impacts on land surface temperature over western Texas. Clim. Dyn. doi:
10.1007/s00382-012-1485-y.
Zoumakis NM, (1993) The dependence of the power-law exponent on surface roughness
and stability in a neutrally and stably stratified surface boundary layer. Atmósfera
6:79-83.
29
Figure 1. Overview schematic of several hypothetical influences of wind energy on
crop agriculture
Figure 2. Overview schematic of several hypothetical influences of agricultural
crop production on wind energy.
30
CHAPTER 2. CWEX: CROP/WIND-ENERGY EXPERIMENT:
OBSERVATIONS OF SURFACE-LAYER, BOUNDARY-LAYER AND
MESOSCALE INTERACTIONS WITH A WIND FARM
From an Article accepted for publication in the Bulletin of the American Meteorological
Society
Daniel A. Rajewski1*
Eugene S. Takle2, Julie K. Lundquist3,4, Steven Oncley5, John H. Prueger6, Thomas W.
Horst5, Michael E. Rhodes7, Richard Pfeiffer6, Jerry L. Hatfield6, Kristopher K. Spoth2,
Russell K. Doorenbos2
Abstract
Perturbations of mean and turbulent wind characteristics by large wind turbines modify
fluxes between the vegetated surface and the lower boundary layer. While simulations
have suggested that wind farms could significantly change surface fluxes of heat,
momentum, moisture, and CO2 over hundreds of square kilometers, little observational
evidence exists to test these predictions. Quantifying the influences of the “turbine
layer” is necessary to quantify how surface fluxes are modified and to better forecast
energy production by a wind farm. Changes in fluxes are particularly important in
regions of intensely managed agriculture where crop growth and yield are highly
dependent on subtle changes in moisture, heat, and CO2. Furthermore, speculations
abound about the possible mesoscale consequences of boundary-layer changes that are
produced by wind farms. To address the lack of observations to answer these questions,
1
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, 50011.
Department of Agronomy, Iowa State University, Ames, IA, 50011.
3
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, 80309.
4
National Renewable Energy Laboratory, Golden, CO, 80401.
5
National Center for Atmospheric Research, Boulder, CO 80303.
6
National Laboratory for Agriculture and the Environment, Ames, IA 50011.
7
Aerospace Engineering Sciences, University of Colorado, Boulder, CO, 80309.
*
Corresponding author address: Daniel A. Rajewski, Iowa State University, 3132 Agronomy Ames, IA
50011.E-mail: drajewsk@iastate.edu
2
31
we developed the Crop/Wind-energy EXperiment (CWEX) as a multi-agency, multiuniversity field program in central Iowa. Throughout the summer of 2010, surface
fluxes were documented within a wind farm test site, and a two-week deployment of a
vertically pointing LIDAR quantified wind profiles.
In 2011, we expanded
measurements at the site by deploying six flux stations and two wind-profiling LIDARs
to document turbine wakes. The results provide valuable insights into the exchanges
over a surface that has been modified by wind turbines and a basis for a more
comprehensive measurement program planned for the summer in 2014.
1.
Introduction
The United States Department of Energy (DOE) has outlined a scenario describing
how wind power can be a major contributor to meet future U.S. renewable energy needs
(DOE 2008). The “20% Wind Energy by 2030” report outlines steps for achieving 20%
of the nation’s electrical energy from wind by 2030, a tenfold increase from the current
level of 2% (AWEA 2011). Most of the richest land-based domestic resources of wind
power in the United States are located in the central United States (North and South
Dakota, Minnesota, Iowa, Illinois, Nebraska, Kansas, Oklahoma, Texas). Therefore, the
DOE 20% by 2030 scenario will likely create additional interest in expanding the
number of wind farms in this region. These states also produce most of the nation’s
wheat and corn for food, livestock feed, or bio-fuel. Iowa alone accounts for 19% of the
nation’s production of corn as well as 15% of soybean (USDA 2012). Much of this
production is on the same land now being considered for wind farms.
While the co-location of wind farms with intensively managed agricultural
production is possible, it leads to physical interactions between two otherwise separate
economic systems. Crop selection and management determines surface drag and fluxes
that influence hub-height wind speeds. By contrast, turbine-generated changes in mean
wind, pressure, and turbulence may influence fluxes of heat, moisture, and CO2 that are
of vital importance to biophysical crop processes. Because multi-megawatt turbines and
their access roads require less than half an acre of land, farmers often continue to graze
livestock and farm crops right up to turbines’ bases (UCSUSA 2011). However, because
32
the wakes of wind turbines are known to persist up to 15 rotor diameters D downwind of
a turbine (Meyers and Meneveau 2012), differences in microclimate may extend well
beyond the wind turbines’ small footprint on the landscape.
As a result, some
agronomists and producers have questioned whether or not the atmospheric impacts of
wind turbines may also influence the biological productivity of the surrounding crops
(E.S. Takle, 2009).
Therefore our goal for CWEX is to develop a basic understanding
of how this land-use co-location changes both the energy and crop production systems
that contribute to the nation’s food and energy security needs.
Originally, CWEX was launched to address the following four agronomic questions:
1.
Do turbines create measureable changes in microclimate over crops?
2.
If Q1 is true, are these changes large enough to produce measureable influences
on plant growth?
3.
If Q1 and Q2 are true, are these changes sufficient to have measureable impact
on yield?
4.
Do agricultural cropping and surface management practices have a measureable
impact on wind energy production?
For this study we will report on the first of these questions and the other three will be
topics of future CWEX experiments.
Two summer measurement campaigns were conducted to observe surface and
elevated meteorological conditions in a wind farm co-located with agricultural fields. In
the summer 2010 experiment, designated CWEX-10, the National Laboratory for
Agriculture and the Environment (NLAE) deployed four flux stations in corn fields
within a wind farm in central Iowa. The University of Colorado conducted upper-air
observations for a portion of the summer. The second summer measurement period,
CWEX-11, coincided with a 10-week Iowa State University summer program of the
National Science Foundation Research Experiences for Undergraduates (REU) in Wind
Energy Science, Engineering and Policy (WESEP). In support of the WESEP REU, the
Earth Observing Laboratory (EOL) of the National Center for Atmospheric Research
(NCAR) provided an educational deployment of instruments to the wind farm consisting
33
of four surface flux stations, and included operational support and data archives. Iowa
State University (ISU) provided two flux stations for CWEX-11. The National
Renewable Energy Laboratory and the University of Colorado provided two windprofiling LIDARs to observe wind and turbulence profiles during CWEX-11. Numerous
discussions with representatives from the agricultural, wind energy, and boundary-layer
meteorology communities about the summer field measurement campaigns have
affirmed that the extension of CWEX to a more comprehensive field program offers a
unique opportunity to create a deeper understanding of the range of basic and applied
science issues.
Section 2 describes the CWEX site, highlighting its use for current and future field
campaigns to address these critical questions.
The experimental design and
instrumentation are described in Section 3. An analysis of surface flux differences is
presented in Section 4, and a case study of the differences in fluxes and in wind and
turbulence profiles is in Section 5. In Section 6 we demonstrate the potential influence
of turbines on daytime crop-canopy fluxes of heat and carbon dioxide. Lastly in Section
7, we present an expanded list of science questions and prospects for future campaigns
and solicit engagement from the academic, national laboratory, and private sector
segments of the agronomic, wind-energy and boundary-layer/mesoscale meteorology
communities.
2.
Site Description
The CWEX experiments were conducted within a 200-turbine (1.5 MW rated power)
wind farm in central Iowa. The wind farm features GE 1.5 MW SLE ‘super-long
extended’ model turbines (rated wind speed of 14 m s-1) with hub-heights of 80 m and
rotor diameters of 74 m for the southernmost 100 turbines and GE 1.5 MW XLE ‘extralong extended’ model turbines (rated wind speed of 11.5 m s-1) with rotor diameters of
77 m and 82.5 m for the northern 100 turbines. Additional turbine specifications are
available from GE and in their 1.5 MW wind turbine brochure (2009).
The land
generally is flat with less than a 0.5-degree slope from southwest to northeast. Crops in
the wind farm were a patchwork of mostly corn and soybeans, with some wetland and
34
lower terrain at the southern edge of the wind farm.
Measurements were taken at the
southwest edge of the farm, as shown in Figure1, to explore crop-turbine-boundary-layer
interactions in the vicinity of the leading line of turbines, designated as the B-turbine
line, for the predominant wind direction, (S to SSE) in mid to late summer.
Climatological wind roses for the nearby Marshalltown airport document prevailing
winds for the months of January (Figure 2a) and July (Figure 2b). Additional wind roses
are
available
from
the
Iowa
Environmental
http://mesonet.agron.iastate.edu/sites/windrose.phtml?network=IA_ASOS.
Mesonet:
Within the
study area is a second line of turbines, designated as the A-turbine line, 1.7 km to the
north of the leading line, and a third line, designated as the C-turbine line, is located 1.8
km southeast of the turbine line of our CWEX-10/11 measurement site.
For both CWEX-10 and CWEX-11 measurements were collected above and within a
corn canopy. At the start of each experiment (late June), the crop height was about 1.5
m, and by the second to third week of July the canopy reached its maximum height near
2.8 m.
Roughness length varied from 0.05 m to approximately 0.4 m for neutral
stratification conditions, which closely follows the parameterization of 1/10th the canopy
height (Campbell and Norman 1998).
3.
CWEX measurement design
To address our initial question, CWEX-10 was designed to examine differences in
surface fluxes and mean variables at several locations in the vicinity of one line of
turbines. Several offshore studies suggest that turbine wake interaction with the surface
would be detected beyond 5-10 D downwind from the turbines (e.g. Barthelmie et al.
2010). Preliminary profile measurements of temperature and 2-m wind speed above a
soybean canopy were taken at the wind farm around the A and B line of turbines in
2009. One mast was held stationary at a distance of 3-4 D upwind of the line of turbines
and depending on the wind direction the other mast was moved every 20 minutes at
intervals of 1 D downstream of the turbine line. Differences in surface mean wind
speed, turbulence intensity, and thermal instability were observed at a few locations
35
within 2-3 D behind the turbines, but impacts were diminished in the 5-7 D range and
the results from these simple studies were the impetus for the larger experiments
conducted in CWEX-10/11.
For CWEX-10, four surface flux stations, designated NLAE 1-4 in Figure 1, were
provided by the National Laboratory for Agriculture and the Environment. The upwind
flux tower in CWEX-10 was placed about 4.5D south of the B-turbine line to measure
characteristics of the undisturbed flow of the prevailing southerly winds. A second flux
tower sampled a near-wake position about 2.5D north of the B-turbine line. The third
flux tower was located 17D from the B-turbine line for observations at a ‘far-wake’
location.
A fourth flux tower was placed north of the A-turbine line about 35D
downstream of the B-turbine line to capture the influence of wakes from two lines of
turbines.
The significant variability of turbine wakes observed in CWEX-10
demonstrated the need for detailed measurements of surface flux differences at closer
distances from the leading line of turbines. Therefore, in CWEX-11, more flux towers
were deployed closer to the B-turbine line. The upwind reference tower (NCAR 1) was
placed 2.0D south of turbine B2. The northerly (downwind) flux towers (NCAR 2,
NCAR 3, and NCAR 4) were placed at 3.5D, 9D, and 14D, respectively, north of turbine
B2. Two additional flux towers, designated as ISU 1 and ISU 2, were placed north and
south of the midpoint between turbines B2 and B3, at approximately 2.0D upwind and
3.5D downwind.
From data collected by the Windcube LIDAR (version 1, manufactured by
Leosphere and NRG Systems, Inc.) that was deployed for two weeks in CWEX-10, we
learned that sufficient particulate loading within the boundary layer in this location
enabled high quality wind and turbulence profiles to be collected as a complement to
surface-based measurements. The LIDAR could “see” to 120 m above the surface over
95% of the time (Aitken et al. 2012). As a result, two LIDARs, designated as WC 68
and WC 49, were deployed in CWEX-11 to observe wind and turbulence profiles at
approximately 2.0D south and 3.5D north of turbine B3.
36
Flux stations in CWEX-10 and CWEX-11 had similar instrumentation (e.g. sonic
and cup anemometers), but not all measurements were collected at identical heights or
with the same type of sensor. Table 1 provides lists of the key instrumentation used in
the two years of the study.
Data from the sonic anemometers, krypton hygrometers, and gas analyzers were
collected at 20 Hz, whereas other flux station sensors sampled every 1 Hz, and the wind
and turbulence profiles were collected every 0.5 Hz. CWEX-10 was conducted from 27
June to 7 September 2010.
We report herein only measurements taken when the
turbines were operational. In CWEX-11, flux measurements and wind profiles were
archived for the period 29 June to 16 August 2011.
One lesson learned from CWEX-10/11 is the inherent variability of the cropland
within the wind farm, even in the rather featureless terrain of CWEX, due to variations
in soil type, drainage quality, and land management practices (tillage, row spacing,
cultivar type, planting date, and chemical applications). These factors influence crop
growth and therefore fluxes of heat, moisture, CO2, and momentum within and above the
crop canopy. Direct comparison of CWEX-10 and CWEX-11 differences in the flux
data also are complicated by the contrasts in growing season weather. Conditions during
CWEX-10 were abnormally wet, whereas the summer of 2011 was much drier. No clear
change in crop roughness was observed from the two distinctly different growing
seasons. The following section provides the results of surface fluxes from CWEX-10, in
which similarities were observed to the data from CWEX-11.
4.
Detection of turbine-induced surface flux differences
We used the wind direction from the near-wake flux tower (NLAE 2 in CWEX-10
and NCAR 2 in CWEX-11) to distinguish between wake and non-wake periods (periods
when an individual wake from turbine B2 or B3 was most likely overhead of the flux
station). For hub height wind speeds below 15 m s-1, Barthelmie et al. (2010) observed
that as wakes advect downwind, they tend to expand by five degrees within the first 10D
downwind. The same procedure also was applied in CWEX-11for determining the
37
turbine B3 wake for southerly flow and westerly flow non-wake periods for the LIDAR
data. The wind directions that represent the influence of wake for NLAE 2, NCAR 2
and WC 49 are marked on the upwind wind roses for NLAE 1, NCAR 1, and WC 68,
respectively (Figure 3a-c). The plots demonstrate the importance of measuring wind
speed and direction at multiple elevations near the turbines, especially under thermallystratified nighttime conditions when the turbines are operating within or underneath a
low-level jet environment that includes significant speed and directional shear.
To investigate the flux differences attributable to the turbine B2 in 2010 we
considered the wind direction window 189°-221° to give a wake over the NLAE
stations, for which we had a total of 420 15-min observations. These were compared to
observations with westerly flow (248°-282°) that gave no wake over the NLAE stations,
for which we had 413 observations. We also present a SSE flow condition (151°-189°)
for which NLAE 2 was between the wakes of turbine B2 and B3. For this wind
direction window we had 574 observations. The differences in conditions between flux
towers north and south of the B-turbine line were compared for daytime and nighttime
conditions. We used the common scaling of thermal stability (z/L0) at the reference flux
tower, where z is the height of the sonic anemometer (6.5 m in CWEX-10 and 4.5 m in
CWEX-11). The Obukhov length at the reference flux tower, L0, is defined following
Stull (1988):
 v u*
L0 
k g wv
3


s
and k is von Karman’s constant (0.4), u* is the friction velocity,  v is the surface virtual


potential temperature, and wv s is the surface moist sensible heat flux defined over a
15-minute averaging period.
Differences between the reference station (NLAE 1) and the flux towers (NLAE 2,
NLAE 3, NLAE 4) north of the B turbine line demonstrate the influence of turbines at
6.5 m in the turbulence and sensible heat flux and at 9 m for the mean wind speed and
𝑢−𝑢0
air temperature (Figure 4). We calculate a normalized wind speed difference, (
𝑢0
)
38
𝑇𝐾𝐸−𝑇𝐾𝐸0
and TKE difference (
𝑇𝐾𝐸0
) with respect to the undisturbed upwind reference speed,
uo, and turbulence kinetic energy, TKEo, at the same height according to the analysis
methods for simulating shelterbelt wind break flow in Wang and Takle (1995). Tables 2
to 5 quantify the mean and spread of the normalized wind speed, TKE, air temperature,
and the sensible heat flux respectively, for each stability class and flux station north and
south of the B-line of turbines for flow from the west (non-wake), SW (B2 wake), and
the SSE (flow between the wakes of turbines B2 and B3). We classify each set of
differences into three categories of the reference stability: unstable (z/L0<-0.05), neutral
(-0.05≤z/L0≤0.05), and stable (z/L0>0.05). Notable values are marked with a double
asterisk in Tables 2-5.
The non-wake westerly flow in Figure 4a,d shows considerable scatter in the wind
speed and TKE for all stability conditions but the overall mean difference is near zero at
the NLAE 2 and NLAE 3 flux towers. For this (westerly) flow direction the data from
NLAE 4 should be considered inconclusive since they may in some cases be influenced
by the four turbines to the west of the A-line (shown in the wind farm layout in Figure
1).
For a narrow window of southwesterly flow the wake of turbine B2 is overhead our
line of flux stations. Wind speeds are reduced (by 10-40%) in neutral to slightly
unstable conditions at NLAE 2 and NLAE 4 but this effect is negligible at NLAE 3
(Figure 4b). The difference in wind speed between NLAE 2 and NLAE 1 reveals a
slow-down in the near-wake of the turbine, whereas at NLAE 3 there is a slight speed
recovery, presumably because higher speed air from above has begun to replenish the
near-turbine deficit. At NLAE 4 there is an aggregated influence from both the B-turbine
line and the A-turbine line. The surface-level wind speed reductions we report are in
agreement with daytime velocity deficits at tall tower masts for an isolated turbine or
groups of turbines in on-shore coastal studies (e.g. Högström et al 1988, Magnusson and
Smedman 1994). For stable-flow, the number of observations is low, but a relatively
high percentage of these observations show a speed-up at all flux towers north of the B
line of turbines. TKE measurements (Figure 4e) for the nighttime B2 wake condition
39
show substantial enhancement at all stations downwind of B2, but we note high
variability in the normalized TKE (Table 3). For the daytime flow, by contrast, the
characteristically large TKE at the reference station is enhanced only modestly (<20%)
by the turbine as measured at downwind stations.
For SSE winds the NLAE 2 flux station is between the wakes of turbines B2 and B3.
As shown in Figure 4c, the northern two flux towers detect higher nighttime overspeeding (e.g., speeds downwind of the turbine being larger than the upwind reference
speed) than at NLAE 2, which demonstrates the expanding influence of multiple wakes
beyond 10D from the B-line of turbines. Under stably-stratified nighttime conditions,
this localized jet is not rapidly dissipated by turbulent exchange, whereas more turbulent
neutral conditions suppress the tendency for wind speed enhancement. We revisit
nighttime over-speeding in Section 5. There are clear effects of enhanced TKE (4-5
times TKE0) at NLAE 4 from the combined influence of the A and B lines of turbines.
NLAE 3 has higher TKE (2.5 times TKE0) than the near wake location at NLAE 2,
which we attribute to the aforementioned expansion of multiple wakes several tens of D
downstream from the B line. Turbulence at NLAE 2 is slightly enhanced, likely due to
the over-speeding at this location. Although Figure 4f demonstrates substantial
differences in the normalized TKE for stable flow at all three stations downwind of the
B-turbine line we detect high variability among the individual cases. TKE is enhanced
at the northern flux stations when the upstream turbulence is very low.
We observed a slight cooling (< 0.75 °C) at 9 m during the daytime for the two
northernmost stations in the southwest B2 wake and south-southeast B2 and B3 gap
conditions (Figure 4h and 4i) but temperature contrasts between NLAE 2 and NLAE 1
are generally less than 0.5 °C as are all differences in the daytime westerly case (Fig 4g).
For nighttime periods the scatter of temperature differences is high for west wind
conditions, and lack of data prevents analysis of temperature impacts for the B2 wake
case. However, for south-southeast winds we anticipate wakes from the B2 and B3
turbines to spread out and reach the surface somewhere near NLAE 3. At the
northernmost flux station (NLAE 4), we see a compounding influence of both B and A
40
turbines to produce several individual periods with a significant warming of 1.0-1.5 °C.
Although the variability is high (Table 4), we observe nighttime warming at NLAE 4
similar to that reported at the downwind edge of the San Gorgoino wind farm and
statistically analyzed in comparison to airport data by Baidya Roy and Traiteur (2010).
In our report of sensible heat flux differences we caution that the fluxes are derived
from the sonic temperature without making a correction for the humidity in the air.
Moisture correction was not possible at NLAE 3 and NLAE 4 since these stations did
not measure H2O and CO2 and therefore could not record fluxes of these constituents.
The “uncorrected” sensible heat flux shows significant scatter of daytime differences for
all three directional categories (Figure 4 j-l). For stable conditions we would expect
turbine-generated turbulence to be enhancing downward heat flux if the turbine wake is
intersecting with the surface Overall, the data do not show a systematic and significant
influence of the turbines on the surface sensible heat flux, although in Figure 4l we
notice a few observations with slightly larger heating at NLAE 4 (up to 40 W m-2) for
south-south easterly flow. Future CWEX experiments will sample surface heat fluxes
deeper in the wind farm, where multiple wakes prevail, for comparison with those near
the windward lines of turbines reported herein where single wakes and gaps between
wakes are more prevalent.
The CWEX-11 results showed similar results for the southerly B2 wake observations
(wind directions from 165°-195°) and exhibited the same daytime speed reductions,
nighttime accelerations, and increases in TKE. These data also revealed decrease
(increase) in daytime (nighttime) temperature and a modest increase in downward heat
transport (25 W m-2) especially at the northernmost flux station (NCAR 4). However,
the nighttime heat flux at NCAR 4 (CWEX-11) was weaker than what was observed at
the NLAE 4 (CWEX-10), which we attribute to the influence of wakes from multiple
lines of turbines.
41
5.
Turbine wake influences on wind and turbulence profiles: a case study, night
of 16-17 July 2011
A case study is presented to show the coupling between wake aloft and surface
processes. The overnight period of 16-17 July 2011 featured southerly flow within the
wind farm during a convection-free and cloud-free period. The dew-point depression
was less than 2oC, but airport Automated Surface Observing System (ASOS) stations
near the wind farm recorded visibilities of two to three standard nautical miles or greater
(NCAR 2011). A synoptic-scale backing pattern was revealed in the flux station and
LIDAR observations. The undisturbed wind profile (Figure 5a) indicated winds steadily
increasing with height, with a maximum between 12 and 14 m s-1 at 220 m above the
surface, and this persisted throughout the night. The wake characteristics in Figure 5c
can be quantified by subtracting the downwind observations (Figure 5b) from the
upwind observations. The momentum deficit of the wake occurs in the layer of the
turbine rotor disk (40 m to 120 m), with some expansion in the vertical to 140 m. The
largest wake deficits of ~ 6 m s-1 occurred at 100 m (which is above the 80-m hub
height) and represent a speed decrease of 40%.
The lowest level of Windcube
observations, 40 m above the surface, suggests some slight acceleration below the wake,
but these wind speed differences were small, being less than 1 m s-1.
The standard deviations of velocities measured by the LIDAR are used to estimate
TKE using the following relationship:
TKE lidar 
1 2
u  v2  w2 

2
where u, v, and w, represent the standard deviations of the zonal, meridional, and

vertical wind components, respectively. Some reports have indicated disagreement
between LIDAR turbulence metrics and those from in situ instruments (Sathe et al.
2011); however, the purpose herein is comparison of two LIDAR measurements, not a
strict calculation of TKE at one location per se. Upwind, downwind, and difference
time-height cross-sections of LIDAR estimates of TKE (Figure 6) corroborate previous
studies (Högström et al. 1988, among others) showing TKE increases in the wake. We
observed that TKE enhancement was confined to the turbine rotor disk layer during the
42
night, with some lofting occurring after sunrise as convective eddies lifted from the
surface. In the mid-morning through early afternoon there is slight expansion of turbine
turbulence to about 20 m above the rotor layer. We expect a sharp decrease of
turbulence above the rotor layer during the night as the temperature stratification
prevents vertical mixing of these larger eddies and sustains the ambient “upwind”
turbulence above the turbines.
Wake effects were also revealed in the 15-min averages of the surface fluxes. As
found in CWEX-10, the region below the wake experiences significant over-speeding
(0.5 to 1.0 m s-1) not only at the near-wake location (NCAR 2), but also at the far-wake
tower (NCAR 4). Data from the ISU flux towers located between turbine wakes exhibit
less over-speeding than for the flux stations directly downwind of turbine B2 (Figure
7a). We present differences in the data from two ISU towers and the reference NCAR
tower, but caution that the differences in measurement height (8 m vs. 10 m) are
responsible for the higher speeds for the NCAR sites. Wake effects on TKE show a
similar pattern (Figure 7b): the NCAR flux stations directly north of turbine B2 exhibit
TKE enhancements of as much as 0.30 m2 s-2, whereas there is negligible difference in
turbulence between the two stations in the gap region (ISU 2 - ISU 1) when wind
directions are between 170°-180°. However, for the wind direction near 160°, the
turbulence at ISU 2 increases as the edge of the B3 wake has shifted over the flux tower,
and conversely, the turbulence is reduced at the NCAR stations north of turbine B2 as
the edge of the wake has moved to the left of the line of the NCAR flux stations.
We observe slightly larger difference in 10-m temperature (0.3 °C) between the gap
stations (Figure 7c), whereas the NCAR stations do not report any significant warming
downstream of turbine B2. However, for the 4.5 m sonic temperature (Figure not
shown) there is roughly a 0.5 °C difference between NCAR 4 and NCAR 1 with lower
contrasts (0.25-0.4 °C) between the upwind flux tower and the near-wake (NCAR 2) or
intermediate location (NCAR 3). The 4.5 m temperature difference in the gap region is
the smallest of any plotted (+/- 0.1 °C), being about 0.25 °C higher downwind only when
a wind direction from 160° from 0330 to 0500 LST positions the edge of the wake over
43
the ISU 2 flux station. Measurements of sensible heat flux in far-wake locations (NCAR
3-4) show in Figure 7d a larger downward heat flux by 15-20 W m-2 as compared to the
enhancement at the near-wake position (NCAR 2). For periods with flow slightly
oblique to the tower line (near 160°) the heat flux difference between NCAR 2 and
NCAR 1 is reduced, whereas the ISU 2-ISU 1 difference indicates more downward heat
transport within the B3 wake above the ISU station.
We conclude that, for this southerly wind case, the turbine wakes from B2 and B3
are confined to an approximately five degree expansion and do not impact the ‘gap’
stations (ISU 1 and ISU 2). Further, the over speeding and enhancement of TKE at
NCAR 4 are near the magnitudes observed at NCAR 2, but the effect is less noticeable at
NCAR 3. Perhaps this is an indication that the turbine wake reaches the surface beyond
10 D downstream of an individual turbine for this nighttime case.
Interpretation of observed winds and TKE near the turbine line calls for a more
refined conceptual model of the pressure field, which we adopt from our previous
modeling and measurements around agricultural shelterbelts (Wang et al. 2001). The
turbines present a barrier to the flow, which creates a stationary (assuming a constant
wind speed and wind direction) perturbation pressure field at the surface, with high
pressure upwind and low pressure downwind.
The largest increases in speed and
turbulence behind the turbines occur at NCAR 2, which is consistent with a perturbationpressure-driven speed-up immediately behind the turbine. The over speeding and the
reduction of TKE at the ISU 2 flux tower between turbines B2 and B3 suggests that the
differences in wind speed are also forced by the perturbation pressure fields around each
turbine.
Our results suggest a need for future exploration of the perturbation pressure
and flow effects around individual turbines and around multiple lines of wind turbines.
6.
Turbine influences on fluxes of heat and carbon dioxide
Exchanges of CO2, moisture, and heat between atmosphere and crops have important
agricultural, as well as microclimate and mesoscale-flow consequences.
Figure 8
provides a contrast between the 30-min average fluxes of sensible and latent heat and the
carbon dioxide for the daytime southwesterly flow case of 18 July 2011 and the daytime
44
frontal case of 2 Aug 2011. The Webb-Pearman Leuning correction (Webb et al. 1980)
was applied to the latent heat and CO2 fluxes. Skies were generally clear in both cases
except for a period of cloudiness from 1250-1345 LST on Aug 2 (delineated by the
vertical dashed lines in (b, d, and f)). The sensible heat flux difference between NCAR 3
and NCAR 1 is slightly larger on Aug 2 compared to Jul 18 but neither showed large
change over the course of the day (Figs. 8a,b). Downwind-upwind latent heat flux
differences for the two days (c) and (d) are similar in the morning hours. After the
cloudiness period on Aug 2 the NCAR 3 – NCAR 1 difference in the latent heat flux
suggests a sign reversal, which is in contrast to a positive mean value for the afternoon
of Jul 18. The vertical flux differences of carbon dioxide (e) and (f) are similar in the
morning with higher downward flux downwind of the turbines. In the afternoon (after
the period of cloudiness on Aug 2) the fluxes are essentially identical on Aug 2, whereas
for the July 18 the morning pattern is preserved.
These data show that changes in
relative magnitude of the latent heat flux and CO2 flux take place at the same time as the
change in wind direction. These are consistent with (but perhaps not proof of) turbines
creating an increase in upward latent heat flux and downward CO2 flux over the crop
during the daytime.
7.
Remaining science questions and future campaigns
CWEX-10/11 provided evidence of changes in flow structures around single turbines
or single lines of turbines and evidence suggesting turbines modify fluxes of importance
to crops (e.g., heat and CO2). Our analysis of these data, together with our previous
experience from modeling and measurements of the aerodynamics of agricultural
shelterbelts (Wang et al. 2001) lead us to propose three mechanisms that influence
surface micrometeorological conditions in the near lee of turbines: (1) wind turbine
wakes overhead that have not reached the surface but modify the wind profile, scales of
turbulence, and the vertical mixing between the surface and the overlying boundary
layer, (2) wind turbine wakes that are intersecting the surface allowing wake turbulence
to modify the surface microclimate, and (3) static pressure fields (high pressure upwind
and low pressure downwind) around each turbine and line of turbines which generate
45
perturbations in surface flow (e.g., localized over speeding) and fluxes within a few D of
the turbine line.
Additional analyses of CWEX-10/11 data and future CWEX
experiments to map out the pressure fields will further explore these proposed
mechanisms.
The experiments thus far do not provide measurements of plant growth and yield
influences of turbines (addressing questions 2-4 in the Introduction). CWEX-10/11
demonstrated that turbines very likely have positive (e.g., enhanced daytime CO2 flux
down into the crop canopy) and negative (e.g., higher nighttime temperature which
enhances respiration) effects over short time periods. However, variability within and
between fields due to cultivar, soil texture and moisture content, and management
techniques create large uncertainties for attributing season-long biophysical changes,
much less yield, to turbines alone. A caveat to this statement is that we have not
sampled the center of the wind farm where aggregate effects of multiple rows of turbines
may be more pronounced. Enlarging the study domain would allow this and other
agronomic questions to be addressed. For instance staging an intensive observation
period during the corn pollination period (mid-July to early August) offers a unique
opportunity to study the transport and viability of pollen throughout the atmospheric
boundary layer. In addition to conducting biophysical studies of pollen, this experiment
could use pollen as a passive tracer for studying mesoscale influences of the wind farm
(see the discussion below).
There is additional motivation for studying the impact of the wind farm as a whole
as a basic science question, in addition to informing future siting and operation of wind
farms. For instance better understanding is needed on how the mean and turbulent flow
fields of the turbine layer interact with the overlying boundary layer and how this
changes from day to night when (at least in summer in the central US) a strong low-level
jet becomes established with peak winds within a few hundred meters of the surface.
Additional unknowns relate to mesoscale influences on the flow fields around and over
the wind farm, which has area of about 150 km2. What are the impacts on low-level
(z<100 m) convergence patterns around the wind farm and vertical velocities above or
46
downwind of the wind farm at 200 m, 500 m, and top of the boundary layer? Do they
correspond with the impacts suggested by wind farm parameterizations in mesoscale
models (Baidya Roy et al. 2004; Barrie and Kirk-Davidoff 2010; Baidya Roy 2011;
Fitch et al. 2012)? Are these changes in convergence patterns sufficient to change
patterns of boundary-layer clouds (e.g. via gravity wave formation in wind farms
described by Smith 2009)?
Are the resulting magnitudes of changes sufficient to
reorganize convectively driven systems leading to precipitation (Fiedler and Bukovsky
2011) or to change non-convective forcing of precipitation [e.g., isentropic lift,
conditional symmetric instability (CSI), and mesoscale banding]? Effects of mesoscale
terrain, such as the Loess Hills feature along the Iowa side of the Missouri River, which
can generate a very shallow short-wave train close to the surface, could potentially
interact with wind farm dynamics. The activity of this shallow short-wave train may
lead to the fluctuation of surface winds across the wind farm under stable night-time
flow.
Finally, numerical modeling using Large Eddy Simulation (LES) and other highresolution models is needed to explore how a wind farm interacts with ambient
meteorological conditions to create local winds, transports, and stresses on wind turbine
components. A deeper understanding of these interactions is needed for improved
forecasts of wind power output by individual turbines within the wind farm and the
forces and stresses (possibly leading to blade and gearbox damage) likely to accrue from
spatial and temporal changes in turbulence patterns.
Databases of field measurements
from operating wind farms are needed to validate a variety of wind-tunnel and numerical
simulation models (Chamorro and Porté-Agel 2009; Calaf et al. 2010; Churchfield et al.
2010; Cal et al. 2011; Lu and Porté-Agel 2011; Porté-Agel et al. 2011; Churchfield et al.
2012).
Current plans call for erection of two 120-m towers in the vicinity of the wind farm
for additional vertical measurements in future CWEX experiments. A community call is
planned to invite participation of other measurement teams for an expanded field
program in the summer of 2014 that will address the many science and application
47
questions we have raised. NCAR data from CWEX-11 are available from the CWEX-11
data
archive
website
of
the
Earth
Observing
Laboratory
http://www.eol.ucar.edu/deployment/educational-deployments/CWEX11.
of
NCAR:
Other data
from CWEX-10 and CWEX-11 will be become available in the near future from the
Iowa
Environmental
Mesonet
(http://mesonet.agron.iastate.edu/index.phtml).
Researchers interested in joining future CWEX experiments should contact co-author E.
S. Takle.
Acknowledgments
This work was supported in part by the National Renewable Energy Laboratory under
Professor Lundquist’s Joint Appointment. NREL is a national laboratory of the U.S.
Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by
the Alliance for Sustainable Energy, LLC. Partial funding for CWEX-10 was provided
by the Ames Laboratory (DOE) and the Center for Global and Regional Environmental
Research at the University of Iowa. Surface flux stations for CWEX-11 were provided
by NCAR Earth Observing Laboratory under an instrumentation deployment, and
undergraduate student participation was supplemented by funding from an NSF REU
program under grant 1063048. Data analysis was supported in part by the National
Science Foundation under the State of Iowa EPSCoR Grant 1101284.
8. References
Aitken, M. L., M. E. Rhodes, and J. K. Lundquist, 2012. Performance of a windprofiling lidar in the region of wind turbine rotor disks. J. Atmos. and Oceanic
Technol., 29, 347-355. doi: 10.1175/JTECH-D-11-00033.1.
American Wind Energy Association, cited 2011: U.S. wind industry year-end 2010
market report. [Available online at
http://www.awea.org/leanabout/publications/upload/4Q10_market_outlook_public.p
df.]
Baidya Roy, S., 2011: Simulating impacts of wind farms on local hydrometeorology. J.
Wind Eng. Ind. Aerodyn., 99, 491–498, doi: 10.1016/j.jweia.2010.12.013.
48
Baidya Roy, S., and J. Traiteur, 2010: Impact of wind farms on surface temperatures.
Proc. Natl. Acad. Sci., 107, 17899-17904, doi: 10.1073/pnas.1000493107.
Baidya Roy, S., S. W. Pacala, and R. L. Walko, 2004: Can large wind farms affect local
meteorology? J. Geophys. Res., 109, D19 101, doi: 10.1029/2004JD004763.
Barthelmie, R. J., and Coauthors, 2010: Quantifying the Impact of Wind Turbine Wakes
on Power Output at Offshore Wind Farms. J. Atmos. Oceanic Technol., 27, 1302–
1317.
doi: 10.1175/2010JTECHA1398.1.
Barrie, D. B. and D. B. Kirk-Davidoff, 2010: Weather response to a large wind turbine
array. Atmos. Chem. Phys., 10, 769–775, doi: 10.5194/acp-10-769-2010.
Cal, R. B., J. Lebron, L. Castillo, H. S. Kang, and C. Meneveau, 2011: Experimental
study of the horizontally averaged flow structure in a model wind-turbine array
boundary layer. J. Renewable Sustainable Energy, 2, 013 106, doi:
10.1063/1.3289735.
Calaf, M., C. Meneveau, and J. Meyers, 2010: Large eddy simulation study of fully
developed wind-turbine array boundary layers. Phys. Fluids, 22, 015 110, doi:
10.1063/1.3291077.
Campbell, G.S., and J.M. Norman, 1998: An Introduction to Environmental Biophysics.
2nd ed. Springer –Verlag, 286 pp.
Chamorro, L. and F. Porté-Agel, 2009: A wind-tunnel investigation of wind-turbine
wakes: Boundary-layer turbulence effects. Bound.-Layer Meteor., 132, 129–149,
doi:10.1007/s10546-009-9380-8.
Churchfield, M.J. et al., 2010. Wind Energy-Related Atmospheric Boundary Layer
Large-Eddy Simulation Using OpenFOAM: Preprint. In 19th Symposium on
Boundary Layers and Turbulence, Keystone, CO, Amer. Meteor. Soc., 1-26.
[Available online at
https://ams.confex.com/ams/19Ag19BLT9Urban/techprogram/paper_172636.htm.]
Churchfield, M. J., S. Lee, J. Michalakes, and P. J. Moriarty, 2012: A Numerical Study
of the Effects of Atmospheric and Wake Turbulence on Wind Turbine Dynamics.
Submitted and accepted to Journal of Turbulence.
Fiedler, B. H. and M. S. Bukovsky, 2011: The effect of a giant wind farm on
precipitation in a regional climate model. Environ. Res. Lett. 6:045101. doi:
10.1088/1748-9326/6/4/045101.
49
Fitch, Anna C., Joseph B. Olson, Julie K. Lundquist, Jimy Dudhia, Alok K. Gupta, John
Michalakes, Idar Barstad, 2012: Local and Mesoscale Impacts of Wind Farms as
Parameterized in a Mesoscale NWP Model. Mon. Wea. Rev., 140, 3017–3038.
doi: 10.1175/MWR-D-11-00352.1
General Electric Energy, cited 2009: 1.5 MW Wind Turbine. [Available online at
www.ge-energy.com/wind.]
Högström, U., D. N. Asimakopoulos, H. Kambezidis, C.G. Helmis, and A.
Smedman,1988: A field study of the wake behind a 2 MW wind turbine. Atmos.
Environ., 22, 803–820. doi: 10.1016/0004-6981(88)90020-0.
Lu, H. and F. Porté-Agel, 2011: Large-eddy simulation of a very large wind farm in a
stable atmospheric boundary layer. Phys. Fluids, 23, 065 101,
doi:10.1063/1.3589857.
Magnusson, M., and A. S. Smedman, 1994: Influence of atmospheric stability on wind
turbine wakes. Wind Eng., 18, 139–151.
Meyers, J., and C. Meneveau, 2012: Optimal turbine spacing in fully developed windfarm boundary layers, Wind Energ., 15, 305-317. doi:10.1002/we.469.
National Center for Atmospheric Research, cited 2011: National Center for
Atmospheric Research-Mesocscale and Microscale Division Image Archive
Meteorological case study selection kit. [Available online at
http://locust.mmm.ucar.edu.]
Porté-Agel, F., Y.-T. Wu, H. Lu, and R. J. Conzemius, 2011: Large-eddy simulation of
atmospheric boundary layer flow through wind turbines and wind farms. J. Wind
Eng. Ind. Aerodyn., 99, 154–168, doi: 10.1016/j.jweia.2011.01.011.
Sathe, A., J. Mann, J. Gottschall, and M. S. Courtney, 2011: Can wind lidars measure
turbulence? J. Atmos. Oceanic Technol., 28, 853–868. doi: 10.1175/JTECH-D-1005004.1.
Smith, R.B., 2009: Gravity wave effects on wind farm efficiency. Wind. Energ., 13,
449-458. doi: 10.1002/we.366.
Stull, R., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic
Publishers, 666 pp.
Union of Concerned Scientists, cited 2011: Farming the wind: wind power and
agriculture. [Available online at
50
http://www.ucsusa.org/clean_energy/technology_and_impacts/impacts/farming-thewind-wind-power.html.]
U.S. Department of Agriculture, National Agricultural Statistics Service, cited 2012:
Crop production 2011 summary January 2012, 95 pp; USDA Report No. 1057 -7823.
[Available online at http://www.usda.gov/nass/PUBS/TODAYRPT/cropan12.pdf.]
U.S. DOE Office of Energy Efficiency and Renewable Energy, 2008: 20% Wind Energy
by 2030: Increasing Wind Energy's Contribution to U.S. Electricity Supply, 248 pp.;
NREL Report No. TP-500-41869; DOE/GO-102008-2567. [Available online at
http://www.nrel.gov/docs/fy08osti/41869.pdf.]
Wang, H., and E. S. Takle, 1995: A numerical simulation of boundary-layer flows near
shelterbelts. Bound.-Layer. Meteor., 75, 141-173.
Wang, H., E. S. Takle, and J. Shen, 2001: Shelterbelts and windbreaks: Mathematical
modeling and computer simulation of turbulent flows. Ann. Rev. Fluid Mech., 33,
549-586.
Webb, E.K., G.I. Pearman, and R. Leuning, 1980. Correction of flux measurements for
density effects due to heat and water vapor transfer. Quart. J. Roy. Meteorol. Soc.,
106, 85-100.
51
Figure 1. Overlay of the wind farm boundaries with an expanded view of the
measurement locations for CWEX-10 and CWEX-11.
52
Figure 2. Climatological 10-m wind roses of the Marshalltown airport for the
months of (a) January and (b) July.
53
Figure 3. Wind roses for (a) CWEX-10 6.5-m winds at the reference flux tower
(NLAE 1), (b) CWEX-11 10-m winds at the reference flux tower (NCAR 1), and (c)
CWEX-11 80-m winds from the upwind wind cube (WC 68). Dashed lines denote
wind directions for turbine wakes on downwind stations.
54
Figure 4. CWEX-10 differences (downwind – upwind) of normalized wind speed
and normalized TKE, 9-m air temperature, and uncorrected sensible heat flux as
functions of upwind flux tower thermal stability (z/L0): for the westerly no-wake
case (a),(d),(g),and (j); for the SW B2 turbine wake case (b),(e),(h),and (k); and for
the SSE case between the wakes of turbine B2 and B3 (c),(f),(i),and (l).
55
Figure 4. (continued)
56
Figure 5. Contours of wind speed from (a) WC 68, (b) WC 49, and (c) calculated
difference in wind speed attributed to the wind turbine wake effect. Overlay with a
solid black line is for the top of the rotor height and the dashed black line indicates
the hub height.
57
Figure 6. Time-height cross sections of (a) upwind TKE profile, (b) downwind
TKE profile, and (c) difference between (a) and (b). Overlay with a solid line is for
the top of the rotor height and the dashed line indicates the hub height.
58
Figure 7. Differences during the night of 16-17 July 2011 for (a) wind speed (b)
TKE, (c) air temperature, and (d) sensible heat flux. Note that at the ISU tower
wind speed and temperature are collected at the 8-m level while the NCAR tower
wind speed and temperature are observed at 10 m.
59
Figure 8.
Comparison of differences in 30-min averaged fluxes of sensible heat
(a-b), latent heat (c-d), and CO2 (e-f) between NCAR 3 and NCAR 1 for a southerly
wind case on 18 July 2011 and for a transition from southerly to northwesterly
direction on 2 Aug 2011. NCAR 3 10-m wind direction vectors are overlaid for
each image. Dashed lines in (b), (d), and (f) denote the period of cloudiness during
the transition of winds from southerly to northwesterly on the early afternoon of 2
Aug 2011.
60
Table 1. Instrumentation type, sensor height, and location for flux stations operating
during CWEX-10 and CWEX-11. More detailed specifications of each sensor in the
following footnotes #1-8.
Sensor type
Height above Location for
Height above Location for
ground (m)
ground (m)
CWEX-10
CWEX -11
CWEX-10
Sonic anemometer1,*
6.5
CWEX-11
NLAE 1-4
4.5
NCAR 1-4
ISU 1-2
Net radiometer2
6.5
NLAE 1-2
4.5
ISU 1-2
Gas analyzer3,**
6.5
NLAE 1-2
4.5
NCAR 1,3
ISU 1
Cup/Prop
9.0
NLAE 1-4
anemometer4
Temp-RH probe5
5.3, 9.0
NLAE 1-4
10
NCAR 1-4
8, 3
ISU 1-2
10, 2
NCAR 1-4
8, 3, 1
ISU 1-2
Tipping bucket6
5.2
NLAE 1-4
3.3
ISU 1-2
Air pressure7
6.5
NLAE 1-2
2
NCAR 1-4
4.5
ISU 1
2
NCAR 1
1.7
ISU 1-2
Leaf wetness8,***,****
61
Table 1. (continued)
1
CSAT3, [Campbell Scientific Inc., Logan UT] - *possible 0.6° C warm bias at NLAE 1
2
CNR1 and CNR4 [Kipp and Zonen, Delft, The Netherlands] for ISU 1-2; Q7.1 REBS [REBS, Inc.,
Bellevue, WA] for NLAE 1-2
3
LI-7500, [Li-Cor Biosciences, Lincoln, NE] for NLAE 1-2, NCAR 1,3; EC-150 [Campbell Scientific,
Inc., Logan UT] for ISU 1 - **H2O flux measured with Krypton hygrometer at NCAR 2,4
4
03101 Wind Sentry [Campbell Scientific Inc., Logan UT] for NLAE 1-4 and ISU 1-2; 05103 Wind
Monitor [R.M. Young, Traverse City, MI] for NCAR 1-4
5
HMP40/45C [Campbell Scientific Inc., Logan UT] for NLAE 1-4 and ISU 1-2; HMP50 [Campbell
Scientific Inc., Logan UT] for ISU 1-2; NCAR SHT-75 thermo-hygrometer with aspiration systems
for NCAR 1-4
6
TE-525 [Texas Electronics, Inc., Dallas, TX]
7
LI-7500 for NLAE 1-2; EC-150 for ISU 1; PTB 220 [Vaisala, Helsinki, Finland] for NCAR 1-4
8
Leaf wetness sensor [Decagon Devices, Inc., Pullman, WA] - *** measured on the NCAR 1 tower,
****
measured in the canopy
62
Table 2. CWEX-10 means and standard deviations (in parentheses) of the differences (downwind – upwind) in normalized
wind speed for upwind flux tower thermal stability (z/L0) categories: unstable, neutral, and stable for the westerly no-wake
case; for the SW B2 turbine wake case; and for the SSE gap case between the wakes of turbine B2 and B3. Notable
differences are indicated with double asterisks.
U
U0
(NLAE 2-NLAE 1) (NLAE 3-NLAE 1) (NLAE 4-NLAE 1)
No
unstable (z/L0<-0.05)
0.00
(0.24)
0.07
(0.18)
0.06
(0.28)
wake
neutral (-0.05<z/L0<0.05)
0.03
(0.21)
0.06
(0.14)
0.02
(0.17)
0.02
(0.30)
0.10
(0.19)
0.16
(0.28)
(West) stable (z/L0>0.05)
unstable (z/L0<-0.05)
-0.10
(0.11)**
-0.01
(0.16)
-0.05
(0.32)
wake
neutral (-0.05<z/L0<0.05)
-0.12
(0.08) **
-0.03
(0.09)
-0.13
(0.12) **
(SW)
stable (z/L0>0.05)
0.21
(0.56)
0.24
(0.34)
0.37
(0.84)
B2_B3
unstable (z/L0<-0.05)
-0.01
(0.16)
0.01
(0.21)
-0.04
(0.28)
(SSE
neutral (-0.05<z/L0<0.05)
-0.07
(0.06) **
0.01
(0.09)
-0.10
(0.12) **
gap)
stable (z/L0>0.05)
0.16
(0.28)
0.31
(0.23) **
0.22
(0.25) **
62
B2
63
Table 3. CWEX-10 means and standard deviations (in parentheses) of differences (downwind – upwind) in normalized TKE
for upwind flux tower thermal stability (z/L0) categories: unstable, neutral, and stable for the westerly no-wake case; for the
SW B2 turbine wake case; and for the SSE gap case between the wakes of turbine B2 and B3. Notable differences are
indicated with double asterisks.
TKE
TKE0
(NLAE 2-NLAE 1) (NLAE 3-NLAE 1) (NLAE 4-NLAE 1)
unstable (z/L0<-0.05)
0.05
(0.40)
0.26
(0.65)
0.21
(0.75)
wake
neutral (-0.05<z/L0<0.05)
0.07
(0.23)
0.25
(0.34)
0.18
(0.42)
(West)
stable (z/L0>0.05)
0.30
(1.08)
0.39
(0.66)
1.22
(1.98)
B2
unstable (z/L0<-0.05)
0.12
(0.27)
-0.01
(0.79)
0.11
(0.51)
wake
neutral (-0.05<z/L0<0.05)
0.12
(0.20)
0.06
(0.21)
0.03
(0.27)
(SW)
stable (z/L0>0.05)
2.68
(4.07)
1.89
(1.66) **
2.42
(2.37) **
B2_B3
unstable (z/L0<-0.05)
0.13
(0.26)
-0.16
(1.03)
0.18
(0.62)
(SSE
neutral (-0.05<z/L0<0.05)
0.09
(0.15)
0.06
(0.21)
0.05
(0.25)
gap)
stable (z/L0>0.05)
1.07
(1.46)
1.34
(1.26) **
1.61
(1.69) **
63
No
64
Table 4. CWEX-10 means and standard deviations (in parentheses) of the differences (downwind – upwind) of 9-m air
temperature for upwind flux tower thermal stability (z/L0) categories: unstable, neutral, and stable for the westerly no-wake
case; for the SW B2 turbine wake case; and for the SSE gap case between the wakes of turbine B2 and B3. Notable
differences are indicated with double asterisks.
T (C )
(NLAE 2-NLAE 1) (NLAE 3-NLAE 1) (NLAE 4-NLAE 1)
unstable (z/L0<-0.05)
-0.11 (0.73)
-0.14 (0.24)
-0.20 (0.26)
wake
neutral (-0.05<z/L0<0.05)
0.00 (0.49)
-0.05 (0.14)
-0.41 (2.04)
(West)
stable (z/L0>0.05)
-0.02 (0.69)
0.01 (0.32)
-0.08 (0.42)
B2
unstable (z/L0<-0.05)
0.06 (0.14)
-0.19 (0.19)**
-0.11 (0.22)
wake
neutral (-0.05<z/L0<0.05)
-0.15 (1.29)
-0.13 (0.12)**
-0.08 (0.17)
(SW)
stable (z/L0>0.05)
-0.04 (1.42)
0.05 (0.24)
-0.02 (0.32)
B2_B3
unstable (z/L0<-0.05)
-0.01 (0.14)
-0.14 (0.21)
-0.06 (0.27)
(SSE
neutral (-0.05<z/L0<0.05)
0.04 (0.08)
-0.08 (0.12)
0.00 (0.19)
gap)
stable (z/L0>0.05)
0.10 (0.24)
0.32 (0.25)**
0.43 (0.43)**
64
No
65
Table 5. CWEX-10 means and standard deviations (in parentheses) of the differences (downwind – upwind) uncorrected
sensible heat flux for upwind flux tower thermal stability (z/L0) categories: unstable, neutral, and stable for the westerly nowake case; for the SW B2 turbine wake case; and for the SSE gap case between the wakes of turbine B2 and B3. Notable
differences are indicated with double asterisks.

H W m2

(NLAE 2-NLAE 1) (NLAE 3-NLAE 1) (NLAE 4-NLAE 1)
unstable (z/L0<-0.05)
0.13 (15.13)
-12.31 (43.68)
11.37 (25.90)
wake
neutral (-0.05<z/L0<0.05)
5.40 (11.94)
- 2.98 (18.94)
13.03 (23.64)
(West)
stable (z/L0>0.05)
- 0.34 ( 7.76)
- 0.17 ( 6.18)
- 5.93 ( 9.60)
B2
unstable (z/L0<-0.05)
0.23 (17.94)
- 0.89 (31.19)
14.74 (39.41)
wake
neutral (-0.05<z/L0<0.05) - 0.08 (12.34)
6.99 (19.39)
10.98 (24.16)
(SW)
stable (z/L0>0.05)
- 6.62 (17.04)
0.28 (23.58)
- 1.08 (14.96)
B2_B3
unstable (z/L0<-0.05)
- 9.07 (19.16)
-18.28 (36.25)
18.30 (37.58)
(SSE
neutral (-0.05<z/L0<0.05) - 3.02 (11.38)
- 0.71 (19.18)
10.55 (27.10)
gap)
stable (z/L0>0.05)
- 6.24 (14.04)
-11.31 (12.94)
65
No
- 6.06 ( 9.14)
66
CHAPTER 3. CHANGES IN FLUXES OF HEAT, H2O, AND CO2 CAUSED BY
A LARGE WIND FARM
From an Article to be submitted to the Journal of Agriculture and Forest Meteorology
Daniel A. Rajewski21*
Eugene S. Takle2, Julie K. Lundquist3,4, Steven Oncley5, John H. Prueger6, Thomas W.
Horst5, Michael E. Rhodes3, Richard Pfeiffer6, Jerry L. Hatfield6, Kristopher K. Spoth2,
Russell K. Doorenbos2
Abstract
The Crop-Wind energy Experiment (CWEX) provides a platform to investigate the
effect of individual turbines and large wind farms on surface fluxes of momentum, heat,
moisture, and CO2. In 2010 and 2011, eddy covariance flux stations were installed
between the outside two lines of turbines at the south west edge of a large Iowa wind
farm from late June to early September. We report changes in fluxes of sensible heat,
latent heat, and CO2 above a corn canopy after surface air has passed through a single
line of turbines. In 2010 our flux stations were placed within a field of homogeneous
land management (same tillage, cultivar, chemical treatments) and in 2011 our stations
were in corn fields subjected to different land management practices. We stratify the
data according to wind direction, diurnal condition, and whether the turbines were
operating. We analyze downwind-upwind flux differences within these categories to
explore the characteristics of turbine influences at the crop surface. Our results suggest
partial statistically significant evidence that daytime fluxes of CO2 and H2O are
enhanced in the lee of the turbines (from 3 to 5 turbine diameter distances downwind
1
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, 50011.
Department of Agronomy, Iowa State University, Ames, IA, 50011.
3
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, 80309.
4
National Renewable Energy Laboratory, Golden, CO, 80401.
5
National Center for Atmospheric Research, Boulder, CO 80303.
6
National Laboratory for Agriculture and the Environment, Ames, IA 50011.
*
Corresponding author address: Daniel A. Rajewski, Iowa State University, 3132 Agronomy Ames, IA
50011.
E-mail: drajewsk@iastate.edu
2
67
from the tower) for southerly to southwesterly directions as compared to a west wind.
However, we observe no flux difference for these same oblique flow directions based on
turbine operational status. In the nighttime there is strong statistical significance that
turbine wakes enhance upward CO2 fluxes and entrain sensible heat toward the crop as
compared to both westerly wind conditions and those with the turbines offline. The
direction of the scalar flux perturbation seems closely associated to the differences in
canopy friction velocity but more analysis is required to determine how fluxes are
modified between two turbine wakes as compared to a situation where the fluxes are
influenced within a turbine wake.
1. Introduction
Large wind farms have been constructed across the U.S. Midwest over the last
decade, and this expansion is likely to continue (U.S. DOE 2008; AWEA, 2012).
Turbine hub-height speed could be impacted by agricultural management (e.g. tillage,
crop-type, cultivar and plant density) as these roughness factors influence drag on the
wind and the heat and moisture and fluxes over the crop. In addition, the surface
roughness regulates the near-surface wind profile (Barthelmie 1999; Emeis 2010; Stull
1988) and influences nighttime speed and directional shear characteristics below and
within the turbine rotor layer (e.g. Pichugina et al. 2005; Storm and Basu 2010).
Parameterizations of wind turbines in weather prediction models suggest effects on nearsurface winds and temperature (Baidya Roy et al. 2011; Fitch et al., 2012; Fitch et al.,
2013a, 2013b). The construction of turbines in agricultural areas has raised the need to
quantify the impact of wind turbines and wind farms on agricultural crops.
The influence of wind turbines and wind farms can be illustrated by the following
conceptual diagram (Figure 1). Based on experience quantifying flow characteristics
around shelterbelts (Wang and Takle 1995; Wang et al. 2001), wind turbines modify
mean and turbulence characteristics of a uniform boundary layer above a crop canopy
surface. As a bluff obstacle to the wind, the rotor blades, nacelle, and tower create a
localized perturbed pressure field that affects mean flow from the surface to well above
68
the blade travel. Small-scale turbulence created by flow past the blades and nacelle
forms a wake region that can influence the surface indirectly (by decoupling ambient
flow aloft from near-surface flow and by creating waves and pressure fluctuations that
are mediated to the surface) and directly when the wake reaches the surface. Exchanges
of heat, H2O, and CO2 with crops are affected by these changes introduced in the lee of
the turbines. The magnitude and locations of these changes are controlled by the turbine
characteristics (hub height, rotor diameter, blade style, blade pitch angle and modelspecific thrust and power coefficients) and the ambient conditions (atmospheric stability,
wind direction, wind speed, and moisture conditions). The field measurements reported
herein quantify fluxes and offer opportunities for verifying numerical and conceptual
models of surface fluxes of sensible and latent heat, and gaseous constituents such as
CO2.
We report small but statistically significant increases in the daytime (nighttime) CO2
flux with a collocated enhancement of transpiration (sensible heating) at a distance of
about 5.5 turbine rotor diameters (D) downwind of a line of turbines. Several numerical
simulations of large wind farms demonstrate increased daytime evapo-transpiration up to
0.4 mm day-1 or about 10 W m-2 and higher nighttime temperatures (0.5° to 2 ºC) and
enhanced downward sensible heat flux of 5 to 10 W m-2 (Adams, 2007; Baidya Roy,
2004, 2011; Baidya Roy and Traiteur 2010; Cevarich and Baidya Roy 2013; Fitch et al.
2013a). Large-eddy simulation (LES) and 1-D surface layer models with wind turbine
parameterizations also indicate a 10-15 % increase in scalar fluxes in the surface layer
below the turbines (e.g. Calaf et al. 2011). Wind tunnel studies by Zhang et al. (2012a,
2013) of wind turbine arrays in neutral or convective boundary layers also depict areas
of increased heat flux (up to 24%) immediately around the downwind side of each tower
base. For a simulation of turbine wakes during a prescribed steady-state nocturnal
period, Lu and Porté-Agel (2011) determine that the downward sensible heat flux is
reduced 14-27% of the ambient flux within a deep-wind farm array but there is no
change in either the surface temperature, nor in the profile of potential temperature up to
the bottom tip of the turbine rotor.
69
A few observational studies, primarily using remote sensing techniques instead of insitu measurements, give evidence for the role of large wind farms in perturbing crop
microclimate conditions. Zhou et al. (2012a, 2012b) analyzed MODIS satellite data at
1-km resolution to extrapolate land surface temperatures over western Texas (several
thousand turbines are located in this area) and determined that the study area was 0.5° to
0.7 °C warmer after construction of the turbines (from 2009-2011) than before (from
2003-2005). The authors also documented slight increases (decreases) in surface albedo
(vegetative fraction) for each turbine construction ‘footprint’ but did not report fieldscale changes related to soils, agricultural cultivars etc. Another study (Walsh-Thomas
et al. 2012) determined similar or more extensive warming from 1982-2011 over the
small power turbines with 15 m hub height and 23 m blades in San Gorgonio wind farm
using 240-m resolution data from the LANDSAT 5 satellite and filtering for days with
less than 20% cloud cover. Temperature bins were created for each 2-km X 2-km area
upwind and downwind of the wind farm but the study area was dominated by terraininduced changes in temperature, which obscured the specific contribution of the wind
farm to changes in surface temperature.
Air temperature, relative humidity, and
evaporation differences were noted among five surface stations positioned in 4.5 km grid
spacing within a large 300-turbine wind farm in Western IN during the post harvest
period in 2011 (Henschen et al. 2011). However, there is no clear comparison of these
field-scale differences to any upwind or un-perturbed ambient weather station, and the
authors do not describe inherent field scale variability that may have been caused by
land management (e.g. tillage) or soil (e.g. temperature, moisture, texture, drainage).
Rajewski et al. (2013) propose four basic agronomic questions to address the windcrop interaction and they provide an overview analysis of differences in surface fluxes
and wind profile measurements collected within a large utility scale wind farm in central
Iowa during the 2010 and 2011 Crop Wind-Energy Experiment (CWEX-10/11). The
purpose of this study is to more specifically address from the CWEX measurements how
fluxes of heat, moisture, and CO2 are modified by the turbine/wind farm flow
perturbations. In Section 2, a brief summary of the relevant measurements is presented.
70
In Section 3 we discuss the analysis used to determine the flux differences for turbineinfluenced and no turbine-influenced conditions. Section 4 highlights the results of the
flux analysis using conditional binning for several factors (e.g. day/night, wind direction,
turbines on/off). We summarize the results in Section 5 and offer additional objectives
for future experiments to quantify the influence of wind turbines/large wind farms on
crops.
2. Materials and Methods
The CWEX overview (Rajewski et al. 2013) provides a description of the instrument
deployment and measurements collected in CWEX-10/11. The National Laboratory for
Agriculture and the Environment (NLAE) provided instruments and data collection in
2010 whereas in 2011 instrumentation for four flux towers was provided by the
Integrated Surface Flux Systems group of the Earth Observing Laboratory of the
National Center for Atmospheric Research (NCAR) Earth Observing Laboratory. Two
additional flux towers were assembled by Iowa State University (ISU). The University
of Colorado and the National Renewable Energy Laboratory (CU/NREL) provided a
wind profiling light detection and ranging (LiDAR) downwind of the turbines in 2010
and one upwind and one downwind of turbines in 2011 to assist in overhead detection of
turbine wakes.
Reference upwind (under prevailing southerly winds) flux stations were positioned
south of the first line of turbines (we denote as a ‘B’ line) [NLAE 1 in 2010 and NCAR
1 and ISU 1 in 2011] and several flux stations downwind of the ‘B’ turbine line in both
2010 and 2011. One upwind and one downwind stations provided measurements of H2O
and CO2 [NLAE 1,2 in 2010 and NCAR 1,3 in 2011].
In 2010 the stations were
installed for the period of 30 June to 7 September, whereas in 2011 measurements were
collected from 30 June to 16 August. The 2010 season provides a ten-day period from
26 July 700 LST to 5 August 1730 LST to measure crop fluxes within the wind farm
when the turbines were shut down for transmission grid upgrades. We calculate average
fluxes over 30-min intervals from the 20-Hz time series of the CSAT3 sonic
71
anemometers (Campbell Scientific, Logan UT) and LI-7500 gas analyzers (Li-Cor,
Lincoln, NE). Corrections are applied to the sonic anemometer tilt angle (Wilczak et al.
2001), temperature (Scotanus et al. 1983) and to the H2O and CO2 concentrations from
the gas analyzer according to Webb et al. (1980), commonly referred to as WPL. Data
taken during precipitation events and data from periods having missing values from both
the upwind or downwind stations were eliminated (about 19%). We also performed a
quality control of the temperature profile measurements at 9 and 5 meters where we
reject all events (an additional 2%) where TNLAE 2-TNLAE 1 > |5°C| or TNCAR 3-TNCAR 1 >
|5°C| ) since the temperature and relative humidity profile measurements were used in
air density content for the calculation of the corrected fluxes.
3. Theory/Calculation
3.1 Surface energy budget considerations
We define the surface energy balance equation as in Leuning et al. (2012, among
others):
R net − G = H + λE + Fc + Fb
(1)
where Rnet is the net radiation, G is the soil heat flux, H is the sensible heat flux, E is
latent heat of evapo-transpiration, Fc is the energy absorbed/released by CO2, and Fb is
the thermal storage within the crop canopy elements (e.g. leaves, stems, reproductive
matter). H, E, Fc and Rnet were measured, but we did not measure G and Fb. We
therefore parameterize G as a daytime or nighttime fraction (0.1 or 0.5,
respectively)*Rnet (e.g. Stull, 1988). We cannot determine Fb in the absence of biomass
measurements, so this term is neglected. Leuning (2012) defines Fc (the energy flux of
CO2) as Fc = fc*-0.489, where the constant is the CO2 absorption determined by Blanken
et al. (1997), and fc is the WPL-corrected vertical flux of CO2. The scatter plot of the
energy terms for (Rnet–G vs. H+E+Fc) in Figure 2 for our four primary sites
demonstrates about 90-95% energy closure in both 2010 and 2011, justifying our neglect
72
of Fb.
We attribute the satisfactory agreement in our energy budget from using the
most updated flux-correction procedures, especially accounting for greater flux capture
when using the sonic tilt corrections (Leuning et al. 2012; Wilczak et al. 2001).
We calculate the differences of the crop energy fluxes of H, E, and fc between the
first station north (downwind) and south (upwind) of the ‘B’ turbine line: [NLAE 2NLAE 1 for CWEX-10 and NCAR 3-NCAR 1 in CWEX-11]. We emphasize that, in
addition to the influence of wind turbines, the flux variability is related to several factors
in crop management (e.g. crop-type, cultivar, plant density, tillage, chemical application)
in 2011 and inherent soil variability (temperature, moisture, texture, etc.) for both 2010
and 2011.
We note from the USDA-NRCS soil maps of this field section (obtained
from the Iowa Geographic Map Server at http://ortho.gis.iastate.edu), up to six different
loamy soils speckled around the study area each with varying degrees of poor quality
drainage. Both the NLAE 1 and 2 stations and the NCAR 1 and 3 stations have
elevation differences of less than 3 m between sites. 2010 also features a very wet
growing season whereas in 2011 the modestly dry conditions complicate our ability to
sort out turbine impacts of crop fluxes among other environmental and crop
physiological factors related to plant stress. Therefore the bulk of our analyses will
focus on the CWEX-10 stations that were placed over the same crop field on either side
of the B turbine line. We analyze the flux differences of H, E, and fc for typical
daytime and nighttime conditions according to two ‘null’ categories (no-turbine
influence) and by specific wind directional composites (with a turbine influence) for
several tests of statistical significance of each difference. These filtering procedures for
the analysis are described in the following sub sections.
3.2 Wind directional filtering for the flux differences composites
Each set of daytime or nighttime fluxes is additionally separated according to the
downwind flux station wind direction at NLAE 2.
‘Waked’ or ‘non-waked’ wind
directions from the flux tower are determined by using an assumption of a 5° expansion
of wakes from the turbine rotor as reported by Barthelmie et al. (2010) and adopted in
73
the analysis of Rajewski et al. (2013). Figure 3 presents a graphical interpretation of
these wake-direction sectors for each wind turbine used in this analysis. In this study we
present wake indicators for the first null case: “W” for westerly winds and test cases #14 for the “WSW”, “SW”, “SSW”, and “S-SSE” directional bins. We omit analysis of
the remaining directional sectors (“SE”, “ESE”, “E”, “ENE”, to “NW-NE”) to avoid
compromising the “undisturbed flow” condition of our upwind station, NLAE 1, caused
by wakes originating from the two turbine lines 1.5 km southeast of the ‘B’ turbine line
or from the deep-wind farm effect in the northerly direction.
3. 3 Diurnal filtering of the flux-differences composites
The 30-min fluxes are additionally partitioned into daytime and nighttime periods
according
to
the
surface
stability
condition
classification
(NEUTRAL/STABLE/UNSTABLE) as presented for 15-min averages in Rajewski et al.
(2013). We classify a daytime period, “DAY”, to be when the reference upwind tower
has Rnet > 300 W m-2 and a nighttime period, “NIGHT” ,when Rnet < 0 Wm-2. Each of
these periods corresponds to daytime (nighttime) radiation conditions indicating minimal
cloudiness and between the morning/evening transitions of the surface layer when we
would expect rapid changes in the energy fluxes. We acknowledge that these boundary
layer-transition and cloudy periods may contribute to crop fluxes, but our goal was to
create ensembles of clearly daytime and nighttime periods when fluxes were not
changing rapidly due to diurnal cloudiness effects. We did observe nighttime periods
when the upwind station reported unstable conditions, likely associated with KelvinHelmholtz wave breaking (e.g Stull 1988), but these events were infrequent (< 1% of all
nighttime events) and sometimes did not occur at both the upwind and downwind
stations simultaneously.
composites.
Therefore these periods are retained in our nighttime
74
3.4 Turbine operation filter for the flux differences composites
Each directional category for the DAY or NIGHT case is additionally filtered by the
operating status of the turbines. SCADA data (nacelle wind speed, wind direction,
temperature, and instantaneous power) for each 10-min period are used to define an
ON/OFF turbine flag for the surface fluxes. We relate the 10-min SCADA to the 30-min
averaged fluxes: “OFF” applied when at least two out of the first three turbines in the B
line (B1-B3) were not producing power for two out of the three 10-min periods that
correspond to our 30-min flux averaging time stamp. Otherwise, “ON” applied. Turbines
B4-B8 are not part of the ON/OFF determination for this procedure because we are
specifically omitting directions when our stations could be influenced by those turbines.
In addition to the 10-day shutdown of the wind farm we noted up to 70-85% of the
observations indicate periods of minimal to no electrical generation during CWEX-10.
During a few days in our study the turbines were shut down for 12 or more hours and
sometimes were below operating wind conditions for up to 36 hours during a typical
quiescent period of northerly winds.
Some “OFF” events occurred with strong winds
but lightning was detected within 50 miles. For these cases the turbines were brought
online only after an extended period (30-60 min) of lightning-free conditions. Our data
set therefore consists of DAY and NIGHT periods when turbines were operating under
clear skies and ample wind without effects of thunderstorms or weak synoptic forcing.
We recognize the potential for flux perturbations caused by turbines that are either
still or not turning fast enough to produce power, because each turbine nacelle and tower
provide a small cross sectional influence on the boundary layer flow. Vortices around
the blades and towers, albeit small (e.g. Vermeer et al. 2003), may have influence on
flux measurements especially for northerly flow conditions.
To provide a clean
comparison to our null and test conditions, we evaluate only those OFF periods in a
composite for the same wind direction categories (WSW, SW, SSW, and S-SSE) as
those used when the turbines are online. Unfortunately, there are not enough
75
observations in the OFF samples to make a statistically meaningful comparison of the
flux differences for individual direction categories when the turbines were on to when
the turbines were offline.
3.5 Comparison metrics of the flux differences composites
We test the hypothesis that the downwind-upwind energy flux differences of each
quantity H, E, and fc (denoted “F”) for the directional sum ( FON) and the
individual directions (FWSW, FSW, FSSW, FS-SSE) are significant as compared to the
sum of the energy flux differences for the direction sum in the first null case”W”
(FW) and in the second null case “OFF” (FOFF). The westerly composite, FW
includes both ON and OFF conditions to increase the sample size since no turbine
influence is expected for westerly flow. During daytime conditions, ON conditions
occur twice as often as OFF conditions, whereas ON and OFF conditions occur equally
during nighttime conditions.
The OFF and ON composites (FOFF and FON,
respectively) include the same wind direction categories. Table 1 quantifies the number
of cases in each category and also relates to each directional bin the NLAE 2 flux tower
distance downwind of a turbine or the ‘B’ line.
3.6 Statistical analyses of the composite and individual flux differences
For each flux considered (H, E, and fc), we quantify the mean, standard deviation,
the 95% upper and lower bounds of the difference, and the skewness and kurtosis of the
difference distributions to provide context for the range of variability within each flux
difference composite. The mean differences and the standard deviations among each
case are graphically depicted to more clearly reveal turbine and non-turbine influenced
conditions that allow us to evaluate our conceptual model of turbine impacts on crop
fluxes. The plots of the mean and standard deviations of the difference also include flux
difference accumulations between each composite condition for the DAY and NIGHT
periods. The sensible and latent heat flux differences are accumulated for each 30-min
period, whereas for the CO2 flux we define the daytime gross primary production (GPP)
76
as the accumulated down welling CO2 area density when fc < 0 and similarly for the
nighttime ecosystem respiration (Re) as the upwelling CO2 flux when fc > 0 according to
Lovett et al. (2006). Finally, we present the statistical significance testing for comparing
the downwind-upwind flux differences for each of the test cases to each of the two null
cases. We determine significance testing using the t-test applied by Zhou et al. (2012a)
in the temperature analysis over the west Texas wind farm complex and the Wilcoxon
paired test as is similar to the method used by Baidya Roy and Traituer (2010) in the San
Gorgonio study. We verify our flux difference significance from these two statistical
tests because the t-test assumes a Gaussian distribution to the data and that the variances
of the samples are equal, whereas the Wilcoxon method allows for non-parametric
comparisons and is more suited for our data set.
We do not observe a normal
distribution to our data for obvious reasons linked to diurnal changes in the boundary
layer. These distribution patterns will be revisited in the analyses of the skewness and
kurtosis for each of the flux difference composites. Finally, the Welch test is a statistical
test that determines if two parameters have unequal variances. Our data confirm that for
most of the flux differences among the null and test cases we cannot assume equal
variance. Our turbine-influenced test cases are determined to be significant when there is
a 95% or greater probability that the null hypothesis (no difference between the upwind
and downwind means) can be rejected (p-value ≤ 0.05).
4. Results
4.1 Sensible Heat flux
We present the accumulations of the NLAE 2-1 station differences in daytime and
nighttime sensible heat flux for the null and test case conditions in Figure 4. Also
overlaid are the mean and standard deviations of each difference in the day or night
event. We observe mostly small accumulations of daytime positive heat flux (greater
surface cooling) at the north station for most of the individual southwest quadrant
directional composites than for when the turbines are off. The daytime mean differences
in the sensible heat are highly variable among all null and test cases and this is also
77
evident in Table 2a.
The skewness of the distribution of the differences is generally
symmetric around the mean and the distribution is platykurtic (less than normal
distribution). In the turbines OFF composite we observe a negative skew and generally a
normal distribution among the differences. In our statistical tests of these differences
(Table 5a) we indicate no evidence of the sensible heat flux difference between the ON
composite and the westerly null case, whereas our t-tests for both the ON composite and
the individual south-southwest and southerly to south-south east directions indicate weak
statistical significance as compared to the turbines off composite.
However the
magnitude of the composite ON differences is negligible compared to previously
reported studies. We expect a weaker influence of the turbines perturbing the sensible
heat flux because the daytime boundary layer, typically in a convective or near neutral
situation, would contain eddies of a much larger size (several hundred m deep)
dominating the vertical mixing above the canopy instead of the turbine-scale eddies,
which may at most be a few tens to one-hundred meters in diameter. Both our mean
differences and our statistical testing seem to support this conceptual picture.
At night, the turbine-scale turbulence (scales of tens to one-hundred meters) can
dominate ambient boundary layer turbulence having scales of only a few meters, so we
can easily detect the bulk turbine influence as in the ON composite promoting larger
downward transport of heat at NLAE 2. We observe a mean effect of 5-10 W m-2
greater downward heat, with isolated events in the southwest wind condition
approaching 30 W m-2 of enhanced flux at the northern station (Table 2b).
Our
statistical tests of these mean differences (Table 5b) also suggest strong correlation of
smaller ambient turbulence and scalars being enhanced by the turbine-scale turbulence.
The bulk of the turbine ON composites differences for the NIGHT condition have
negative skew but these follow closely to a normal distribution, in contrast to the DAY
events. The OFF composite distribution is centered on the mean difference but contains
several outliers at the tails of the mean. In all comparisons of the test composites vs. the
null composites of winds from the west and for turbines off we have strong confidence
that the flux difference between the two stations is statistically significant (p<0.005) for
78
both the parametric and non-parametric tests. For the nighttime south to south-southeasterly case there are several observations in which the downward sensible heat is
enhanced at the north station, although the mean difference is relatively small. We are
unsure why the null hypotheses are rejected for the S-SSE condition when the difference
in flux is relatively small (< 5 W m-2). This sector of wind directions represents flow
between turbines B2 and B3, so perhaps some other measure of sorting the data (e.g. hub
height wind speed) would be useful in developing a conceptual model for the overspeeding aloft and at the surface between two turbines (e.g. Hirth and Schroeder 2013).
4.2 Latent heat flux
For the accumulations of latent heat flux in Figure 5 we observe a definite
enhancement of daytime evapo-transpiration at NLAE 2 when the station is under the
influence of the B1 or B2 turbines (at a distance of 5D or 3D downwind) as compared to
the null cases (EW and EOFF). Among the two null cases, as shown in Table 3a,
there is high variability of the daytime flux differences, and the mean is quite small (< 5
W m-2, ambient values near 325 W m -2). The west-south-westerly direction indicates
that the B1 turbine wake is overhead of the NLAE 2 flux station and significantly
increases transpiration (up to 1.0 mm d-1 or about 10% increase) over the reference flux
(350 W m-2) at the southern station (NLAE 1). The distribution of the difference of the
latent heat flux is quite similar to the sensible heat as the turbine ON composites feature
near-zero skewness, but a platykurtic (flatter peak) shape. The turbines OFF composite
has a negative skewness but the distribution is slightly more normal (or Gaussian) as
compared to the other cases. Table 6a shows no significant difference between the flux
differences for the turbines ON composite and those of westerly conditions. The latent
heat flux is enhanced downwind of the turbines for wind directions from the southwest
quadrant (WSW to SSW), but not for flow between turbines B2 and B3 in the southerly
wind condition (ES-SSE).
The highest confidence of the flux difference (p<0.004)
occurs in the west-south-westerly direction where the wake at NLAE 2 presumably has
some interaction with the surface at a distance of 5.4 D downstream of turbine B1. This
79
particular enhancement of evapo-transpiration for WSW winds is verified in the
comparison to the second null case (turbines offline). The individual cases from the
south-west and south-southwesterly directions (between the B1 and B2 turbine and
within the B2 wake, respectively) give conflicting significance results between the two
statistical tests presented. We presume among the large standard deviations of the
differences that the latent heat flux is more perturbed on the edge of the turbine wake
(for SW wind direction) vs. less flux difference within the centerline position of the
wake (for SSW wind direction). The mean 8 W m-2 reduction of latent heat flux in the
south-south-easterly direction, however, depicts a peculiar result. For the comparison to
westerly flow we cannot reject the null hypothesis of no flux difference between the
westerly and southerly directions. However, in the second null case (turbines OFF) we
can safely assume that the differences are significant; but the flux is downward and not
upward as in the west southwesterly to southwesterly wind directions. The southerly
direction indicates the flux difference is slightly less variable as to when the turbines are
offline and the mean difference is also larger in the southerly case. Although the result
seems ‘statistically important’, the high variability of the flux suggests a different type of
turbine impact than for the other wind directions from the southwesterly quadrant. Since
we have observed wake-edge effects of fluxes on the aerodynamics that do not match
our conceptual model, we propose that there may be a reduction of outgoing vapor flux
for the flow moving between two turbines.
The decrease in flux for this direction is
observed in both the DAY and NIGHT conditions.
At night there is about an equal number of accumulated positive flux differences
among the two null case composites and the ON composite. The H2O flux differences
between the two stations in Table 3b are small (<10 W m-2) for the composite ON
condition and about 15 W m-2 for the westerly case. There are conflicting signs of the
flux difference among the individual case ESSW, for which we currently cannot offer
any explanation. For the west-south-westerly direction we observe less variability in the
flux differences. This result may indicate a weak wake influence from turbine B1 as in
the daytime scenario of increased transpiration.
The nighttime plots of the flux
80
difference reveal distributions with higher skewness and sharper peaks around the mean
(leptokurtic). This pattern may be evidence of the greater role of nighttime intermittency
in surface layer fluxes.
In both the statistical testing of the composite ON case and
those for the individual directions to the two null cases there is low confidence in the
northern station reporting significantly different latent heat than at the reference station
(Table 6b). We are uncertain on the role of turbines in modifying nighttime latent heat
flux and recognize the complexities in crop physiology with H2O exchange. Among the
individual 30-min nighttime observations the flux indicates the crop transpiring on the
downwind side of the turbine line, whereas at the reference station (NLAE 1) there are
more periods with condensation occurring, as indicated by a negative heat flux.
4.3 Carbon Dioxide flux
The accumulated differences of the daytime GPP and the nighttime Re under each
null and test condition are shown in Figure 6. The overall mean difference and standard
deviations of the NLAE 2-NLAE 1 CO2 flux difference (fc) are also presented for each
DAY and NIGHT condition.
We observe higher GPP (Re) at the northern tower for all
daytime (nighttime) samples of the ON composite. The daytime CO2 flux difference is
about 25% larger in this case than when the winds are from the west, whereas there is
hardly any difference in the flux (-0.03 g m-2 s-1) between when the turbines are
operational and when the wind farm is offline. For the wind direction categories WSW
and SW we observe a mean flux difference between NLAE 2 and NLAE 1 of about 0.20
mg m-2 s-1 (or 5 mol m-2 s-1) as shown in Table 4a. Isolated individual observations
may have a flux difference as large as 0.40-0.50 mg m-2 s-1. These differences agree
with our conceptual model. We conclude there is consistent daytime coupling in the
turbine-enhanced downward (upward) turbulent flux of CO2 (H2O) for this range of
wind directions. The turbine wakes from B2 and B1 are expanding and likely reaching
the surface at approximately 3.0 to 5.5D downstream of the turbines. The sample size of
cases is quite small (n=25), but our statistical analysis of the daytime CO2 flux confirms
that the difference is near the significance cutoff.
In Table 7a for comparison to the
81
westerly null case, the composite ON case demonstrates that the CO2 flux difference is
somewhat significant (p-value is slightly less than 0.05) according to the t-test, but the
Wilcoxon test suggests we cannot reject the null hypothesis. For comparison of the
westerly composite to the oblique flow directions in the southwesterly quadrant (WSW,
SW, SSW) we detect strong evidence (p<=0.005) for larger downward CO2 flux in the
wakes and between the wakes of turbine B1 and B2. However, for the comparison of
these individual directions to the composite OFF case there is little-to-no evidence to
support our conceptual model of wind turbine wake perturbation of the CO2 fluxes. One
possible explanation of the conflicting results between our two “null” cases is the flow
perturbations around obstacles (e.g. Wang et al. 2001). That is, even motionless turbines
in a relatively homogeneous boundary layer likely have some influence on all the energy
and CO2 surface fluxes. This may support the speculation of Rajewski et al. (2013) that
the static pressure field between each line of turbines measurably perturbs the flow,
regardless of whether the turbines are on or off. Turbine tower shadow effects of overspeeding flow below and between turbine rotors have been previously reported in
smaller-scale turbines (e.g. Ainslee 1988; Whale et al. 1996).
At night, we observe higher respiration by 0.20-0.40 mg m-2 s-1 at the NLAE 2
station for the WSW and SSW wind direction cases whereas there is very little station
difference under the two null conditions. Similarly to our daytime conclusions, we
propose that nighttime wakes also influence the surface. However, fluxes are enhanced
closer downwind of turbine B2 (3.0 D downstream) under the south-south-westerly flow
as compared to the 5.4 D distance from the B1 turbine for west-south-westerly flow.
Respiration is both a thermodynamic and dynamic process and so we recognize that both
canopy temperature and soil respiration are important in describing the turbine impact on
the CO2 flux. Unfortunately, we did not measure either of these variables and so this
partition of the respirative forcing is omitted from the analyses. We observe in Table 4b
slightly higher variability in the difference of the CO2 fluxes for the night conditions
than during the day but there is higher confidence in the nighttime positive flux
difference at NLAE 2 for the SW and SSW directions. The distribution of the CO 2 flux
82
differences closely follows the nighttime patterns for both the sensible and latent heat
fluxes (large skewness and very sharp peaks around the means). Only in the WSW
turbine ON case is skewness near zero and the distribution less peaked than the Gaussian
curve. In Table 7b we present the statistical tests of the differences in respiration flux.
In comparison to both null composites we find high confidence (p < 0.001) that the
northern station is significantly different than the upwind station for several individual
directions in the southwesterly quadrant (WSW, SW, SSW). Our tests in the southerly
to south-south-easterly wind direction demonstrate very weak evidence that the CO2
fluxes are significantly different from the west or turbines OFF cases, but perhaps this
result is associated with the large variability of the flux differences for the southerly
data. The higher frequency of southerly to southeasterly flow conditions warrants future
analysis of between-turbine influences on aerodynamics and the related crop energy and
CO2 fluxes.
4.4 Friction velocity perturbations linked to flux differences
The consistent enhancement of fluxes in both
day or night periods for the
southwesterly wind directions provides strong evidence of the turbines perturbing the
vertical fluxes of momentum as revealed by the friction velocity (u*).
In the daytime,
the difference in u* between the two stations is negligible or at most a 3% decrease
(u*NLAE 1=0.3 m s-1) for when the turbines are offline, nearly zero for the westerly wind
direction, and about a 2% increase (u*NLAE 1=0.5 m s-1) for when the turbines are
operating (Figure 7).
These range of differences are negligible for numerical or wind
tunnel experiments and near the instrument error of the sonic anemometer.
For the nighttime, we report 5% higher friction velocity at the northern flux station for
the two null cases whereas the composite ON case features a downwind station increase
of 15% and for the individual directions (WSW, SSW, and SW) we indicate up to 2570% higher u* than at the upwind station.
Additionally, for the individual ON cases we
observe slightly less variability ( u* u*=0.05 ms-1) than for the null cases, or the
remaining southerly to south-south-easterly case. Statistical tests show strong
83
confidence (p<0.001) that the turbines are enhancing nighttime mixing for all directions
reported as compared to both of the null westerly and turbines OFF composites.
5. Discussion
Our overall results show some evidence in the daytime that a turbine wake can
perturb H2O and CO2 fluxes at a distance of 5.4 D downstream. This location indicates
some expansion of the wake although it is unlikely that the wake has reached the canopy
surface. For the 5.4 D location in the wake we report about an 8-10% increase in GPP,
but this is smaller than the reported difference of CO2 flux (12-20%) between two maize
fields, one irrigated and one rain fed system each with a different hybrid by Suyker et al.
(2004). However, individual events occur when flux perturbations attributed to turbines
may match or exceed those related to other field-scale heterogeneities.
At night,
ecosystem respiration and sensible heating are enhanced by the turbines when the wake
is both directly overhead the flux station or when the outside edge of the wake is above
the station. Our estimates of the nighttime turbine-perturbed heat flux are in reasonable
agreement with numerical studies that parameterize the momentum sink and turbulence
sources of the turbines (Baidya Roy 2004; Baidya Roy et al. 2011; Fitch et al. 2012;
Fitch et al. 2013a). The representation of the flux differences also is comparable to the
scaled wind tunnel experimental results (Zhang et al., 2012a, 2013), among others. We
observe an increased (more negative) heat flux at NLAE 2 unlike the decreased (less
negative) flux demonstrated in the nocturnal LES simulation of Lu and Porté-Agel
(2011), noting that the simulation used an infinite turbine array for deep wind farm
impacts while we study the effect of the first row of turbines. It is plausible that for more
oblique wind directions (“SE” and “ESE”) our measurements would be responding to a
reduction in the scalar flux from the influence of multiple lines of turbines south-east
and east-south-east of the CWEX study area).
Our CWEX-10 measurements
corroborate that downward sensible heating is slightly increased in the gap region
between two turbines but the mechanism inducing the difference remains unclear. We
believe the forcing occurs from a combination of pressure field drag effects suggested by
84
Smith (2009) or the over-speeding zone between two turbines that was previously
reported in Rajewski et al. (2013) and detected recently in Hirth and Schroeder K-a band
radar imagery (2013).
We find agreement in our results and in our conceptual model of turbine-turbulence
more efficiently perturbing nighttime scales of above-canopy mixing. In the southerly
case there is a slight decrease in the mean difference of u* and we provide some insights
from previously reported work on the connection to u* and scalar flux.
The wind
tunnel studies of Zhang et al. (2012a and 2013) report how the aligned turbine arrays
give alternating patches of higher (lower) scalar flux on the left (right) cross-wind side
of the turbine for daytime boundary layer situations.
This may correspond to the
downward sweep induced by the blades on the left side of a turbine rotating clockwise
and a corresponding upward sweep on the right side of the rotor (e.g. Yang et al. 2011).
These upward and downward sweeps would mark the edge of the blade-tip vortices,
which undergo expansion and a helical rotation around the downwind side of the
turbines to create the turbulence in the wake. The vortex rotation is opposite of the
rotation of the turbine blades to conserve angular momentum. Field detection of these
wake vortices from operational scale turbines is very limited, so our understanding of the
structure and evolution of the wake is developed from wind tunnel or numerical
simulations.
Zhang et al. (2012b), reports that for neutral or convective boundary layer conditions
the tip vortices are present at a distance of 3 D downwind of the turbine, whereas by 5 D
there is complete dissipation of these motions. Our field measurements demonstrate
some agreement to those daytime findings, yet we believe that that some coherent
structure of the wake is influencing the surface fluxes at a distance of greater than 5 D
downwind, at least at night.
Under a southerly wind direction, NLAE 2 may be
influenced by the left side of this wake vortex and therefore the sign in the flux
difference is not consistent with what we expect would happen on the inside of the wake.
(An expansion of between-turbines ‘gap flow’ as suggested in the Rajewski et al. (2013)
will be presented in a forthcoming manuscript). Calaf et al. (2011) in their numerical
85
simulations commented on two opposing forces on the sensible heat flux perturbation.
In a fully developed turbine-wake boundary layer, the turbine wakes are increasing u*
beyond the top of the blades (u*hi) whereas below the turbines, the speed reduction in the
wake leads to a decrease in the friction velocity (u*low). This ratio of u*hi/u*low will
control how the scalar flux will change near the surface. We do not completely observe
this effect in our data as we are only comparing effects on the upwind and downwind
side of one line of turbines.
6. Conclusions
Surface fluxes measured upwind and downwind of a line or turbines give evidence of
conditional crop microclimate modification by individual wind turbines. Canopy fluxes
could be perturbed directly by the enhanced turbulence in the wake, by the reduction in
vertical mixing underneath the turbine wake, or by influences of the static pressure field
between each line of turbines.
We observe in the daytime CO2 flux a small but not
statistically significant difference when the turbines are ON as compared to when the
turbines are OFF. Our flux difference comparisons of the ON cases to westerly winds
contrarily indicate a potentially five-fold increase in CO2 flux at NLAE 2 for the ON
cases. The CO2 flux difference is most noticeable when the turbine wake has reached
the surface at a distance of 5.4 D downstream. Concurrent daytime fluxes of H2O are
significant for the particular wind directions (WSW to SW) and we have evidence that
transpiration may be enhanced at most around 1 mm/day.
Our results show that the
turbines did not contribute to sensible heat flux during the day at levels measurably
above ambient. Conversely, at night the ambient turbulence is enhanced by the mixing
generated from the turbines such that CO2 respiration is increased 1.5-to-2 times the
reference magnitude.
We again demonstrate the southwesterly wind direction in
favoring the middle position or the outside edge of the B2 turbine wake being directly
overhead of the northern flux station. Nighttime sensible heat perturbations follow a
similar pattern of significance to those of the CO2 respiration flux, whereas we cannot
discern any major differences in the latent heat flux among our conditions tested. In
86
both the daytime and the nighttime conditions the S-SSE direction often presents
conflicting results to those observed in the other individual directions.
The flux
perturbations for these cases likely demonstrate flow asymmetries on the turbulence
wake edge and other pressure field flow distortions around the line of turbines. A
forthcoming note will address some of these flow features for the southerly wind
directions, and we anticipate from the larger sample size to carefully pinpoint the
subtleties of canopy exchanges for near-normal oriented flow between two turbines.
Although we report changes in the CO2 and H2O fluxes above the canopy, we made
no measurement of biophysical changes within the crop.
Therefore we cannot yet
determine the overall impact of wind turbines and wind farms on yield. There is weak
evidence that the daytime CO2 uptake can be increased for fields that are within a
location of 5.4 D from a turbine but we have stronger confidence that nighttime
respiration is exacerbated in the lee of the turbine line. We may therefore surmise that in
the CWEX-10 study both an enhanced downward flux of CO2 into the canopy during the
daytime and a comparable or higher nocturnal venting of CO2 (via increased mixing and
a warmer nighttime temperature) oppose each other and therefore limit the aggregate
benefit to maize yield.
High-resolution monitoring of grain yield both north and south
of the turbine line revealed no significant departures from the field-scale mean yield in
2010 (variability was within ± 5 bushels/acre). Quantifying the perceived impact of
wind farms on crop yield remains a topic for future field experimentation.
Acknowledgments
This work was supported in part by the National Renewable Energy Laboratory under
Professor Lundquist’s Joint Appointment. NREL is a national laboratory of the U.S.
Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by
the Alliance for Sustainable Energy, LLC. Partial funding for CWEX-10 was provided
by the Ames Laboratory (DOE) and the Center for Global and Regional Environmental
Research at the University of Iowa. Surface flux stations for CWEX-11 were provided
by NCAR Earth Observing Laboratory under an instrumentation deployment, and
87
undergraduate student participation was supplemented by funding from an NSF REU
program under grant 1063048. The authors recognize assistance in interpretation of the
results from James Brache, Thomas Sauer, Fernando Miguez, and Sotoris Archontoulis).
Data analysis was supported in part by the National Science Foundation under the State
of Iowa EPSCoR Grant 1101284. The authors also acknowledge the land wind farm
manager and land owners/operators cooperation in conducting CWEX.
We give
recognition to one farm manager who shared their Yield Monitor information for the
2010 growing season. The SCADA data provided by the owner of the meteorological
observations within the wind farm was also a beneficial component in conducting this
analysis.
7. References
Adams, A., Keith, D.W., cited 2009. A wind farm parameterization for WRF, 8th WRF
Users Workshop, June 2007, Boulder, CO. (Available online at
http://www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/5-5_Adams.pdf)
Ainslie JF (1988). Calculating the flowfield in the wake of wind turbines. J. Wind
Eng.Ind. Aerodyn., 27: 213–224.
American Wind Energy Association, cited 2013: U.S. wind industry year-end 2012
market report. [Available online at
http://www.awea.org/suite/upload/AWEA_USWindIndustryAnnualMarketReport20
12_ExecutiveSummary.pdf]
Baidya Roy S., Pacala, SW., Walko, RL (2004) Can large wind farms affect local
meteorology? J. Geophys. Res., 109, D19 101, doi: 10.1029/2004JD004763.
Baidya Roy S (2011). Simulating impacts of wind farms on local hydrometeorology. J.
Wind Eng. Ind. Aerodyn., 99: 491–498, doi: 10.1016/j.jweia.2010.12.013.
-----, Traiteur, J (2010). Impact of wind farms on surface temperatures. Proc. Natl.
Acad. Sci. U.S.A., 107:17899-17904, doi: 10.1073/pnas.1000493107.
Barthelmie, RJ (1999). The effects of atmospheric stability on coastal wind climates.
Meteor. Appl., 6: 39–48.
-----., Pryor, SC, Frandsen, ST, Hansen, KS, Schepers, JG, Rados, K, Schlez, W,
Neubert, A, Jensen, LE, Necklemann, S (2010). Quantifying the Impact of Wind
88
Turbine Wakes on Power Output at Offshore Wind Farms. J. Atmos. Oceanic
Technol., 27: 1302–1317. doi:10.1175/2010JTECHA1398.1.
Cevarich, M, Baidya Roy S (2013). Impact of wind farms on surface and air
temperatures. Fourth Conf. on Weath., Clim., and the New Energy Econ. Paper
7.1, Tuesday, 8 January, Austin, TX.
Connell JR, George RL (1982) The wake of the MOD-OA wind turbine two rotor
diameters downwind on 3rd December 1981. Report no. PNL-4210, Pacific
Northwest Laboratory, Battelle.
Emeis S (2010) A simple analytical wind park model considering atmospheric stability.
Wind Energy, 13: 459-469. doi:10.1002/we.367.
Fitch AC, Olson JB, Lundquist JK, et al. (2012) Local and Mesoscale Impacts of Wind
Farms as Parameterized in a Mesoscale NWP Model. Month. Weath. Rev.
140:3017–3038. doi: 10.1175/MWR-D-11-00352.1.
-----, Lundquist JK, Olson JB (2013a). Mesoscale Influences of Wind Farms throughout
a diurnal cycle. Accepted for publication in Monthly Weather Review.
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00185.1
-----., Olson JB, Lundquist JK. (2013b). Representation of wind farms in climate models.
Accepted for publication in J. Climate http://dx.doi.org/10.1175/JCLI-D-12-00376.
Henschen, M, Demchak, K, Herrholtz, B, Rudkin, M, Rhudy, L, Larson, E, Doogs, B,
Holland, J, Martin J (2011). Do wind turbines affect weather conditions? A case
study in Indiana. Journal of Purdue Undergraduate Research, 1, 22 – 29. doi:
10.5703/jpur.01.1.4
Hirth, BD, Schroeder, JL (2013). Documenting Wind Speed and Power Deficits behind
a Utility-Scale Wind Turbine. J. Appl. Meteorol. and Climatol., 52: 39–46. doi:
10.1175/JAMC-D-12-0145.1
Leuning, R, van Gorsel, E, Massman, W, Isaac, PR, (2012) Reflections on the surface
energy imbalance problem, Agr. Forest Meteorol., 156, 65–74.
doi:10.1016/j.agrformet.2011.12.002.
Lovett, GM, Cole, JJ, Pace, ML (2006). Is net ecosystem production equal to ecosystem
carbon accumulation? Ecosyst., 9, 1-4. doi:10.1007/s10021-005-0036-3.
Lu H, Porté-Agel F (2011) Large-eddy simulation of a very large wind farm in a stable
atmospheric boundary layer. Physics of Fluids 23:065101. doi: 10.1063/1.3589857
89
Pichugina YL, Banta RM, Kelley ND, (2005): Application of high resolution doppler
lidar data for wind energy assessment. 2nd symposium of Lidar Atmospheric
Applications for the 85th American Meteorological Society Annual Meeting.
Paper 4.6, Tuesday, 11 January, San Diego, CA. (Available online at:
http://ams.confex.com/ams/pdfpapers/86691.pdf).
Rajewski DA, Takle ES, Lundquist JK, Oncley SP, Prueger JH, Horst TW, Rhodes ME,
Pfeiffer R, Hatfield JL, Spoth KK, Doorenbos RK (2013). CWEX: Crop/Wind
Energy Experiment: observations of surface-layer, boundary-layer and mesoscale
interactions with a wind farm Bull. Am. Meteorol. Soc. In press
(doi:10.1175/BAMS-D-11-00240).
Schotanus P, Nieuwstadt FTM, DeBruin, HAR(1983). Temperature measurement with a
sonic anemometer and its application to heat and moisture fluctuations. Boundary
Layer Meteorol., 26: 81-93.
Smith RB (2009) Gravity wave effects on wind farm efficiency. Wind Energy,
13: 449-458. doi: 10.1002/we.366.
Storm B and Basu, S (2010). The WRF model forecast-derived low-level wind shear
climatology over the United States Great Plains. Energies, 3: 258-276.
Stull,R (1988) An Introduction to Boundary Layer Meteorology. Kluwer Academic
Publishers, The Netherlands,. 666 pp.
Suyker AE, Verma SB , Burba GG, Arkebauer TJ, Walters DT, Hubbard KG (2004).
Growing season carbon dioxide exchange in irrigated and rainfed maize.
Agric. Forest Meteorol., 124: 1-13. doi: 10.1016/j.agrformet.2004.01.011.
U.S. DOE Office of Energy Efficiency and Renewable Energy, cited 2008: 20% Wind
Energy by 2030: Increasing Wind Energy's Contribution to U.S. Electricity Supply,
248 pp.; NREL Report No. TP-500-41869; DOE/GO-102008-2567. (Available
online at http://www.nrel.gov/docs/fy08osti/41869.pdf).
Walsh-Thomas JM, Cervone G., Agouris P, Manca G (2012) Further evidence of
impacts of large-scale wind farms on land surface temperature. J. Renewable
Sustainable Energy. Rev., 16: 6432-6437. doi:/10.1016/j.rser.2012.07.004.
Wang C, Prinn RG, (2010) Potential climatic impacts and reliability of very large-scale
wind farms. Atmos. Chem. Phys., 10: 2053-2061. doi/10/2053/2010.
Wang H, Takle, ES (1995) A numerical simulation of boundary-layer flows near
shelterbelts. Boundary Layer Meteorol., 75:141-173.
90
-----, Shen, J (2001) Shelterbelts and windbreaks: Mathematical modeling and computer
simulation of turbulent flows. Ann. Rev. Fluid Mech.,
33: 549-586.
Webb, EK, Pearman, GI, Leuning, R (1980) Correction of flux measurements for
density effects due to heat and water vapor transfer. Quart. J. Roy. Meteorol. Soc.,
106: 85-100.
Whale J, Papadopoulos KH, Anderson CG, et al.(1996) A study of the near wake
structure of a wind turbine comparing measurements from laboratory and full-scale
experiments. Solar Energy, 56:621–633.
Wilczak, J M, Oncley, SP, Stage SA (2001) Sonic anemometer tilt correction
algorithms. Boundary Layer Meteorol., 99:127–150.
Yang, Z, Sarkar, P, Hu, H, (2012) Visualization of the tip vortices in a wind turbine
wake. J. Visualization. 15: 39-44. doi:10.1007/s12650-011-0112z.
Zhou, L M, Tian, YH, Baidya Roy S, Thorncroft, C, Bosart, L F, Hu, Y L (2012)
Impacts of wind farms on land surface temperature. Nature Clim. Change 2: 539–
543. doi:10.1038/nclimate1505.
Zhou, L M, Tian, YH, Baidya Roy, S, Dai, Y, Chen, H, (2012) Diurnal and seasonal
variations wind farm impacts on land surface temperature over western Texas. Clim
Dyn. doi: 10.1007/s00832-012-1484-y.
Zhang W, Markfort CD, Porté-Agel F, (2012a) Wind-turbine wakes in a convective
boundary layer: a wind-tunnel study. Boundary Layer Meteorol.,
(doi:10.1007/s10546-012-9751-4).
Zhang W, Markfort CD, Porté-Agel F (2012b) Near-wake flow structure downwind of a
wind turbine in a turbulent boundary layer. Exp. Fluids, 52: 1219–1235. doi:
10.1007/s00348-011-1250-8.
Zhang, W, Markfort, CD, Porté-Agel, F (2013) Experimental study of the impact of
large-scale wind farms on land–atmosphere exchanges. Environ. Res. Lett., 8, 015002.
91
Figure 1. Conceptual model of wind turbine wake flow and the corresponding
effect on crop fluxes
92
Figure 2. Energy budget closure trend line and correlation coefficient (R2) of (Rnet–
G vs. H+E+ Fc) among the upwind and downwind flux stations: NLAE 1-NLAE 2
and NCAR 1 and NCAR 3.
93
Figure 3. Graphical representation of ‘wake’ wind direction sectors for the
downwind flux tower (NLAE 2) overlaid on the CWEX-10/11 measurement
locations. These directional categories are represented in the flux difference
directional day and nighttime composites.
94
Figure 4. Wind direction-sector accumulated downwind-upwind differences in
sensible heat flux (H) for the DAY and the NIGHT case. The downwind-upwind
mean difference and standard deviations of the difference in H are overlaid for
each directional composite category and distance from an individual turbine.
95
Figure 5. As in Figure 4 but for latent heat flux (E).
96
Figure 6. Wind direction-sector accumulated downwind-upwind differences in
CO2 Gross Primary Production (GPP) for the DAY case and net ecosystem
respiration (Re) for the NIGHT case. The downwind-upwind mean difference and
standard deviations of the differences in CO2 flux (fc) are overlaid for each
directional composite.
97
Figure 7. Mean and standard deviations of the downwind-upwind differences in u*
for the DAY and the NIGHT cases of directional composite category and distance
from an individual turbine.
98
Table 1. Wind direction sectors corresponding to the turbine wake or gap (between
turbine) flow for the B1 to B3 turbines on the leading line of turbines at the wind
farm. Composites of these direction sectors are included for when the turbines were
operational or offline. The number of observations in the DAY and NIGHT cases is
included.
Case direction
category
Turbine wake
Indicator
OFF
ON
W
WSW [B1]
SW [B12G]
SSW [B2]
S-SSE [B23G]
(combination turbines offline)
(combination turbines operating)
(Westerly no-wake, turbines on and off )
B1 (5.3 D to turbine)
gap between B1 and B2 (3.8 D to line)
B2 (2.7 D to turbine)
gap between B2 and B3 (2.6 D to line)
Sample size (N)
DAY
94
333
35
25
31
79
198
Sample size (N)
NIGHT
98
529
60
43
19
51
416
99
Table 2. Means, standard deviations, the 95% upper and lower confidence interval of the mean difference, and the
skewness and kurtosis of the difference distributions for (a) DAY and (b) NIGHT downwind-upwind sensible heat flux
differences (H) for each direction composite as presented in Table 1.
a)
DAY
Rnet > 300 W
m-2
Wind
H
H
direction
Lower 95% Upper 95% Skewness
Kurtosis
-2
-2
-2
-2
category
(W m )
(W m )
(W m )
(W m )
-2.29
13.64
-6.98
2.40
-0.15
-0.21
HOFF
-3.65
18.54
-7.45
0.15
-0.97
3.27
HON
0.83
15.55
-0.84
2.51
0.18
0.71
HWSW
1.88
10.26
-2.36
6.11
0.04
-0.46
HSW
-3.61
12.26
-8.11
0.89
-0.26
0.05
HSSW
1.98
15.20
-1.43
5.38
0.05
0.72
HS-SSE
0.94
16.63
-1.39
3.27
0.22
0.57
99
HW
100
Table 2. (continued)
b)
NIGHT
Rnet < 0 W m-2
H
(W m-2)
H
(W m-2)
Lower 95%
(W m-2)
Upper 95%
(W m-2)
Skewness
Kurtosis
HW
0.57
5.78
-0.92
2.07
-0.61
3.24
HOFF
-0.80
4.90
-1.78
0.18
-0.05
0.22
HON
-4.67
10.22
-5.54
-3.80
-0.90
2.53
HWSW
-9.65
12.23
-13.41
-5.89
-0.58
-0.56
HSW
-13.90
16.50
-21.85
-5.95
0.86
0.39
HSSW
-7.04
13.96
-10.97
-3.12
-1.37
2.99
HS-SSE
-3.44
8.59
-4.27
-2.62
-0.53
2.11
100
Wind
direction
category
101
Table 3. Same as in Table 2 but for downwind-upwind latent heat flux differences (E) for each direction composite as
presented in Table 1.
a)
DAY
Rnet > 300 W m-2
Wind
direction
category
 E
(W
m-2)

(W m-2)
Lower 95%
(W
m-2)
Upper 95%
(W
Skewness
Kurtosis
m-2)
-1.70
39.88
-15.40
12.00
0.09
-0.36
EOFF
3.25
57.26
-8.48
14.98
-0.76
1.57
EON
3.58
44.15
-1.18
8.34
0.25
0.66
EWSW
31.20
41.05
14.25
48.14
0.64
0.45
ESW
26.57
55.00
6.40
46.75
0.23
0.18
ESSW
15.87
39.78
6.96
24.78
0.11
0.09
ES-SSE
-8.42
40.10
-14.04
-2.80
0.09
0.86
101
EW
102
Table 3. (continued)
b)
NIGHT
Rnet < 0 W m-2
Wind
direction
category
 E
(W
m-2)
E
(W
m-2)
Lower 95%
(W
m-2)
Upper 95%
(W
Skewness
Kurtosis
m-2)
4.41
32.87
-4.09
12.90
5.09
35.33
EOFF
-1.26
11.60
-0.49
1.07
-4.48
22.75
EON
1.18
19.53
3.58
1.07
-0.54
37.54
EWSW
4.28
6.42
2.30
6.26
0.76
1.17
ESW
12.17
19.94
2.56
21.78
2.15
5.51
ESSW
-0.49
24.46
-7.37
6.39
-2.67
12.22
ES-SSE
0.56
19.62
-1.33
2.45
-0.11
43.02
102
EW
103
Table 4. Same as in Table 3 but for downwind-upwind CO2 flux differences (fc) for each direction composite as
presented in Table 1.
a)
DAY
Rnet > 300 W
m-2
Wind
 fc
fc
direction
Lower 95% Upper 95% Skewness
Kurtosis
category
(mg m-2 s-1)
(mg m-2 s-1) (mg m-2 s-1) (mg m-2 s-1)
-0.03
0.20
-0.09
0.04
-0.55
1.41
fcOFF
-0.11
0.30
-0.17
-0.05
0.76
1.95
fcON
-0.11
0.23
-0.14
-0.09
-0.69
0.28
fcWSW
-0.19
0.20
-0.28
-0.11
-0.44
-0.54
fcSW
-0.19
0.27
-0.29
-0.10
-0.63
0.01
fcSSW
-0.14
0.20
-0.18
-0.09
0.14
0.13
fcS-SSE
-0.08
0.23
-0.11
-0.05
-0.99
0.70
103
fcW
104
Table 4. (continued)
b)
NIGHT
Rnet < 0 W m-2
Wind
direction
category
fc
(mg m-2 s-1)
Lower 95%
(mg m-2 s-1)
Upper 95%
(mg m-2 s-1)
Skewness
Kurtosis
fcW
-0.01
0.49
-0.14
0.12
-4.83
34.18
fcOFF
0.00
0.22
-0.04
0.05
1.55
11.75
fcON
0.09
0.43
0.05
0.13
5.28
56.56
fcWSW
0.20
0.44
0.06
0.33
0.22
0.83
fcSW
0.40
0.36
0.22
0.57
0.95
0.79
fcSSW
0.30
0.75
0.09
0.52
3.23
13.36
fcS-SSE
0.04
0.36
0.00
0.07
7.26
112.73
104
 fc
(mg m-2 s-1)
105
Table 5. Tests of statistical significance (t-test, and Wilcoxon-pair) for the means of the flux
differences of sensible heat flux (H) for each of the comparison categories tested in the
DAY case (a) and the NIGHT case (b). Statistical significance is identified by bold red
labeling if the mean differences are not equal according to a probability threshold at or
exceeding 95%.
a)
DAY
Rnet > 300 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
HON = HW
0.25
0.30
HWSW = HW
0.30
0.27
HSW = HW
0.73
0.73
HSSW = HW
0.17
0.19
HS-SSE = HW
0.25
0.33
HON = HOFF
0.02
0.05
HWSW = HOFF
0.13
0.10
HSW = HOFF
0.99
0.86
HSSW = HOFF
0.02
0.04
HS-SSE = HOFF
0.02
0.08
b)
NIGHT
Rnet < 0 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
HON = HW
<0.001
<0.0001
HWSW = HW
<0.001
<0.0001
HSW = HW
<0.001
<0.0001
HSSW = HW
<0.0001
<0.0001
HS-SSE = HW
0.003
<0.0001
HON = HOFF
<0.001
<0.001
HWSW = HOFF
<0.0001
<0.0001
HSW = HOFF
<0.0001
<0.001
HSSW = HOFF
<0.001
0.001
HS-SSE = HOFF
0.01
0.001
106
Table 6. Same as in Table 5 but for latent heat flux (E).
a)
DAY
Rnet > 300 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
EON = EW
0.50
0.57
EWSW = EW
0.003
0.004
ESW = EW
0.006
0.03
ESSW = EW
0.04
0.03
ES-SSE = EW
0.38
0.34
EON = EOFF
0.95
0.66
EWSW = EOFF
0.007
0.02
ESW = EOFF
0.01
0.07
ESSW = EOFF
0.07
0.14
ES-SSE = EOFF
0.04
0.02
b)
NIGHT
Rnet < 0 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
EON = EW
0.46
0.80
EWSW = EW
0.98
0.01
ESW = EW
0.16
0.01
ESSW = EW
0.23
0.29
ES-SSE = EW
0.19
0.65
EON = EOFF
0.23
0.25
EWSW = EOFF
0.10
<0.001
ESW = EOFF
0.00
0.001
ESSW = EOFF
0.81
0.08
ES-SSE = EOFF
0.38
0.85
107
Table 7. Same as in Table 5 but for CO2 flux (fc).
a)
DAY
Rnet > 300 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
fcON = fcW
0.03
0.06
fcWSW = fcW
0.004
0.003
fcSW = fcW
0.002
0.01
fcSSW = fcW
0.01
0.002
fcS-SSE = fcW
0.20
0.53
fcON = fcOFF
0.92
0.38
fcWSW = fcOFF
0.12
0.24
fcSW = fcOFF
0.09
0.35
fcSSW = fcOFF
0.44
0.60
fcS-SSE = fcOFF
0.31
0.03
b)
NIGHT
Rnet < 0 W m-2
Directional
comparison
null hypothesis
t-test p-value
(=0.95)
Wilcoxon pair p-value
(=0.95)
fcON = fcW
0.09
0.34
fcWSW = fcW
0.02
0.004
fcSW = fcW
<0.001
<0.0001
fcSSW = fcW
<0.001
<0.0001
fcS-SSE = fcW
0.41
0.70
fcON = fcOFF
0.05
<0.001
fcWSW = fcOFF
0.008
<0.0001
fcSW = fcOFF
<0.0001
<0.0001
fcSSW = fcOFF
<0.0001
<0.0001
fcS-SSE = fcOFF
0.44
0.04
108
CHAPTER 4. AIR TEMPERATURE AND HEAT FLUX MEASUREMENTS WITHIN
TWO LINES OF TURBINES IN A LARGE OPERATIONAL WIND FARM
Daniel A. Rajewski
Abstract
Surface air temperature and heat flux measurements were recorded at several stations between
the leading lines of wind turbines within a large utility scale wind farm. Daytime differences
between the downwind and upwind stations corroborate similar mean differences in temperature
and heat flux for both daytime and nighttime conditions as previously reported in numerical or
wind tunnel studies. Remote sensing studies of nighttime temperature impacts by large wind
farms appear slightly warmer by a 0.75 to 1.5 °C difference when compared to the CWEX
observations, which were warmer by around 0.5 °C.
Heat flux differences from the CWEX
study demonstrate spatial variability on local areas of intense warming (cooling) created by the
cross-wind vortex circulation on the downward (upward) branch of an individual turbine wake.
Two aligned wakes may improve energy transfer of heat from aloft to the surface for roughly a
25% to 50% increase in the ambient flux than for the influence of one wake. The CWEX
measurements corroborate also indicate higher nighttime turbine disruption of scalar transport at
the flux station in closest proximity downwind of the turbine line as compared to previous field
and modeling studies. The results in this report illustrate that well-established turbine wake flow
may not perturb surface fluxes in the same intensity as from the leading edge of the first or
second turbine lines within a wind farm. Additional measurements of fluxes in the middle and
far end of the wind farm facilitate model validation of deep wind farm-array reduction of scalar
transport at the surface.
1. Introduction
Large wind farms constructed over much of the U.S. Midwest agricultural cropland and this
expansion will likely continue, as renewable resources become greater assets for meeting the
domestic demands of electrical generation (U.S. DOE 2008). Turbines and large wind farms are
installed on several millions of acres of cropland and the convergence of these two un-like
energy resources, crop-biomass and wind power, presents an interaction, which should be
investigated to further economic benefit for both industries. The Crop Wind-Energy Experiment
109
(CWEX) was conducted to answer key questions about how wind turbines within large wind
farms alter crop canopy flows and fluxes of heat, H2O, and CO2. Preliminary results presented in
Rajewski et al. (2013a) demonstrate that turbines reduce (enhance) daytime (nighttime) surface
wind speed at various locations downstream of a leading line of turbines in a full-scale wind
farm in Central Iowa. The CWEX study recently addresses the role turbines modify crop fluxes
of heat, moisture, and carbon dioxide. Impacts on crop fluxes depend on both meteorological
factors (ambient hub-height wind speed, wind direction, surface thermal stratification), turbine
operating characteristics (turbine model specifications, on/off operating mode), and the micro
scale variability of field characteristics due to crop management (e.g. tillage, crop type, cultivar)
and soil properties (Rajewski et al. 2013b). The scalar fluxes of H2O and CO2 in the daytime and
heat and CO2 fluxes in the nighttime indicate turbine wake influence at a distance of 5.4 D
downwind of a turbine.. The mechanisms to produce these flux differences are linked to the
static pressure field around the turbines, the direct interception of turbine-turbulence at the
surface, or an overhead vertical coupling of the ambient scales of surface turbulence. The results
demonstrate some plausible evidence for the daytime downward fluxes of heat between two
turbine wakes that are reported in recent wind tunnel experiments (e.g. Zhang et al. 2012, 2013).
The current study described here will address the impact of turbines on air temperatures and
will expand the analysis of sensible heat flux with more stations than those offered in the crop
energy flux study.
Few field studies with smaller turbines (e.g. 30 m hub height, two blade
models) demonstrate some change in thermal properties within a turbine wake. The increase in
the CT 2 parameter, which indicates higher temperature variance in the wake demonstrates wind
turbine isotropic mixing of the temperature profile (Högström et al. 1988; Kambezidis et al.
1990). The well-mixed temperature profile is simulated among recent numerical simulations of
large wind farms with 80-m hub height and similar-sized rotor diameters
Porte-Agél 2010, Calaf et al. 2011, Lu and Porte-Agél 2011).
(e.g. Chamorro and
Many of the field-scale
experiments (Kelley 1989, Helmis et al. 1995, Papadopoulous et al. 1995, Whale et al. 1996)
were conducted over heterogeneous terrain, which add uncertainty in characterizing the turbinewake modifications of surface fluxes. Högström et al. (1988) captured one clear separation of
temperature fluxes, variances, and triple moment heat eddy correlation terms for an isolated
waked and non-waked period downwind of a 2MW wind turbine both at the top of the rotor
depth (135m), hub height (77m) and near the surface (11 m). These early measurements as
110
reported could be interpreted as a ‘bulk’ effect of waked flow on the thermal profile and
therefore do not have the spatial resolution needed for comparison to current hi-resolution
numerical, and wind tunnel modeling validation.
Recent remote sensing analyses of surface temperatures within large wind farms indicates
that a 1.0 °C warming is likely during the overnight and about a 0.5 °C or less cooling during the
daytime. I am cautious to comment on the full scale of warming that has previously been
reported either due to the lack of in situ measurements (e.g. Zhou et al. 2012a, 2012b), the role of
complex terrain in the San Gorgonio wind farm (Kelley 1989), or the un-clarity of the
experimental method as presented in the Indiana wind farm study by Heschen et al. (2011). The
latter micrometeorological study from Indiana illustrates one particular location or portion of the
wind farm and is not representative of the full-wind farm effects that were presented in the West
Texas study by Zhou et al. (2012a, 2012b) or in the recent analyses of the San Gorgonio wind
farm by Baidya-Roy and Traiteur, (2010) and Walsh-Thomas et al. (2012).
The CWEX-10/11 measurements do not provide any information on deep-wind farm effects
but the data do support the relative scales of changes in temperature and heat flux that are
reported in field experiments of isolated turbines or in several numerical or wind tunnel
simulations of wind farms.
This report illustrates the complexities to understand which
conditions (meteorological and turbine-operation based) and to what intensity the leading line of
turbines in a wind farm modify air temperature and heat flux. Section 2 describes the essential
features of the experiment that are necessary to understand the measurement results. In Section
3, a summary of the analysis procedures is provided to display the mean differences in
temperature and heat flux between the upwind and downwind surface stations.
Section 4
highlights the key results of both the variability and the confidence intervals of the mean
differences according to data sorting metrics for the surface thermal stratification, flux tower
wind direction, and speed of the turbines. Finally, in Section 5, I offer additional insights on a
refined conceptual model of turbine aerodynamics and scalar flux exchanges with the surface
and in Section 6 conclude with a vision toward understanding deep-wind farm impacts on
temperature and scalar fluxes.
111
2. Materials and Methods
The full details of the CWEX-10/11 measurements are provided in the project overview
(Rajewski et al. 2013a). Some essential documentation of the downwind-upwind comparisons of
measurements in both years is described for the purpose of clarity. In 2010, the National
Laboratory for Agriculture and the Environment (NLAE) provided instruments and data
collection, whereas in 2011 instrumentation was provided by the Integrated Surface Flux
Systems group of the Earth Observing Laboratory of the National Center for Atmospheric
Research (NCAR) Earth Observing Laboratory. The University of Colorado and the National
Renewable Energy Laboratory (NREL) measured wind profiles with a light detection and
ranging (LiDAR) downwind of the turbines in 2010, one upwind, and one downwind of turbines
in 2011 to assist in overhead detection of turbine wakes.
For this report I acknowledge the use
of the upwind and downwind LiDAR wind cubes (WC 68 and WC 49) in the 2011 analysis of
temperature and heat flux. In the 2011 year the research team from Iowa State University (ISU)
installed two flux stations to compare with the NCAR station measurements.
Reference upwind (under prevailing southerly winds) flux stations were positioned south of
the first line of turbines (denoted as a ‘B’ line) [NLAE 1 in 2010 and NCAR 1 and ISU 1 in
2011]. Several flux stations were installed downwind of the ‘B’ turbine line in both 2010 [NLAE
2-4] and 2011 [NCAR 2-4, and ISU 2].
Of the total number of stations only one upwind and
one downwind station measured H2O and CO2 with identical instrumentation [NLAE 1 and
NLAE 2 in 2010 and NCAR 1 and NCAR 3 in 2011]. In 2011, two other downwind stations,
NCAR 2 and NCAR 4, calculated moisture flux with Krypton hygrometers but the flux
correction procedure did not yield a valid comparison between measurements using both the
Krypton hygrometers and the Gas analyzer instruments. Therefore, to detect sensible heat flux
differences over more stations I used the un-corrected sensible heat flux from the sonic
anemometer and assume the dry air constant of cp = 12 31 [J kg-1 K-1 kg m-3] to convert from
the kinematic heat flux [K m s-1] to the energy flux density [W m-2].
I can then calculate
downwind-upwind heat flux differences for all the NLAE stations downwind of the upwind
tower (NLAE 1) and all NCAR and ISU station differences from the upwind towers (NCAR 1
and ISU 1).
For the air temperature analysis in 2010, I compare the non-aspirated sensors
installed at a 9-m height, but in 2011 I compare the 10-m level NCAR sensors, which did have
forced ventilation. In 2011, stations ISU 1 and ISU 2 recorded temperature at an 8-m height, but
112
these measurements are omitted because of frequent periods of sensor error when ISU 2 reported
several degrees of difference from the reference ISU 1 station. Data taken during precipitation
events and from other periods with missing values from both the upwind or downwind stations
were eliminated. I also performed a quality control of the temperature profile measurements at 9
and 5 meters for NLAE 1-4 where we reject all events where |TNLAE 1-TNLAE 2 | > 5°C. In 2011
for the 10 m level at NCAR 1-4, the same filtering threshold of temperature difference is applied.
In 2010 the stations were installed for the period of 30 June to 7 September, whereas in 2011
measurements were collected from 30 June to 16 August. The 2010 season provided a ten-day
period from 26 July 700 LST to 5 August 1730 LST to measure crop fluxes within the wind farm
when the turbines were shut down for an upgrade in transmission. 10-minute averages of the
sonic heat fluxes are calculated from the raw 20-Hz data for ease of comparison to the SCADA
averages of wind speed and wind direction from the first three of the turbines in the ‘B-line’.
Temperature averages of 10-min intervals are also calculated from 5-min output of the NLAE
stations during CWEX-10 and from 1-Hz output of the NCAR stations in CWEX-11.
3. Analyses procedures
The differences of the sonic heat flux and air temperature between the stations north
(downwind) and south (upwind) of the ‘B’ turbine line are presented as follows: [NLAE 2NLAE 1; NLAE 3-NLAE 1; NLAE 4-NLAE 1 for CWEX-10 and NCAR 2-NCAR 1; NCAR 3NCAR 1; NCAR 4-NCAR 1; and additionally heat flux at ISU 2-ISU 1 in CWEX-11]. Note that
the comparison of heat flux between ISU 2- ISU 1 is for a location between two turbines and
there is high spatial variability of heat flux between two wakes instead of measuring within the
center of a turbine wake or measuring on the edge of one wake.
The differences of air
temperature and sonic heat flux are presented for typical daytime and nighttime conditions
according to two ‘null’ categories (no-turbine influence with westerly flow and flow with
turbines offline) and by specific wind directional composites (with a turbine influence) for
several tests of statistical significance of each difference.
3.1 Wind directional filtering for the flux differences composites
Each set of daytime or nighttime fluxes are separated according to the downwind flux station
wind direction at NLAE 2 for the 2010 measurements and at the NCAR 2 station in 2011.
113
‘Waked’ or ‘non-waked’ wind directions from the flux tower were determined by using an
assumption of a 5° expansion of wakes from the turbine rotor as reported by Barthelmie et al.
(2010) and adopted in the analysis of Rajewski et al. (2013a, 2013b). Table 1 presents an
interpretation of these wake-direction sectors for each wind turbine used in this analysis. A
description of the distance from the nearest downwind flux station to an individual turbine is also
included as a table category. The reader should refer to the CWEX introductory paper (Rajewski
et al. 2013a) for the other distances of the flux towers downwind of the B-turbine line. For this
paper wake indicators are presented for the first null case: “W” for westerly winds and test cases
#1-4” for the “WSW”, “SW”, “SSW”, and “S-SSE” in CWEX-10 and test cases “1-4” for the
“SW” “SSW”, “S”, and “SSE” directional bins in CWEX-11. The differences in wake direction
windows are a consequence of different station positions of NLAE 2 and NCAR 2 north of the
‘B’-turbine line. Analysis of the remaining directional sectors (“SE”, “ESE”, “E”, “ENE”, and
“NE to NW”) is omitted to avoid flow contamination of the upwind stations, NLAE 1 and
NCAR 1, caused by multiple wakes. These complications of flow are believed to originate from
(1) multiple turbines in the ‘B’ turbine lines for east south-east directions, (2) multiple lines of
turbines 1.5 km southeast of the ‘B turbine line’ for the southeasterly direction, or (3) the deepwind farm wake effect in the northerly direction when there is no reference tower outside of the
wind farm influence.
3.2 Diurnal filtering of the flux-differences composites
According to the procedures developed in Rajewski et al. (2013b), the 10-minute fluxes are
partitioned into daytime and nighttime periods with a net radiation metric.
I also separate the
day/night periods by stability classification (NEUTRAL/STABLE/UNSTABLE) from the earlier
analyses of downwind-upwind differences of wind speed, turbulence kinetic energy and other
variables reported in Rajewski et al. (2013a).
These periods indicate daytime (nighttime)
conditions with low cloudiness and between the morning/evening transitions of the surface layer
when we would expect rapid changes in the fluxes or air temperature. Like the crop energy flux
analyses of CWEX, the objective is to create ensembles of clearly daytime and nighttime periods
when fluxes are not changing rapidly due to known diurnal effects. There are at least a few
hundred 10-minute observations for these day and night classifications and therefore I separate
out the differences by the type of stability condition. Rather than document changes for all six
114
combinations of day/night with stable/neutral and unstable periods, I highlight two daytime
classifications for neutral and unstable stratification and one nighttime stable condition. These
stability conditions associate to the largest number of observations in presenting the station
differences in both pictorial and statistical analyses.
3.3 Turbine operation filter for the flux differences composites
The turbine SCADA data (nacelle wind speed, wind direction, temperature, and
instantaneous power) is used for each 10-min period to define an ON/OFF turbine flag for the
surface fluxes as demonstrated in Rajewski et al. (2013b).
Unlike choosing an arbitrary
procedure for filtering 10-min ON/OFF periods of the SCADA to 30-min flux averages, I
directly filter 10-minute periods when the first three turbines within the leading line (B1-B3)
were not producing power. Any periods in the composite ON or OFF analysis are omitted when
1 (2) turbines were operational and the other 2 (1) were/was offline. Turbines in the offline state
may yet cause flux perturbations because each turbine rotor, nacelle, and tower provides a small
cross sectional influence on the boundary layer flow.
Shadow tower effects are reported in
several coastal studies (Helmis 1995; Sanderse et al. 2011; Whale 1996).
Blade vortices
generated around the turbine, (e.g. Vermeer et al. 2003) albeit small, may influence the surface
flux measurements especially for northerly flow conditions. To provide a clean measure of
comparisons of the temperature and sonic heat flux differences among the null and test
conditions, I evaluate only those OFF periods for the same wind direction categories (WSW,
SW, SSW, and S-SSE for CWEX-10; W-WSW, SW,SSW, S, and SSE for CWEX-11) as those
used when the turbines are online. Additionally noted are several nighttime periods when there
is significant directional shear between the 80-m turbine-nacelle wind vane and the wind
direction reported at the surface stations. Unfortunately, there are more sheared than un-sheared
events. The nighttime composites include both situations of negligible directional shear between
the surface and the rotor layer depth and highly sheared cases with directional veering of at least
20-30° between the surface wind and the turbine rotor wind speeds.
3.4 Comparison metrics of the flux differences composites
The following analyses will test whether the downwind-upwind differences in air
temperature (T ) and sonic flux (H), (which universally we denote “F”) for the directional sum
115
( FON) and the individual directions (FWSW, FSW, FSSW, and FS-SSE, in 2010; FSW,
FSSW, FS, and FSSE, for 2011) are significant as compared to the sum of the energy flux
differences for the direction sum in the first null case ”W” (FW) and in the second null case
“OFF” (FOFF). The westerly composite, FW includes both ON and OFF conditions to
increase the sample size. In the daytime the number of westerly conditions is about a 2:1 ratio of
ON to OFF, whereas at night there are about an equal amount of cases when the turbines are
producing power or have offline status. The OFF composite (FOFF) includes the same wind
direction categories as the ON composite, FON. Table 1 describes these individual and
composite null and test cases with an explanation of the turbine wake indicators for each wind
direction, and the number of samples that are included in the statistical analyses.
3.5 Statistical analyses of the composite and individual flux differences
The mean differences and the standard deviations among each case are graphically depicted
to more clearly reveal turbine and non-turbine influenced conditions that support a conceptual
model of turbine impacts on temperature and heat flux.
The plots of the mean and standard
deviations of the difference also include difference accumulations between each composite
condition for the DAY-NEUTRAL, DAY-UNSTABLE and NIGHT-STABLE periods.
For
each flux difference, I present tables of the mean, standard deviation, and the 95% upper and
lower confidence bounds on the mean difference. The standard deviation is reported to give the
reader a scale of the measurement variability, whereas the standard error of the mean denotes
confidence intervals in determining the mean difference for each station difference composite.
Significant differences are identified when the upper and lower bounds of the mean for each
composite are outside the range of the upper and lower bounds of the null cases. A preliminary
test of statistical significance not presented in this paper compared the downwind-upwind flux
differences for each of the test cases to each of the two null cases as described in the method of
Rajewski et al. (2013b). However, the charts of the significance tests indicate high variability
and particular site-specific results that are difficult to summarize. Therefore, I compare the 95%
Confidence Intervals of the test cases for the turbines ON composite and the individual directions
to the 95% confidence intervals for the two null cases of turbines OFF and for westerly wind
direction. If the interval of the flux differences in both the test and the null cases are similar then
116
as a proxy I retain the null hypothesis of there being no significant difference of flux or
temperature between the test and null conditions.
3.6 The effect of turbine nacelle speed and refined wind direction
As indicated in Table 1, nearly 900 observations for the S-SSE nighttime-stable case of flow
between turbines B2 and B3 are available from CWEX-10. Specifically for this wind direction,
I relate the SCADA turbine speed from B2 to the temperature and heat flux differences from the
downwind-upwind NLAE stations. The turbine speeds are classified by 1 m s-1 increments with
a staggered ±0.5 ms-1 interval for each bin. Speeds range from 3.3 m s-1 to 12.3 m s-1 as shown
in Table 2, and I present the corresponding Monin Obukhov length (L) at the upwind tower for
reference on the effective size of the mixing related to the turbine-scale of turbulence suggested
by several studies (e.g Ainslee 1988) as 0.5 D to 1.0 D. In CWEX-11, the upwind and
downwind 80-m wind speed is available from the CU-NREL wind cubes WC 68 and WC 49.
Refined wind direction categories are developed at the surface to link the downwind-upwind
differences in heat flux and temperature at the NCAR and ISU flux stations to the wind cube
downwind-upwind wind speeds of the 40 m to 120 m rotor–layer heights. These bins are
determined in 10° partitions centered on each five degrees of wind direction from 120° to 270°
degrees. Overall there are sufficient samples in Table 3 to perform this partitioning of the flux
differences.
4. Results
4.1 Air temperature differences for the directional composites
4.1.1 DAY-NEUTRAL
Figs. 1a and 1b indicate the accumulations of the downwind-upwind station differences
of air temperature to the DAY-NEUTRAL condition for each null and test case condition in
CWEX-10 and CWEX-11. Also overlaid in the figures are the mean and standard deviations of
each difference between the NLAE downwind-upwind stations and the NCAR downwindupwind stations.
In both years, I observe the highest frequency of cooler temperatures reported
at the far-wake distance (14 to 17 D downstream from the B line of turbines). The largest
daytime cooling occurs for west-south westerly to south-westerly wind directions, which indicate
the edge of a turbine wake (from turbine B1) being over or near NLAE 3 or NLAE 4,
117
respectively. The downwind distance of at least 10 D seems relevant to the turbine wake
expansion reaching the surface in accordance with the Monin Obukhov length scales of hundreds
of meters that would occur for neutral flow conditions. Larger sized eddies in the boundary layer
appear to advect the turbine-waked air to the surface .This does not happen until the turbine has
had some degree of upward expansion above the turbine blade tip-top and the wake turbulence
interacts with the ambient scales of mixing.
Although the null cases report a similar range of variability, I indicate some degree of
confidence that a single turbine wake at may cool the surface at NLAE 3 by about 0.2 to 0.3 °C.
For the station at NLAE 4 there is mixing from the ‘A’ and ‘B’ lines of turbine wakes to
facilitate slightly cooler conditions (0.4 to 0.6 °C) as indicated in Table 4a. For the NCAR
stations, temperature differences among all directions convey negligible effects as the variability
is high among both turbine-wake and non-wake westerly/turbines off conditions. The weak
temperature differences (< -0.1 °C) reported in Table 4b for the NCAR data likely demonstrate
that field variability between the upwind station and the two northern downwind stations (NCAR
3 and NCAR 4) may also contribute to temperature differences. This agronomic factor was
discussed in reducing the crop energy and CO2 flux differences between the downwind and
upwind stations in Rajewski et al. (2013b).
4.1.2 DAY-UNSTABLE
In the unstable daytime pattern, station differences in temperature are less clear than for
neutral conditions (Fig 2a and 2b). For the NLAE flux towers, there is weak confidence that the
B1 turbine wake may be inducing cooling at NLAE 3 for south-south westerly flow.
Less
station difference in temperature is expected because atmospheric instability favors spatial
perturbations in temperature generated by surface heating. However, there are indications that a
looping pattern of the wake may be present since we do not observe the same degree of cooling
for this or any wind direction case at NLAE 4.
As demonstrated in neutral conditions for the
NCAR stations, there is no difference in the temperature for turbine-influenced and non-turbine
influenced conditions. Since surface scale mixing is more important in unstable vs. neutral flow
I project that the temperature differences due to field-variability produce a stronger influence
than to the perturbations attributed to wind turbines and large wind farms .
There is the
possibility of turbine-tower shadow effects as previously reported. Although a weak difference,
118
there is evidence of slight warming (by 0.1 °C) at the NLAE 2 station for unstable flow. The
drag of wind around the tower would also demonstrate less mixing and therefore slightly warmer
temperatures at the surface. However this warming effect is not seen at the NCAR 2 station
positioned about 1.0 D northward of the NLAE 2 placement, so this warming zone may be only
related to the pressure field recovery occurring at about 1-2 D downstream of the rotor (e.g.
Ainslee 1988). At a distance of 2-4 D downstream the turbine wake speed should gradually
recover as blade vortices within the wake and ambient mixing on the outside of the wake
replenish the momentum loss directly behind the rotor.
4.1.3 NIGHT-STABLE
For the stable nighttime conditions, the highest accumulation of warmer temperatures at
the downwind flux towers occurs for south and south-south easterly wind directions. There
appears to be an aggregate effect of two or more wakes to increase the surface warming at the
northernmost station at NLAE 4, whereas for the NCAR stations the highest accumulated
warming is observed at the near-turbine line location of NCAR 2. In both Figure 3a and Figure
3b the turbine wake interacts with the surface at a much shorter distance downstream from the
turbines as compared to daytime neutral/unstable flow.
In the confidence intervals of Table 6a I
detect close to 0.4 °C of warming at NLAE 2 for the SW direction with the edge of the B1 wake
turbulence over the station and around 0.2-0.3 °C for other wind directions that support the
turbine wake centerline being near the flux station. Warmer temperatures of 0.2-0.3 °C within
the turbine wake edge also occur at NCAR 2 for SSW and SSE winds with about a 0.1 °C
reduction in the temperature difference for SW and S wind directions for centerline wake effects
from turbine B1 and B2, respectively (Table 6b). In the SSE wind direction there is indication
of a similar temperature difference (0.3 °C) at NCAR 2, NCAR 3, and NCAR 4. This wind
direction category pattern plausibly demonstrates the right-side of the wake from turbine B2
impacting NCAR 2 and the left-side of the turbine B3 wake influencing NCAR 3 and NCAR 4.
For further downwind stations, NLAE 3 and NLAE 4, there is agreement in a larger temperature
difference for wind directions that indicate the edge of a turbine wake being over the flux station
as compared to wind directions where the centerline position of the wake moves over the flux
station. The highest nighttime temperature difference at NLAE 4 (above 0.4 °C) occurs from the
influence of two lines of turbine wakes as similarly observed in the daytime cooling at this
119
station.
NLAE 3 reports higher nighttime temperature (0.25 °C) in the SSE wind directions
unlike at NLAE 2 where the station is measuring in-between two turbine wakes..
4.2 Sonic anemometer heat flux differences for the directional composites
4.2.1 DAY-NEUTRAL
As reported in Rajewski et al (2013b), the daytime sensible flux station differences are
small and highly variable for most wind directions that indicate one or two turbines moving over
the region. The sharp contrast in station downwind-upwind differences between NCAR 3 and
NCAR 2 and NCAR 4 is related primarily to the field-scale variability. For the NLAE stations
there appears an aggregate lowering of the wake to the surface at NLAE 4 when there is the
double wake influence from the ‘B’ and ‘A’ line of turbines (Figure 4a). No such effects are
seen at NLAE 3 or NLAE 2. However, there is evidence in 2011 of a counter-gradient flux
difference at ISU 2 for WSW winds and this is maybe collocated with a farther distance
downstream in the B1 wake (6.3 D) as compared to a closer positioning of the NLAE 2 station
(5.4 D downstream of B1) as presented in Figure 4b.
The results reinforce the idea of the west-
facing side of the turbine wake as the downward branch of the wake vortex circulation. There is
consistency in the downward heat flux both in the means and in the confidence intervals of the
difference (Table 7 a, b),which are in agreement to previous isolated turbine studies (Högström
et al. 1988).
4.2.2 DAY-UNSTABLE
For the unstable daytime condition the near-wake positions of the flux differences are
highly sensitive to the distance from a particular turbine wake edge or the wake centerline
(Figure 5a,b). Heat flux compared to the upwind is 10-15 % higher at NLAE 2 and NLAE 3 and
about 20% higher at NLAE 4 for the west-southwesterly wind direction. I am unclear of why the
flux difference at NLAE 3 is higher than the < ±5 W m-2 reference null values. Wake meandering
may be an important factor as previously reported by (Ainslie 1988; Medici and Alfredson 2006,
2008), but I cannot support this aloft feature from the surface-based observations available
during CWEX-10.
For the SSW and S-SSE wind direction sensible heat fluxes are also 20%
higher at NLAE 4 as compared to the upwind station and the double line of turbine wakes would
facilitate this larger mixing of heat.
120
At NLAE 2, a counter-gradient flux occurs both in SW and SSE wind direction
categories. As mentioned in Rajewski (2013b) the clockwise rotation of the blades induces a
counter-clockwise rotation of the turbine wake vortex to maintain conservation of angular
momentum. The counter-gradient flux difference in the southwesterly case is attributed to the
station location near the left-side edge of the B2 wake, which is the downward branch of the
turbine vortex ring (Table 8a). However, the NCAR stations (Figure 5b and Table 8b) indicate
low confidence that the flux differences are significantly different from the two null westerly and
turbines offline conditions. The high degree of flux variability in Table 8b seems largely related
to a positive heat source coming from the surface, which noted in the energy flux study of
Rajewski et al. (2013b) is dependent on field-scale heterogeneities of soil and crop surfaces. In
agreement with Monin-Obukhov surface similarity theory (e.g. Stull 1988), I observe somewhat
significant differences in heat flux for unstable than for neutral conditions because the wake is
reaching the surface within a few D downstream of each turbine line. Wake meandering, is also
a relevant process as large scale eddies move the turbine wakes into deep looping patterns
similar to a dispersion pattern of a scalar concentration (e.g. Medici and Alfredson 2006, 2008;
Stull 1988).
4.2.3 NIGHT-STABLE
The nighttime stable case features the largest accumulated difference in heat flux at the
northernmost stations for southerly to south-south easterly flow.
In both the NLAE station
differences and in the NCAR station differences the near wake location depicts the most intense
downward heating of about (15-30 W m-2) in the southwesterly wind direction (Figs. 6a and 6b).
Smaller flux differences (5-10 W m-2) are noted for wind directions that approximate a centerline
position of the wake near the flux station.
The 95% confidence intervals of the mean difference
for each composite in Table 9a indicate favorable wake influence for several directions in the
near wake region at NLAE 2 and somewhat the far-wake location of NLAE 3 and the double line
wake influence at NLAE 4. For the south-southeast direction, there is agreement among the
magnitude of the downward flux station difference of 12 W m-2 reported at NLAE 3 and
NLAE 4.
Negligible flux differences occur at NLAE 3 for WSW flow but NLAE 4 measures
about the similar 12 W m-2 enhanced downward flux when the station is receiving a wake
influence from the A-line of turbines west of the station. This magnitude of the flux difference at
121
NLAE 4 is nearly identical to the NLAE 2 flux in the centerline position of the wake of turbine
B1 (about 5.4 D downstream of the turbine). The largest downward heat flux difference ( > |20
W m-2|) in the near-wake corresponds to the left-edge of the B1 wake vortex near the NLAE 2
flux tower as indicated for southwesterly winds.
Similar to the larger station difference between NLAE 2 and NLAE 1, the SW wind for
the ISU 2 station indicates a 10 W m-2 higher downward heating than the upwind ISU 1 station
and this magnitude of difference shifts to NCAR 2 for SSW wind when compared to the flux at
NCAR 1 (Table 9b).
In the centerline positions of the wake for each respective station of
NCAR 2 or ISU 2 the heat flux differences is reduced (less negative) by about 30-40% as
compared to the wake-edge cases.
However, for wind directions from the SSE, all four
downwind stations report a mean flux difference near 15 W m-2. I propose that the wake-edge
geometry over the stations explains for this consistent advection of heat to the surface. At ISU 2
and NCAR 3 the left edge of the B3 wake is near the stations and there is presumably a similar
position of the B4 wake at NCAR 4. At NCAR 2 however, we would expect a positive heat flux
on the right side of this wake vortex ring. On the contrary, the flux difference at the surface
remains negative.
Until sufficient high-resolution tower or RASS type profiler measurements
are made of the thermal structure of wakes I explain this downward transport of heat by the
effect of the above-turbine layer buoyant consumption of upward-moving heat eddies.
Temperature stratification quenches vertical motion directly above the turbine blade top height.
This pattern is indicated in the return-to-ambient vertical velocity variances immediately above
the rotor top height in LiDAR measurements during CWEX-11 (D. A. Rajewski, unpublished,
2012). It seems conceptually feasible that a warmer temperature gradient above the turbine
depth of the boundary layer retards vertical mixing above the turbines, whereas the turbine wake
may sufficiently mix down to the surface when the surface stratification is not too strong (L ~ 0.5
to 1.0 D).
4.3 Turbine speed refinement for the S-SSE NIGHT-STABLE conditions of CWEX-10
In the NLAE station difference for the S-SSE nighttime stable case in Figure 7a, there is a
clear pattern that increasing the turbine speed generates the highest warming (more than 1.0 °C)
at NLAE 4. The nearly linear increase in temperature with nacelle speed is also evident at
NLAE 3 for nacelle speeds lower than 8 m s-1. At NLAE 2, there little difference to the upwind
122
station temperature and turbine forcing on the surface is expected to be weak for flow that is
between the wakes of turbines B2 and B3. The warming at NLAE 4 follows a near-linear
relationship to the nacelle wind speed; however, I cannot compare this data to previous field
experiments, which related thermal or aerodynamic forcing to the turbine thrust coefficient. The
intensity of the warming is linked to the pattern of the surface over-speeding caused by the
turbines as shown in Figure 7b. At slow turbine speeds the wake is highly variable and has
stronger expansion to the surface, whereas when the turbine speeds approach rated power
capacity, the lower portion of the wake region appears confined to a restricted height within a
few meters below the bottom of the rotor.
An examination of the heat flux differences over this same region (Figure 7c) reveals that the
magnitude of downward flux difference does not follow the increase in wind speed but rather has
a maximum value (10 to 15 W m -2) at all three stations for nacelle speeds of 8 m s-1. The nonlinear relationship with nacelle speed relates to the greatest turbulence generation from a single
turbine at wind speeds that are slightly lower than the rated speed (13 m s-1) and at a wind speed
in which high thrust and power coefficients produce the largest drag and turbulence generated by
the rotor (e.g. Adams 2007; Baidya Roy et al. 2011). Some reduction in speed is expected in the
nacelle measurement as the wind passes through the rotor. The SCADA data from this site
demonstrates cases where the nacelle speed is around 1.7 m s-1 and positive power is still
recorded. Therefore, I surmise that for a turbine cut-in speed of 3.5 m s-1 for these GE 1.5 MW
SLE turbines a 2 to 2.5 m s-1 speed reduction from the ambient flow to the nacelle position is
plausible. The maximum effect of normalized difference in surface turbulent kinetic energy
(TKE) for a nacelle speed of 8 m s-1 is also confirmed in Figure 7d. Turbulent enhancement at
the surface remains low at NLAE 2 since we do not expect a wake influence whereas at the
northern two sites the wake increases surface turbulence at NLAE 3 and NLAE 4 by over a
factor of 5 times the ambient scales of mixing.
4.4 Wind direction refinement for the NIGHT-STABLE conditions of CWEX-11
In 2011 upwind and downwind profiles of wind speed are evaluated from the CU NREL
LiDARs (WC 68 and WC 49) to determine wind speed characteristics both in the rotor disk and
aloft. In Figure 8a, I emphasize that the effects of turbines at the surface are often not correlated
with height. The SCADA data and wind cube data both confirm several nighttime periods with
123
15-30 ° directional differences between the 10-m surface and the 80-m hub height. For multiple
10° bins in the southerly sector, I observe the same range of over-speeding (10-20%) at both the
40-m level and at the NCAR 2 or ISU 2 stations. Velocity deficits within the rotor height reach a
maximum when the centerline position of the B3 turbine wake is overhead of WC 49 at a
distance of 3.5 D north of the turbine. Conversely, for oblique wind direction sectors from the
WSW there is slight enhancement (negative velocity deficit) aloft in the rotor layer and about the
same magnitude of speed reduction (10-15%) at the surface.
WNW flow at the hub height level.
This case may represent slight
For the SE directional bins a weaker velocity deficit is
noted within the rotor and also a small to negligible over-speeding at the surface. A plausible
conjecture for the combination of wakes from the B-line of turbines is that the intensity of the
momentum exchange with the surface is weakened at the outer edge of the wake.
I next compare the turbine-added surface turbulence intensity, Iadded between downwind flux
stations where Iadded=(I2- I20 )0.5) and I0 is the ambient turbulent intensity according to the
methods of Frandsen et al. (1996) and adopted in Barthelmie et al. 2003, 2007). Figure 8b
indicates up to a 6% increase in the turbulence intensity at the NCAR 2 and ISU 2 towers
whereas the expansion of the wake to the 9 D and 14 D locations downwind of the turbine
illustrate that the turbulence decreases downwind of the turbine monotonically (only a few
percent higher of I0). In the 10-m temperature difference (Figure 8c) there is a steady pattern of
warming of about 0.2 to 0.4 °C at NCAR 2 for wind directions in the southerly sector whereas a
slight cooling pattern occurs for more oblique flow angels from either the WSW or the SE. The
NCAR 3 and NCAR 4 stations are more variable based on agronomic factors previously
mentioned. However, in the heat flux pattern of Figure 8d, I observe some congruency with the
larger directional categories that were earlier presented. Downward heat flux is increased at the
two near wake stations, NCAR 2 and ISU 2, for wind directions that indicate the edge of a
turbine wake being over the flux station. Particularly for the 160° bin, there is convincing
evidence that the edges of turbine wakes from B3 and B4 are yielding similar heat flux
differences (15-20 W m-2) at all the NCAR and ISU downwind stations. Downwind stations
NCAR 3 and NCAR 4 report a smaller flux difference (<10 W m-2) when the wakes from B1 or
B2 turbine have sufficiently expanded out to 9 D and 13 D downwind of the B-turbine line,
respectively.
For increasing oblique angles of wind the additional interaction of multiple
turbines within the B-line indicate low overall difference but higher variability. The weakening
124
of wake impacts on flow and scalars requires additional foresight in future experimentation in the
middle or downwind edge of the wind farm.
5. Discussion
This investigation of surface flux station data from CWEX-10/11 reveals some essential
features of prior field and conceptual wind tunnel and numerical experiments.
The
measurements reported here corroborate with the patterns of temperature and heat flux.
However, additional details in the structure of cooling and warming within the individual, double
wake, or multiple-wake regions have not been previously indicated or are only recently receiving
attention in numerical and wind tunnel simulations (e.g. Zhang et al. 2013).
Daytime
temperatures are reduced by less than 0.5 °C within a single wake at a downstream distance of at
least 10 D, whereas for a convective boundary layer, buoyant-generated turbulence can displace
the wake to the surface at a much shorter distance downstream of each turbine tower. In
conditions of wake meandering I expect that the location of the turbine-induced cooling may not
line up over a projected plume-like motion coming from each turbine. The observations show
weak agreement of warmer temperatures within the tower shadow (a distance of less than 2 D
downwind). The extent of this zone of slower speed air and reduced mixing is indicated by the
recovery of the perturbation pressure field around each turbine (e.g. Ainslee 1988).
For a situation with two completely aligned vortex rings from two turbine wakes from two
different turbine lines the CWEX measurements suggest that there is likely an enhancement in
the turbulence and fluxes which follow the cross-wind rotation of the wake.
Daytime upward
fluxes of heat occur on the right side of the wake vortex and corresponding downward fluxes of
heat are reported on the left side of the turbine vortex; Zhang et al. 2013 previously demonstrated
the counter-gradient heat flu differences in a wind tunnel wind farm configuration.
The
difference in the daytime heat flux at a location impacted by one turbine wake is less than 10 W
m-2, whereas if two aligned wakes are influencing the surface there is an approximate doubling
of the flux. Zhang et al. (2013) also simulated in a wind tunnel a staggered wind farm array and
determined that there was not a counter-gradient flux of heat for convective boundary layer
simulations. The CWEX results potentially indicate a smoothing out of the ‘wake’ turbulence as
more and more wakes intersect and undergo lateral merging. There is a general decrease in the
125
daytime cooling and positive heat flux at the downwind stations when the surface is influenced
by several turbine wakes from multiple turbine lines.
At night turbine wakes warm temperatures by <0.5 °C at a distance of less than 10 D
downwind of the leading turbine line and this result corresponds to the intensities reported from
isolated field studies and numerical studies (e.g. Baidya Roy and Traiteur 2010; Baidya Roy et
al. 2004, 2011).
Many of the numerical and wind tunnel studies (e.g. Cal et al. 2010;
Chamorro and Porté-Agel 2010; Calaf et al. 2011, Lu and Porté-Agel, 2011) describe a
normalized temperature change with respect to the upwind profile of temperature.
Unfortunately, without a measured profile there cannot be a clear comparison of the temperature
at the surface. The aerial thermal imagery studies of Zhou et al. (2012a, 2012b) and WalshThomas et al. (2013) suggest that ground temperature measurements may overestimate the
degree of nighttime warming occurring within the leading line of turbines within a wind farm.
Locations within one turbine wake may see 0.2 to 0.4 °C of warming depending on the exact
position of the wake (centerline vs. edge) as compared to the flux tower location. The turbine
wake influence from two distinct turbine lines nearly doubles the nighttime warming (variability
between 0.2-1.2 °C) and the increase in hub-height wind speed facilitates a stronger transfer of
warm air aloft to the surface. It is yet unknown if this magnitude of warming continues deeper
into the wind farm.
From the measurements taken at this southeast edge of the 200-turbine wind farm, I alluded
caution on the role of surface warming from multiple lines of turbines.
Perhaps for cropland
within the downwind side of the wind farm and for land parcels a few km outside of this
boundary the surface warming is induced by a full change in the vertical profile of temperature
extending several hundred meters above the turbine rotor height. This fully developed turbinewake boundary layer of a presumed quasi equilibrium with the surface layer, the turbine layer,
and the boundary layer above the wind farm has been proposed in recent LES studies (e.g. Calaf
et al. 2011; Meneveau 2012, Meyers and Meneveau 2012). Instead, near the leading two to three
lines of turbines within a wind farm the CWEX measurements propose that
the temperature profile is modified by turbine wakes only at heights within the rotor layer and
sometimes down to the surface. More measurements in the study area are necessary and planned
to reinforce this claim.
126
Among nighttime heat flux differences conceptual experiments simulate the same mean
magnitude of flux as CWEX measurements. As for the concern of temperature and deep-wind
farm wake effects, there is similar justification for the heat flux. In a situation of one turbine
wake influencing the surface there is about a 5-10 W m-2 enhancement in downward flux when
the wake centerline is near the flux station, whereas 10-25 W m-2 difference in flux is possible at
the surface when the edge of the wake is near the station or when two aligned turbine wake edges
impact the surface. The results have preliminarily suggested a reduction in the heat transfer for
southeasterly flow when multiple lines of turbine wakes are influencing the surface. Fine scale
differences in flux appear related to wake geometry and complex collisions of multiple wakes
expanding by 5° from the rotor. The small spatial variations in wake turbulence are difficult to
simulate especially when numerical applications have sought to improve characterization of the
wake aerodynamics and power constraints for operational scale wind farms.
6. Conclusions
As continuing evidence for the impact of wind turbines and wind farm modification of crop
fluxes of momentum, heat, H2O, and CO2, this study expands analysis of air temperature and
sonic heat flux differences over several surface stations during CWEX-10/11. The careful
filtering and sorting metrics of the 10-minute observations allow for comparison to several recent
simulations of wind farm impacts on scalar transports. In the daytime (nighttime), an isolated
turbine wake may cool (warm) the surface by less than 0.5 °C and this effect can be somewhat
intensified for the interaction with two aligned wakes from consecutive turbine lines or be
mitigated when multiple wakes have merged. Differences in heat flux also follow closely to the
turbine wake-edge or centerline position over a flux tower. Daytime differences in temperature
and heat flux are negligible near the turbine line for most wind directions whereas at about a
distance of greater than 10 D downstream of a turbine the wake may interact with the surface
during conditions of neutrally thermal stratification. Isolated wakes may slightly cool the surface
at these same distances during conditions of unstable stratification, but the effects are quite
variable and sensitive to directional meandering of the wake.
The CWEX measurements
demonstrate from the upwind viewing of a turbine an upward flux on the right facing side of the
vortex circulation and a corresponding downward flux on the left facing side. The aggregate
effect of two wakes may conversely improve upward transport of heat at distances of 20-40 D
127
downstream of the leading turbine line. In the nighttime, the heat flux differences are more
sensitive to the flux tower positioning near the centerline position or right or left facing edge of
the wake. Stable stratification promotes less lateral expansion of the wake and therefore higher
downward flux differences (within less than 5 D of a turbine) are detected than in previous
numerical and wind tunnel simulations. There is plausible evidence for multiple wakes reducing
the scalar transport as in a large wind farm array and future measurements conducted at the
CWEX study site will more specifically address the common features of aerodynamics and
scalar flux perturbations depicted in deep-wind farm arrays.
Acknowledgments
This work was supported in part by the National Renewable Energy Laboratory under Professor
Lundquist’s Joint Appointment. NREL is a national laboratory of the U.S. Department of
Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for
Sustainable Energy, LLC. Partial funding for CWEX-10 was provided by the Ames Laboratory
(DOE) and the Center for Global and Regional Environmental Research at the University of
Iowa. Surface flux stations for CWEX-11 were provided by NCAR Earth Observing Laboratory
under an instrumentation deployment. Undergraduate student participation was supplemented by
funding from an NSF REU program under grant 1063048. Data analysis was supported in part
by the National Science Foundation under the State of Iowa EPSCoR Grant 1101284. SCADA
data for this analysis was provided by the owner of the meteorological data from the wind farm.
7. References
Adams M, Keith D (2007) A wind farm parameterization for WRF. 8th WRF Users Workshop,
June 2007, Boulder, CO. (Available online at
http://www.mmm.ucar.edu/wrf/users/workshops/WS2007/abstracts/5-5_Adams.pdf)
Ainslie JF (1988) Calculating the flowfield in the wake of wind turbines. J. Wind Eng. and
Indust. Aerodyn. 27:213–224.
Baidya Roy, S, Pacala SW, and Walko RL (2004) Can large wind farms affect local
meteorology? J. Geophys. Res., 109, D19 101, doi: 10.1029/2004JD004763.
-----, Traiteur JJ (2010) Impacts of wind farms on surface air temperatures. Proc. Natl Acad. of
Sci. 107:17899–17904. doi: 10.1073/pnas.1000493107
128
-----,(2011) Simulating impacts of wind farms on local hydrometeorology. J. Wind Eng. and
Indust. Aerodyn. 99:491–498. doi: 10.1016/j.jweia.2010.12.013
-----, Folkerts L, Ormel FT, et al. (2003) Offshore wind turbine wakes measured by SODAR. J.
Atmos. and Ocean. Tech. 20:466–477.
-----, Frandsen ST, Nielsen MN, et al. (2007) Modelling and measurements of power losses and
turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm. Wind
Energy 10:517–528. doi: 10.1002/we.238.
-----, Pryor SC, Frandsen ST, et al. (2010) Quantifying the Impact of Wind Turbine Wakes on
Power Output at Offshore Wind Farms. J. Atmos. Oceanic. Tech. 27:1302–1317. doi:
10.1175/2010JTECHA1398.1.
Cal RB, Lebrón J, Castillo L, et al. (2010) Experimental study of the horizontally averaged flow
structure in a model wind-turbine array boundary layer. J. of Renew. and Sustain. Energy
2:013106. doi: 10.1063/1.3289735.
Calaf M, Parlange MB, Meneveau C (2011) Large eddy simulation study of scalar transport in
fully developed wind-turbine array boundary layers. Phys. of Fluids 23:126603. doi:
10.1063/1.3663376.
Chamorro LP, Porté-Agel F (2010) Effects of Thermal Stability and Incoming Boundary-Layer
Flow Characteristics on Wind-Turbine Wakes: A Wind-Tunnel Study. Bound.-Layer
Meteorol. 136:515–533. doi: 10.1007/s10546-010-9512-1.
Frandsen F, Chacón, L Crespo, A, Eenvoldsen, P, Goméz-Elvira, R, Hernández, J, Højstrup, J.,
Manuel, F, Thomsen, K., and Sorensen, P, (1996). Final report on measurements on and
modeling of offshore wind farms. Riso National Laboratory document Riso-R-903-(EN),
100pp.
Helmis CG, Papadopoulos KH, Asimakopoulos DN, et al. (1995) An experimental study of the
near-wake structure of a wind turbine operating over complex terrain. Solar Energy 54:413–
428.
Henschen, M, Demchak, K, Herrholtz, B, Rudkin, M, Rhudy, L, Larson, E, Doogs, B, Holland,
J., Martin J (2011). Do wind turbines affect weather conditions? A case study in Indiana.
Journal of Purdue Undergraduate Research, 1, 22 – 29. doi: 10.5703/jpur.01.1.4
Högström, U, Asimakopoulos DN, Kambezidis H,. Helmis, CG, and Smedman, A (1988) A field
study of the wake behind a 2 MW wind turbine. Atmos. Environ., 22, 803–820.
http://dx.doi.org/10.1016/0004-6981(88)90020-0.
129
Kambezidis HD, Asimakopoulos DN, Helmis CG (1990) Wake measurements behind a
horizontal-axis 50 kW wind turbine. Solar & wind technology 7:177–184.
Kelley ND (1989) An initial look at the dynamics of the microscale flow field within a large
wind farm in response to variations in the natural inflow. Wind Power ’89 Conference and
Exposition publication, San Francisco, California, 24-27 September 1989. Solar Energy
Research Institute publication SERI/TP-257-3591.
Larsen GC (2007) Dynamic wake meandering modeling. Risø National Laboratory, Roskilde
Lu H, Porté-Agel F (2011) Large-eddy simulation of a very large wind farm in a stable
atmospheric boundary layer. Physics of Fluids 23:065101. doi: 10.1063/1.3589857
Medici D, Alfredsson PH (2006) Measurements on a wind turbine wake: 3D effects and bluff
body vortex shedding. Wind Energy 9:219–236. doi: 10.1002/we.156
-----, (2008) Measurements behind model wind turbines: further evidence of wake meandering.
Wind Energy 11:211–217. doi: 10.1002/we.247
Meneveau C (2012) The top-down model of wind farm boundary layers and its applications.
Journal Turb. 13:N7. doi: 10.1080/14685248.2012.663092
Meyers J, Meneveau C (2012) Optimal turbine spacing in fully developed wind farm boundary
layers. Wind Energy 15:305–317. doi: 10.1002/we.469
Papadopoulos KH, Helmis CG, Soilemes AT, et al. (1995) Study of the turbulent characteristics
of the near-wake field of a medium-sized wind turbine operating in high wind conditions.
Solar energy 55:61–72.
Rajewski DA, Takle ES, Lundquist JK, Oncley SP, Prueger JH, Horst TW, Rhodes ME, Pfeiffer
R, Hatfield JL, Spoth KK, Doorenbos RK (2013). CWEX: Crop/Wind Energy Experiment:
observations of surface-layer, boundary-layer and mesoscale interactions with a wind farm
Bull. Am. Meteorol. Soc. In press (doi:10.1175/BAMS-D-11-00240).
-----,(2013b) Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm. To be
submitted to Agric. and Forest Meteorol.
Stull, R., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers,
666 pp.
-----, Pijl SP, Koren B (2011) Review of computational fluid dynamics for wind turbine wake
aerodynamics. Wind Energy 14:799–819. doi: 10.1002/we.458
U.S. DOE Office of Energy Efficiency and Renewable Energy (2008): 20% Wind Energy by
2030: Increasing Wind Energy's Contribution to U.S. Electricity Supply, 248 pp.; NREL
130
Report No. TP-500-41869; DOE/GO-102008-2567. (Available online at
http://www.nrel.gov/docs/fy08osti/41869.pdf).
Vermeer LJ, Sørensen JN, Crespo A (2003) Wind turbine wake aerodynamics. Prog. Aero.
Scien. 39:467–510. doi: 10.1016/S0376-0421(03)00078-2
Walsh-Thomas JM, Cervone G, Agouris P, Manca G (2012) Further evidence of impacts of
large-scale wind farms on land surface temperature. Renewable and Sustainable Energy
Reviews 16:6432–6437. doi: 10.1016/j.rser.2012.07.004
Whale J, Papadopoulos KH, Anderson CG, et al. (1996) A study of the near wake structure of a
wind turbine comparing measurements from laboratory and full-scale experiments. Solar
energy 56:621–633.
Zhang W, Markfort CD, Porté-Agel F (2012a) Wind-Turbine Wakes in a Convective Boundary
Layer: A Wind-Tunnel Study. Bound.-Layer Meteorol. 146:161–179. doi: 10.1007/s10546012-9751-4
-----,(2013) Experimental study of the impact of large-scale wind farms on land–atmosphere
exchanges. Environ. Res. Letters 8:015002. doi: 10.1088/1748-9326/8/1/015002.
Zhou L, Tian Y, Baidya Roy S, et al. (2012a) Impacts of wind farms on land surface
temperature. Nature Clim. Change. doi: 10.1038/nclimate1505.
-----, Tian Y, Baidya Roy S, et al. (2012b) Diurnal and seasonal variations of wind farm impacts
on land surface temperature over western Texas. Clim. Dyn. doi: 10.1007/s00382-012-1485y.
131
Figure 1. Wind direction-sector accumulated downwind-upwind differences in air
temperature for the DAY-NEUTRAL case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in temperature are overlaid for each directional composite category and distance
from an individual turbine.
132
Figure 2. Wind direction-sector accumulated downwind-upwind differences in air
temperature for the DAY-UNSTABLE case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in temperature are overlaid for each directional composite category and distance
from an individual turbine.
133
Figure 3. Wind direction-sector accumulated downwind-upwind differences in air
temperature for the NIGHT-STABLE case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in temperature are overlaid for each directional composite category and distance
from an individual turbine.
134
Figure 4. Wind direction-sector accumulated downwind-upwind differences in sensible
heat flux (H) for the DAY-NEUTRAL case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in H are overlaid for each directional composite category and distance from an
individual turbine.
135
Figure 5. Wind direction-sector accumulated downwind-upwind differences in sensible
heat flux (H) for the DAY-UNSTABLE case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in H are overlaid for each directional composite category and distance from an
individual turbine.
136
Figure 6. Wind direction-sector accumulated downwind-upwind differences in sensible
heat flux (H) for the NIGHT-STABLE case for the (a) NLAE flux stations and (b) NCAR
flux stations. The downwind-upwind mean difference and standard deviations of the
difference in H are overlaid for each directional composite category and distance from an
individual turbine.
137
Figure 7. S-SSE NIGHT-STABLE case of the NLAE downwind-upwind mean differences
and standard deviations of differences in (a) 9-m air temperature , (b) normalized wind
speed with respect to the nacelle speed, (c) sonic heat flux, and (d) normalized TKE with
respect to the upwind flux tower TKE (TKE0) according to the variability of the nacelle
speed. Nacelle speed bins are centered on the ±0.5 m s-1 mark of integer category from 4 to
12 m s-1.
138
Figure 8. NIGHT-STABLE case illustrating the variability of mean differences and
standard deviations of differences according to 10-m wind direction at the NCAR 2 station.
Directional bins are centered on the ±5° interval for each 10° bin. In (a) DownwindUpwind differences of the wind cube data (WC 49-WC 68) at 40m, 80, and 120m are
overlaid on to the NCAR downwind-upwind differences and the ISU downwind-upwind
differences. In (b) turbine-added turbulence intensity is determined among all downwind
stations from the NCAR and ISU stations. In (c) downwind-upwind NCAR station
differences of 10-m air temperature. In (d) downwind-upwind NCAR and ISU station
differences of 6.5-m sonic heat flux. Blue X’s denote the centerline position of the B1, B2,
and B3 turbine wakes at the NCAR 2 station for wind directions near 225°, 180°, and 135°
respectively.
139
Table 1. Wind direction sectors corresponding to the turbine wake or gap (between turbine) flow for the B1 to B3 turbines on
the leading line of turbines at the wind farm for (a) NLAE station differences and for (b) NCAR station differences.
Composites of these direction sectors are included for when the turbines were operational or offline. The number of
observations, (N) in each of the DAY-NEUTRAL, DAY-UNSTABLE and NIGHT-STABLE conditions is included.
a)
Turbine wake
category
Indicator
W
OFF
ON
WSW
SW
SSW
S-SSE
SE
(Westerly no-wake, turbines on and off )
(combination turbines offline)
(combination turbines operating)
B1
gap between B1 and B2
B2
gap between B2 and B3
B3-B4, C line of turbines
Case direction
Turbine wake
Sample size (N)
DAYNEUTRAL
32
88
715
51
60
174
350
80
Sample size (N)
DAYUNSTABLE
38
183
260
17
7
51
151
35
Sample size (N)
NIGHTSTABLE
138
265
1327
83
40
106
889
209
Sample size (N)
DAYNEUTRAL
17
29
508
117
68
199
96
28
Sample size (N)
DAYUNSTABLE
54
182
103
9
6
42
35
11
Sample size (N)
NIGHTSTABLE
144
112
659
59
137
229
177
57
139
Case direction
b)
category
Indicator
W-WSW
OFF
ON
SW
SSW
S
SSE
SE
(Westerly no-wake, turbines on and off )
(combination turbines offline)
(combination turbines operating)
B1
gap between B1 and B2
B2
gap between B2 and B3
B3-B4, C line of turbines
140
Table 2. Nacelle speed categories and upwind flux tower Monin Obukhov lengths (L) of
for the S-SSE NIGHT-STABLE condition to determine relationships with surface flux
differences with turbine speed. Sample sizes are also provided for each category. Nacelle
speed bins are centered on the ±0.5 m s-1 mark of each integer category from 4 to 12 m s-1.
Nacelle speed
category (± 0.5 m/s)
NCAR 1
Monin-Obukhov length
(m)
4
5
6
7
8
9
10
11
12
5.3
5.1
8.4
11.2
19.8
34.8
45.5
36.7
89.8
Sample size
(N)
NIGHT
19
49
109
104
174
195
168
43
26
141
Table 3. 10-m wind direction sectors at the NCAR 2 flux station corresponding to the
turbine wake centerline or edge position (on periphery of wake) for the B1 to B3 turbines
on the leading line of turbines at the wind farm for the NIGHT-STABLE case.
Directional bins are centered on the ±5° interval for each 10° bin composite. The number
of observations, (N) is included for each direction sector.
NCAR 2 10-m wind
direction
category (±5 °)
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
Turbine wake
Indicator at NCAR 2 station
Right edge of B3, C line of turbines
Centerline of B3, C line of turbines
Centerline of B3, C line of turbines
Left edge of B3
Right edge of B2
Centerline of B2
Left edge of B2
Right edge of B1
Centerline of B1 at 225°
Centerline of B1 at 225°
Left edge of B1
West
Sample size
(N)
NIGHT
41
21
40
58
129
102
82
57
101
37
18
14
29
57
51
27
142
Table 4. Means and the 95% upper and lower confidence interval of the mean differences in air temperature of the
downwind-upwind difference distributions for NIGHT-STABLE cases among the (a) NLAE flux tower stations and (b)
NCAR flux tower stations. Significant differences are indicated in light red highlight when the confidence interval of
the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
DAY-NEUTRAL
NLAE 2-NLAE 1
NLAE 3-NLAE 1
NLAE 4-NLAE 1
T
Upper
Lower
T
Upper
Lower
T
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW
-0.03
0.00
-0.07
-0.03
0.04
TOFF
0.03
0.05
0.01
-0.13
TON
0.05
0.06
0.04
TWSW
0.00
0.02
TSW
0.03
TSSW
0.06
TS-SSE
0.07
(°C)
Upper
95%
CI
Lower
95%
CI
-0.09
-0.15
-0.06
-0.23
-0.10
-0.16
-0.13
-0.09
-0.18
-0.16
-0.15
-0.17
-0.05
-0.03
-0.07
0.03
-0.22
-0.18
-0.27
-0.27
-0.21
-0.32
0.03
-0.02
-0.21
-0.18
-0.24
-0.22
-0.19
-0.25
0.07
0.04
-0.20
-0.18
-0.22
-0.09
-0.07
-0.12
0.08
0.05
-0.13
-0.11
-0.14
0.05
0.08
0.01
142
Wind direction
143
Table 4. (continued)
b)
DAY-NEUTRAL
NCAR 2-NCAR 1
NCAR 3-NCAR 1
NCAR 4-NCAR 1
Wind direction
T
Upper
Lower
T
Upper
Lower
T
Upper
Lower
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW-WSW
-0.09
0.02
-0.20
-0.07
0.02
-0.15
-0.21
-0.11
-0.30
TOFF
-0.21
-0.09
-0.33
-0.20
-0.07
-0.33
-0.26
-0.12
-0.40
TON
-0.19
-0.18
-0.21
-0.21
-0.20
-0.23
-0.31
-0.30
-0.33
TSW
-0.30
-0.27
-0.32
-0.25
-0.22
-0.27
-0.41
-0.38
-0.44
TSSW
-0.20
-0.16
-0.20
-0.24
-0.20
-0.28
-0.31
-0.28
-0.35
TS
-0.17
-0.15
-0.19
-0.21
-0.19
-0.24
-0.30
-0.27
-0.32
TSSE
-0.13
-0.09
-0.16
-0.14
-0.11
-0.18
-0.22
-0.18
-0.25
143
144
Table 5. Means and the 95% upper and lower confidence interval of the mean differences in air temperature of the
downwind-upwind difference distributions for DAY-UNSTABLE cases among the (a) NLAE flux tower stations and (b)
NCAR flux tower stations. Significant differences are indicated in light red highlight when the confidence interval of
the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
DAY-UNSTABLE
NLAE 2-NLAE 1
NLAE 3-NLAE 1
NLAE 4-NLAE 1
T
Upper
Lower
T
Upper
Lower
T
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW
0.07
0.00
-0.10
-0.21
-0.14
TOFF
-0.02
0.01
-0.04
-0.18
TON
0.07
0.09
0.05
TWSW
-0.01
0.05
-0.08
TSW
0.05
0.15
TSSW
0.12
TS-SSE
0.06
(°C)
Upper
95%
CI
Lower
95%
CI
-0.28
-0.27
-0.21
-0.34
-0.15
-0.21
-0.18
-0.14
-0.21
-0.20
-0.18
-0.22
-0.08
-0.05
-0.11
-0.25
-0.21
-0.30
-0.21
-0.15
-0.28
-0.05
-0.21
-0.13
-0.28
-0.16
0.00
-0.32
0.17
0.08
-0.31
-0.27
-0.35
-0.14
-0.07
-0.21
0.09
0.04
-0.16
-0.14
-0.19
-0.01
0.02
-0.05
144
Wind direction
145
Table 5. (continued)
b)
DAY-UNSTABLE
NCAR 2-NCAR 1
NCAR 3-NCAR 1
NCAR 4-NCAR 1
Wind direction
T
Upper
Lower
T
Upper
Lower
T
Upper
Lower
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW-WSW
-0.04
0.01
-0.10
-0.04
0.03
-0.11
-0.19
-0.13
-0.26
TOFF
-0.19
-0.14
-0.24
-0.19
-0.14
-0.25
-0.26
-0.20
-0.32
TON
-0.11
-0.08
-0.14
-0.17
-0.13
-0.20
-0.24
-0.21
-0.28
TSW
-0.05
0.01
-0.11
-0.15
-0.09
-0.21
-0.15
-0.09
-0.22
TSSW
-0.10
-0.02
-0.18
-0.15
-0.02
-0.28
-0.18
-0.01
-0.36
TS
-0.18
-0.12
-0.24
-0.21
-0.14
-0.27
-0.30
-0.23
-0.37
TSSE
-0.10
-0.06
-0.13
-0.14
-0.11
-0.17
-0.21
-0.17
-0.25
145
146
Table 6. Means and the 95% upper and lower confidence interval of the mean differences in air temperature of the
downwind-upwind difference distributions for NIGHT-STABLE cases among the (a) NLAE flux tower stations and (b)
NCAR flux tower stations. Significant differences are indicated in light red highlight when the confidence interval of
the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
NIGHT-STABLE
NLAE 2-NLAE 1
NLAE 3-NLAE 1
NLAE 4-NLAE 1
T
Upper
Lower
T
Upper
Lower
T
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW
0.06
0.10
0.01
0.06
0.12
TOFF
0.03
0.05
0.02
0.03
TON
0.13
0.15
0.12
TWSW
0.21
0.27
TSW
0.40
TSSW
TS-SSE
(°C)
Upper
95%
CI
Lower
95%
CI
0.00
-0.02
0.06
-0.09
0.06
0.00
-0.14
-0.10
-0.18
0.23
0.25
0.22
0.27
0.30
0.24
0.16
0.07
0.14
0.00
-0.01
0.07
-0.10
0.47
0.32
0.21
0.29
0.13
0.18
0.29
0.07
0.25
0.31
0.20
0.09
0.13
0.05
0.00
0.06
-0.06
0.10
0.12
0.09
0.25
0.27
0.24
0.41
0.44
0.38
146
Wind direction
147
Table 6. (continued)
b)
NIGHT-STABLE
NCAR 2-NCAR 1
NCAR 3-NCAR 1
NCAR 4-NCAR 1
Wind direction
T
Upper
Lower
T
Upper
Lower
T
Upper
Lower
category
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
(°C)
95% CI
95% CI
TW-WSW
-0.10
-0.06
-0.15
0.06
0.12
0.01
-0.24
-0.20
-0.29
TOFF
-0.12
-0.06
-0.17
0.01
0.07
-0.04
-0.04
0.01
-0.10
TON
0.16
0.18
0.13
0.15
0.18
0.13
0.12
0.14
0.10
TSW
0.05
0.14
-0.04
0.14
0.21
0.08
0.02
0.09
-0.04
TSSW
0.23
0.27
0.20
0.09
0.12
0.06
0.13
0.16
0.09
TS
0.14
0.17
0.11
0.11
0.14
0.09
0.11
0.14
0.08
TSSE
0.30
0.35
0.25
0.32
0.38
0.27
0.25
0.30
0.21
147
148
Table 7. Means and the 95% upper and lower confidence interval of the mean differences in sonic heat flux of the
downwind-upwind difference distributions for DAY-NEUTRAL cases among the (a) NLAE flux tower stations and (b)
NCAR and ISU flux tower stations. Significant differences are indicated in light red highlight when the confidence
interval of the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
DAY-NEUTRAL
NLAE 2-NLAE 1
Wind direction
category
H
NLAE 3-NLAE 1
Upper
-2
Lower
H
NLAE 4-NLAE 1
Upper
-2
Lower
H
-2
Upper
Lower
(W m )
95% CI
95% CI
(W m )
95% CI
95% CI
(W m )
95% CI
95% CI
HW
2.14
8.57
-4.28
8.27
19.69
-3.16
9.55
19.20
-0.09
HOFF
-2.93
2.03
-7.89
4.22
10.77
-2.32
5.34
10.89
-0.22
-0.89
0.53
-2.30
3.13
5.07
1.19
19.13
22.87
15.39
1.89
6.16
-2.84
5.62
10.70
0.53
5.60
11.65
-0.44
HSW
-0.93
2.97
-4.82
4.15
16.11
-2.19
9.37
13.50
5.25
HSSW
1.49
4.17
-1.18
1.35
7.48
0.82
17.87
22.41
13.26
HS-SSE
-0.04
2.13
-2.20
6.07
4.18
-1.48
28.00
35.18
20.81
148
HON
HWSW
149
Table 7. (continued)
b)
DAY-NEUTRAL
NCAR 2-NCAR 1
NCAR 3-NCAR 1
H
Upper
Lower
(W m-2)
95% CI
HW-WSW
16.14
HOFF
NCAR 4-NCAR 1
ISU 2-ISU 1
Upper
Lower
H
Upper
Lower
95% CI
H
(W m2
)
95% CI
95% CI
(W m-2)
95% CI
32.37
-0.09
31.58
47.05
16.10
19.72
1.31
6.91
-4.28
17.62
25.40
9.84
HON
-3.16
-1.90
-4.42
6.39
8.14
HSW
-1.84
0.29
-3.96
13.63
HSSW
0.62
3.11
-1.88
HS
-3.50
-1.33
HSSE
-6.55
-3.10
Wind direction
category
Upper
Lower
95% CI
H
(W m2
)
95% CI
95% CI
33.30
6.15
6.44
23.43
-10.55
11.99
22.58
1.40
4.28
9.87
-1.30
4.63
-3.88
-2.27
-5.49
-7.60
-6.12
-9.09
16.31
10.95
-3.36
-0.97
-5.75
-12.47
-10.12
-14.82
11.08
14.78
7.38
-1.87
2.05
-5.79
-10.87
-8.05
-13.70
-5.66
1.52
4.69
-1.66
-4.68
-1.84
-7.52
-6.92
-4.37
-9.47
-10.00
3.45
7.26
-0.36
-3.16
0.70
-7.01
-3.95
-0.03
-7.87
149
150
Table 8. Means and the 95% upper and lower confidence interval of the mean differences in sonic heat flux of the
downwind-upwind difference distributions for DAY-UNSTABLE cases among the (a) NLAE flux tower stations and (b)
NCAR and ISU flux tower stations. Significant differences are indicated in light red highlight when the confidence
interval of the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
DAY-UNSTABLE
NLAE 2-NLAE 1
Wind direction
category
H
NLAE 3-NLAE 1
Upper
-2
Lower
H
NLAE 4-NLAE 1
Upper
-2
Lower
H
-2
Upper
Lower
(W m )
95% CI
95% CI
(W m )
95% CI
95% CI
(W m )
95% CI
95% CI
HW
0.61
4.54
-3.32
-2.89
2.95
-8.74
6.76
12.05
1.46
HOFF
-5.19
-1.19
-9.20
3.92
9.16
-1.32
1.84
6.08
-2.41
-1.40
1.89
-4.69
10.05
14.25
5.86
20.40
26.62
14.19
18.66
30.96
6.35
13.76
23.16
4.36
22.16
33.64
10.67
HSW
-25.14
-4.65
-45.63
-1.18
19.74
-22.09
1.32
31.96
-29.31
HSSW
-1.15
6.15
-8.44
3.25
12.76
-6.26
21.99
35.72
8.26
HS-SSE
0.01
4.02
-4.01
7.71
12.85
2.57
28.16
37.06
19.27
150
HON
HWSW
151
Table 8. (continued)
b)
DAY-UNSTABLE
NCAR 2-NCAR 1
NCAR 3-NCAR 1
H
Upper
Lower
(W m-2)
95% CI
HW-WSW
9.41
HOFF
HON
NCAR 4-NCAR 1
ISU 2-ISU 1
Upper
Lower
H
Upper
Lower
95% CI
H
(W m2
)
95% CI
95% CI
(W m-2)
95% CI
95% CI
H
(W m2
)
16.23
2.60
18.98
26.72
11.23
5.96
15.65
-3.73
-12.12
-7.40
-16.84
9.46
15.08
3.84
-8.96
-3.41
-4.18
0.12
-8.49
3.48
8.70
-1.74
-8.69
-2.10
HSW
-3.54
16.90
-23.98
23.96
40.22
7.69
22.79
HSSW
-1.79
19.91
-23.49
15.16
55.71
-25.38
3.50
HS
-6.20
0.91
-13.31
4.98
12.84
-2.88
HSSE
-3.08
3.93
-10.09
0.77
8.18
-6.64
Wind direction
category
Upper
Lower
95% CI
95% CI
-2.17
6.87
-11.22
-14.52
-7.90
-3.16
-12.63
-15.28
-3.30
0.99
-7.58
43.68
1.91
-8.60
8.75
-25.96
51.21
-44.21
1.29
34.50
-31.91
-6.59
2.28
-15.47
-4.55
2.90
-12.00
-9.84
-0.16
-19.52
-0.71
4.73
-6.14
151
152
Table 9. Means and the 95% upper and lower confidence interval of the mean differences in sonic heat flux of the
downwind-upwind difference distributions for NIGHT-STABLE cases among the (a) NLAE flux tower stations and (b)
NCAR and ISU flux tower stations. Significant differences are indicated in light red highlight when the confidence
interval of the test condition does not share the bounds of two null cases of westerly wind and turbines offline.
a)
NIGHT-STABLE
NLAE 2-NLAE 1
Wind direction
category
H
NLAE 3-NLAE 1
Upper
-2
Lower
H
NLAE 4-NLAE 1
Upper
-2
Lower
H
-2
Upper
Lower
95% CI
95% CI
(W m )
95% CI
95% CI
(W m )
95% CI
95% CI
HW
-0.46
0.70
-1.63
-0.86
0.17
-1.89
-5.62
-4.21
-7.04
HOFF
-0.70
0.00
-1.41
-1.95
-1.08
-2.81
1.35
-0.50
-2.20
HON
-7.08
-6.50
-7.67
-10.26
-9.56
-10.95
-9.37
-8.38
-10.37
HWSW
-13.76
-11.43
-16.10
-1.73
0.22
-3.68
-12.00
-9.16
-14.84
HSW
-22.40
-18.31
-26.49
-3.35
-1.24
-5.45
-12.07
-8.37
-15.77
HSSW
-12.46
-9.32
-15.60
-11.84
-7.22
-16.45
-4.95
-2.88
-7.03
HS-SSE
-5.91
-5.29
-6.52
-11.56
-10.76
-12.37
-13.06
-11.97
-14.15
152
3
(W m )
153
Table 9. (continued)
b)
NIGHT-STABLE
NCAR 2-NCAR 1
NCAR 3-NCAR 1
Wind direction
H
Upper
Lower
category
(W m-2)
95% CI
HW-WSW
-0.17
HOFF
HON
NCAR 4-NCAR 1
ISU 2-ISU 1
Upper
Lower
H
Upper
Lower
95% CI
H
(W m2
)
Upper
Lower
95% CI
H
(W m2
)
95% CI
95% CI
(W m-2)
95% CI
95% CI
95% CI
0.92
-1.26
-3.77
-2.12
-5.42
0.18
1.77
-1.41
-3.76
-2.52
-4.99
-0.74
0.46
-1.93
-0.22
0.50
-0.93
-9.08
-8.18
-9.98
-7.67
-6.77
-8.57
-1.46
-0.58
-2.34
-1.89
-1.10
-2.67
-8.82
-7.96
-9.68
-8.02
-7.13
-8.91
HSW
-7.38
-5.06
-9.70
-6.19
-4.15
-8.22
-4.11
-2.42
-5.80
-12.37
-9.96
-14.79
HSSW
-12.72
-11.16
-14.27
-6.43
-5.21
-7.66
-8.79
-7.36
-10.21
-7.00
-5.84
-8.16
HS
-6.39
-5.23
-7.55
-6.56
-5.35
-7.77
-8.97
-7.91
-10.03
-4.22
-3.30
-5.12
HSSE
-13.76
-12.02
-15.49
-14.11
-12.32
-15.90
-14.01
-12.38
-15.63
-15.40
-13.17
-17.64
153
3
154
CHAPTER 5. CROP AND TILLAGE ROUGHNESS SENSITIVITIES TO WIND
POWER
Taken from a manuscript in preparation to Wind Energy
Abstract
Wind farm expansion over the U.S. Midwest has brought convergence of two unlike
energy resources: crop biomass and wind power, which provides a unique interaction
over agricultural croplands. Turbines and wind farms perturb fluxes of momentum, heat,
moisture, and other scalars of crop microclimate. Crop type and other agricultural
management practices (e.g. tillage) produce drag and alter the sensible and latent heat
fluxes, which control the profile of wind speed at hub height. This report addresses the
crop on wind impact by using a conceptual modeling tool for developing strategies for
energy synergy. The WRF single column model is reconfigured to evaluate multiple
sensitivities of surface roughness and boundary layer height wind speed to the wind
energy resource. An ensemble of PBL scheme solutions demonstrates similar wind
profile results that are dependent on the type of turbulence closure parameterization
(local vs. non-local) used in the scheme as previously reported. However increasing the
roughness will yield some improvement to hub-height or blade top height wind speed for
nighttime events in which a low-level jet profile develops. A slight change in roughness
from plowed soil to mulched short-height tillage offers the greatest improvement in wind
both in the daytime and nighttime periods. Conservation tillage (no-till) may reduce
erosion and provides a smaller nighttime benefit but a slight daytime deficit to the wind
resource as compared to a mulch-till.
A wind farm may potentially recover an
additional $10-20 thousand/per turbine in net revenue when short till or no till practices
are applied in the off-season as compared to a smooth plowed soil surface.
Crop
production of soybeans instead of corn provides little benefit of the wind resource
<$5,000/turbine during the summer months when the wind speed is low. Long-term
benefits in electrical production can be applied from a corn or soy harvested crop to
applying mulch tillage during the off-season.
Adjusted-revenue savings of a few
155
hundred thousand dollars are proposed when mulch tillage is practiced in the off- season
between a variety of corn and soybean rotation practices.
1. Introduction
At the end of 2012 U.S. wind power generation was exceeding 60 GW of installed
capacity (AWEA) and another ten to twenty fold expansion of this energy resource is
likely in the coming decades (U.S. DOE 2008).
Much of the recent wind farm
construction since 2005 has occurred over several Midwestern states, which also feature
some of the largest arable acres for cultivating renewable bio-energy fuels and
substrates.
The state of Iowa alone accounts for about 19% of the national corn
production and about 12% of the soybean yield and at the end of 2012 nearly 5500 MW
of electricity were generated from several thousand wind turbines clustered across the
state. The construction of large wind farms on agricultural cropland brings two related
renewable energy systems into a potentially unique interaction and opportunity for
energy synergy.
Isolated wind turbines and large wind farms perturb local wind fields and generate
turbulence behind each turbine. This change of mixing can influence crop fluxes of
momentum, heat, moisture, and other gases (e.g. CO2). During the 2010 and 2011 Crop
Wind Energy Experiment (CWEX-10/11) recent in situ flux measurements within the
leading line of turbines at the southwest edge of a 300 MW wind farm in Central Iowa
indicate turbine modification of canopy turbulence, wind speed, air temperature
(Rajewski et al. 2013a), and fluxes of heat, H2O, and CO2 (Rajewski et al. 2013b).
Additional evidence has been given for the high spatial variability of downwind fluxes
according to distance from the turbine, and the relative centerline or periphery location
within the wake (Rajewski et al. 2013c). These studies have documented changes on the
upwind and downwind side of turbines but the effects are highly variable and dependent
on ambient meteorological conditions (e.g. wind direction, hub-height wind speed,
thermal stratification, moisture), the operating characteristics of the turbine, as well as
the variations in agricultural management and the inherent variability of the soil.
156
Conversely, on the role of wind power and reliability the agricultural management
techniques and local field scale conditions may potentially cause variations in the hubheight wind resource and in the shear characteristics throughout that layer. A taller or
shorter crop (e.g. sorghum/corn vs. soybeans/alfalfa) may induce more drag on the wind
speed at hub height, yet a rougher surface also induces more turbulence to cool the
surface and therefore regulate the diabatic exchange between the crop and the boundary
layer. The effect of surface roughness (z0) on the wind speed at a given height u(z) is
seen in the following profile equation (e.g. Stull 1988):
𝑢(𝑧) =
𝑢∗
𝑘
𝑧
z
[(𝑙𝑛 𝑧 ) + ΨM (L)]
0
(1)
where u* is the surface friction velocity, k is the Von karman constant assumed to be
0.4, and M(z/L) is a stability correction term with a function that depends on the type of
stability situation (NEUTRAL/STABLE/UNSTABLE). The L term denotes the MoninObukhov length at the height for which the production of shear-based turbulence over
the buoyant generation are in equilibrium. L is determined from the surface sensible
̅̅̅̅̅̅̅𝑣 ) according to
heat flux (𝑤′𝜃′
𝐿=
−𝑘𝑔 (𝑤′𝜃𝑣 ′)
(2)
𝜃𝑣 𝑢∗ 3
For the post-harvest/pre-plant period, a no-till surface is expected to have lower soil
temperatures within an undisturbed soil with ample wetness.
However vertically
oriented residue promotes increased friction and turbulence on the surface (e.g. Horton
et al. 1994, 1996).
Strip tilling leaves about 75% of the field residue above the soil
surface and promotes aeration and warming of the soil in the intended planting row.
Conventional tillage methods remove much of the field residue, warm and dry the soil,
but also increase erosion potential Management decisions are also dependent on the type
of cultivar and the soil drainage quality (ISU 2005).
157
Additionally a dry or wet soil determines whether a crop can regulate at the maximum
potential evapotranspiration allowed by the atmospheric constraints (net radiation, wind
speed, ambient relative humidity). Crop fluxes of heat and moisture may perturb the
vertical wind profile up to the turbine height through the diabatic correction term YM
from equation (1). In the daytime, one may thus determine the surface profile changes
with roughness on the hub-height wind speed.
However, at night this profile equation
may not apply up to the hub height of turbines.
Throughout much of the U.S. Midwest, the nighttime development of a jet-like sheet
of air provides a substantial boost in electrical generation from wind farms. A low-level
jet (LLJ) can form in any season and vary in intensity, height, duration, and other factors
according to the previous daytime profile of above-PBL height or geostrophic wind
speed, Vg (Davies 2000). Typically, LLJ development occurs a few hours after sunset
and decay begins a few hours after sunrise. The height of the jet wind maximum, the
intensity of this maximum, the amount of turbulence within the jet are all functions of
the shears of wind speed and wind direction within the jet layer and the thermal
stratification within that layer (e.g. Banta et al. 2002; Kelley 2004; Pichugina and Banta
2010; Storm and Basu, 2010). Nocturnal jets can feature significant shear in both speed
and direction especially if the maximum speeds are located within a few 100 m above
the top of the rotor. The underside of the jet supports strong turbulence and may develop
Kelvin Helmholtz instabilities if the layer stability is only slightly stable.
Tall tower
data from the Lamar Low Level Jet experiment and the Great Plains CASES-99
experiment demonstrate the likelihood of adverse turbulence conditions in the lower
boundary layer where turbine blades may be located (Kelley et al. 2004; Kelley et al.
2006)).
Excess turbulence creates lulls in the available power (Wharton and Lundquist
2012) and induces twisting and bending forces on the blades and tower components
(Sathe and Bierbooms 2007; Walter et al. 2009)
Forecasting of LLJ profiles continues to be an objective of improvement for
operational models as nighttime forecasted wind speeds generally under predict the
strength of the wind turbine rotor speeds and shear and over predict the daytime resource
158
(Draxl et al. 2012; Storm and Basu 2010; Shin and Hong 2011) .
These biases are
related to the type of planetary boundary layer scheme used in the simulation and the
turbulence closure parameterization techniques used for each scheme. Wind speed bias
in the forecast is reduced from a combination of high-resolution models and the use of
model ensembles. Recent studies by Deppe et al. (2013) and Gallus and Deppe (2011);
demonstrate improved accuracy in some of the nighttime wind resource when an
ensemble of hub-height wind speeds using different PBL schemes was compared with
80-m meteorological tower data within a wind farm in Northwest Iowa. Traituer et al.
(2012) also indicated an improved short term forecast (1-3 hr lead time) by using an
ensemble of high resolution operational models and a PBL suite of single column
models and developing a calibration tool through running several days or weeks of
simulations to develop a training method of determining bias corrections.
Extremes in hub-height wind speed over a few hours can occur in localized
meteorological conditions (frontal passage, mesoscale convective system gusts and
diurnally driven transitions in the boundary layer). Ramp up and ramp down events (e.g.
Gallus and Deppe 2011) have been shown to be very difficult to predict even in short
lead time for the forecasts. Surface sensitivity may be one such factor in modulation of
the frequency, intensity, and duration of such ramping events.
Increasing skill in
prediction of these ramping events may facilitate energy sale savings of $2-5 MW-hr or
up to $2-4 billion -yr for nationwide implementation of these strategies under the 20%
by 2030 DOE scenario (Marquis et al. 2011).
Recent high-resolution modeling indicates that the spatial variability of 80-m wind
speed is similar to the variations present on a surface terrain map (Personal
communication, Anderson, 2012). Therefore, it is highly plausible that in addition to
terrain, the heterogeneous patchwork of agricultural fields may also provide an influence
on wind power and reliability that has not yet been determined. Numerical simulations
with spatial variation soil moisture, roughness, soil type, and crop resistance alter
boundary layer development of clouds and/or induce localized circulations similar to a
lake breeze (e.g. Zhong and Duran 1997; Alapaty et al. 1997; Courault et al. 2007).
159
Hacker (2010) has initially demonstrated how soil moisture perturbations and roughness
can alter near-surface boundary layer wind speed and turbulence. However, there was no
distinction between how these surface sensitivities altered the day vs. night wind
resource.
Little understanding exists of how differences in diurnal variations in surface fluxes
of heat, moisture, and momentum over crop canopies will influence on shore hub-height
wind speed and rotor layer thermal stability and wind shear and turbulence
characteristics.
Several modeling studies have explored wind power and reliability
impacts on surface heterogeneities for idealized vertical column models of quasi steady
neutral, stable or unstable boundary layer parameterizations
(Barthelmie 1999,
Emeis 2010) , or similar physics are implored with large eddy simulations (Sim et al.
2009 ; Meyers and Meneveau 2009; Zhou and Katopodes Chow 2012; Park et al. 2012).
A model that can offer sensitivities to several surface features (roughness,
vegetation fraction, soil moisture) may improve risk assessments of these changes in
wind and turbulence.
The testing of several PBL schemes in a similar model offers a
range of sensitivity and is desirable to improve forecasting of LLJ events. To facilitate
better assessment of the sensitivity of surface conditions to short-term prediction, we
present here a simplified conceptual tool in analyzing variations in surface roughness on
the wind power resource over the diurnal cycle.
The interactions between the surface
and boundary layer require time-dependent forcing of radiation, surface conduction,
convection, evapotranspiration, surface heat fluxes, microphysics, and boundary layer
mixing.
Investigation of wind characteristics over the diurnal cycle is a desirable
outcome for improving understanding of the crop-turbine interaction.
In this study, I
address if agricultural management practices have any significant impact on the wind
resource.
I hypothesize that the increased drag imposed on the surface by a taller crop or tall
tillage residue will slow winds at hub height in the daytime whereas during the night the
loss or benefit that the roughness may provide will be highly dependent on the wind
strength of the geostrophic wind speed. Sensitivities to wind energy characteristics are
160
directly linked to the differences in PBL and surface layer options of each configuration.
The non-local closure PBL schemes will demonstrate the least response of surface
roughness conditions to the nighttime wind resource, whereas the local turbulence
closure schemes will be the most sensitive to changes in canopy drag and the
corresponding surface fluxes. Therefore, the vertical mixing parameterization imposed
within in each PBL scheme is a dominating factor in the behavior of the wind profile
(e.g. Deppe et al. 2013, among others). In Section 2, I provide the numerical method in
an ensemble frame work of single column PBL models to quantify the surface roughness
sensitivity to the wind resource. Section 3 offers the analysis procedure for depicting the
effect of the tillage on available power and other diagnostics of reliability (e.g. rotorlayer shear (both in speed and direction) and stability). In Section 4 provides additional
insights for using this quantitative assessment from the model and extrapolate from the
analyses some proposed guidelines in wind farm siting assessment. Concluding remarks
are presented in Section 5.
2. Materials and Methods
The 1-d multiple single column model, referred hereafter as WRF_1d (from WRF
V3.1.1), was developed from Tardiff and Hacker (2006) and applies several noninteracting single column modules to create a matrix of testing independent sensitivities
to two different parameters according to the method of Segal et al. (1993).
For this
study in using the east-west coordinate, the model sets geostrophic speeds between 2 and
20 m s-1 and in the north-south coordinate any surface variable (soil type, soil
temperature, soil moisture, surface roughness, can range from a low to a high end. For
this specific configuration, I test the effect of surface roughness from 0.005 m to 0.5 m
using a logarithmic increment as shown in Table 1.
We depict the effect of surface
roughness on the wind resource by assuming a crop cover in near harvest stage (with
very little transpiration) for our high roughness categories and a dry tilled surface for our
low roughness categories.
161
The model uses a standard horizontal and vertical mixing coefficient (300 m2 s-1, and
1 m2 s-1, respectively) to prevent numerical instability and thus requires no turbulence
option as specific mixing options are configured in each PBL schemes.
The individual
PBL schemes tested in this WRF_1-D configuration were (1) the non-local turbulence
closure scheme Yonsei University PBL (YSU) from Hong et al. (2006) , (2) the local
turbulence closure scheme of Mellor Yamada Janjic (MYJ) (Janic 2002), (3) The local
closure Quasi Normalization Surface Exchange PBL (QNSE) from Sukoriansky and
Galperin (2008) and the 2.5 level turbulence closure from Mellor Yamada Nakanishi and
Nino scheme (MYNN2.5) from Nakanishi and Nino (2009).
Although the 3rd order-
level closure (MYNN3.0) is offered in WRF there was difficulty in running the idealized
simulations with our small vertical grid spacing.
Additionally I tested the non-local
closure asymmetric convective boundary layer scheme version 2 (Pleim 2007) known as
ACM-2 but found considerable oscillation in the wind fields at within our smallest grid
discretization within the turbine rotor layer. Both these scheme results are omitted in the
following analysis.
WRF_1d uses 49 vertical levels with about 18 m grid separation in
the lowest 150 m and so this layer provides higher vertical resolution in the PBL below
and within the top and bottom blade pass height (roughly from 42 to 118 m for an 80-m
hub height 1.5 MW turbine). The model time step is set to 15 s. All simulations are
configured to use the NOAH land surface model and RRTM long-wave and Dudhia
shortwave radiation schemes. The NOAH LSM conveys flux information at only the
level of the roughness and therefore WRF modelers are assuming a mixture of air,
vegetative elements and soil surface in the surface level parameterization of the model.
The microphysical scheme is deactivated for these simulations to isolate surface
effects in conditions free of clouds and precipitation. The absence of full 3-d motions
and large-scale advection requires that the perturbation coriolis forcing generate the
inertial oscillation of the wind profile. The ‘perturbation’ is a departure from the base
state of the geostrophic wind speed. Appropriate mean hub height speeds (indicated in
Table 1) categorize power characteristics according to the strength of the boundary layer
top wind speed and not the hub-height wind speed. The multi-1d simulations are absent
162
of frozen ground or snow cover/snow melt and soil moisture and temperature are held at
constant input levels throughout the simulation (T0=288 K, VWC=0.10 m3 m-3). The
model simulations are initialized for the fall Iowa landscape using a default sounding
from the Tardiff and Hacker configuration of 19 UTC (1400 LST) 22 Oct 1999. The
simulations spin-up in about 6 hours so that the first night and the corresponding first
two full days are used for the 48-hr analysis period. Data are output in 15-minute
increments.
At the completion of each PBL scheme configuration, I determine the
power output statistics among the PBL schemes and the factors illustrating what surface
and turbine layer variables are influencing the wind resource.
3. Theory/Calculation
Wind power calculation in WRF_1d
Hub-height wind power (P) is calculated in WRF_1d according to the following
relationship:
P = ½ Cp Ng Nb A UH3,
(3)
where  is air density, Cp is the power coefficient, Ng and Nb are efficiency factors (0.85
and 0.95) due to frictional loss of energy on the turbine gear box and other components,
A is the cross sectional swept area of each turbine rotor disk, and UH is the hub height
wind speed. Cp is a function of blade tip speed, which has direct relationship to the wind
speed. The power coefficient is parameterized as in previous studies according to the
rated power coefficient depending on the turbine model (as in Adams 2007).
We
determine the power coefficients from the power curve of the GE 1.5 MW turbine with
super long extended blades (SLE), presented in Figure 1a. The variation of Cp with the
wind speed (Figure 1b) indicates the maximum power coefficient occurs at a hub speed
slightly less than the rated speed of 13 m s-1.
The rotor swept area, A, of the turbine
(5346 m2) is calculated for a 74 m diameter rotor of the GE 1.5 MW turbine with hub
height of 80 m (GE 2009). For easier detection of the surface roughness sensitivities, I
163
normalize the power to a base condition (e.g. dry soil over a smooth plowed field).
Using this normalization power losses/enhancement from the surface heterogeneities are
related to a percent value. The simulations are configured to both output a 15- minute
instantaneous power (units in MW) and an accumulated power (in MJ). I convert the
accumulated power in MJ to kW-hr over the by the following relationship (3600
kWhr=1 MJ) to address the economic implications of tillage and crop type on single
wind turbines and wind farms in several temporal scales (daily, annual, and a 30-yr
lifetime of the wind park).
Power-derived quantities relating to electrical generation
The baseline power results for plowed smooth soil for each PBL scheme and the
average of the 4 schemes is offered in average and normalized differences in power over
a 48-hr period starting after the initial 6-hrs for presumed model spin-up time. This data
will compare the behavior of the PBL schemes over the first night of the 48-hr
simulation before examining the sensitivity of the wind profiles to roughness. Then the
comparison of scheme behavior is offered between roughness types of no-till, soybean
and corn canopy height. Normalized power difference from the plowed soil condition
for these same roughness categories will be examined according to the rotor layer
Richardson number (Ri) which is a stability metric of the temperature gradient between
the top and the bottom blade span and the wind speed shear in the same layer as shown
in Eq. (4):
𝑅𝑖 =
𝑔 𝜕𝜃𝑣
𝜃𝑣 𝜕𝑧
2
2
𝜕𝑢
𝜕𝑣
⌈( ) +( ) ⌉
𝜕𝑧
𝜕𝑧
(4)
After demonstrating a relationship to normalized power and stability, I will discuss the
48-hr accumulated differences in power in kWhr, and the translated power revenue
change going from plowed soil to a higher surface roughness. Three speed categories
are considered: (1) near the cut-in turbine speed of 3.0 m/s (Vg is near 6 m/s), (2) an
164
intermediate speed slightly less than the rated speed of 13 m/s (Vg is near 10 m/s) when
Cp is at a maximum value, and (3) a fast boundary layer speed of 16 m/s, which indicates
hub speeds at or slightly above the rated speed. Each plot depicts a PBL schemeaverage and the standard deviation of that mean for the individual surface roughness
categories.
From the 48-hr accumulated power difference of each scheme (m), each roughness
(j), and wind category (i) I compute a probable net power production for a single day
period according to equation (4), which demonstrates daily net income (Rday) according
to a balance in power produced (Pgross) at a given cost (e.g. $50 MWhr-1), the service
charge to upload power to the grid (T), and the land use rental (L):
$
$
𝑅𝑑𝑎𝑦(𝑖,𝑗,𝑚) = 𝐶𝐹(𝑃𝑔𝑟𝑜𝑠𝑠(𝑖,𝑗,𝑚) [𝑀𝑊ℎ𝑟] ∗ 𝐶𝑜𝑠𝑡[𝑀𝑊ℎ𝑟] − 𝑃𝑔𝑟𝑜𝑠𝑠(𝑖,𝑗,𝑚) ∗ 𝑇[𝑀𝑊ℎ𝑟])-L
(5)
CF is the capacity factor and was assumed to be 30% but more optimistic estimates in
future production capabilities could reach near 40%. The capacity factor recognizes that
the source of energy is intermittent according to periods of low wind speed and at the
same time not all the wind energy produced can be on the grid if the electric demand is
low. Cost of electricity produced is presumed to be $50 per MWhr as previous studies
have shown the cost of production to be between $20-70 MW/hr in the last few years
(Tegen et al. 2013). The low end reflects tax payer subsidy contributions so the $50 is a
good middle estimate. The transmission fee charge depends on the region of the electric
oversight but on average wind farms are charged between $2 to $12 MWhr-1 to put their
electric generation on the grid (Fagan et al. 2012). Land Use fees range between $2-10
k per
year per turbine OR up to 2% in annual gross revenue from the park.
For this study, I
assume a transmission cost of $5 MWhr-1 and a land lease estimate of $4,000 per year
per turbine.
165
Proposed climatology of crop roughness and surface tillage on annual electric
production and 30-yr lifetime of the wind farm
I use the roughness information from the three speed classes and extrapolate from the
departures in daily net income from the plowed soil category compared among the PBL
schemes and the wind speed classes to a monthly change in revenue (Rmonth):
𝑅𝑚𝑜𝑛𝑡ℎ(𝑖,𝑗,𝑚) = ∑30
𝑛=1 𝑅𝑑𝑎𝑦(𝑖,𝑗𝑚)
(6)
and then from a monthly production to a weighted yearly production
𝑛
𝑖
𝑅𝑦𝑒𝑎𝑟(𝑗,𝑚) = ∑12
𝑛=1(𝑅𝑚𝑜𝑛𝑡ℎ(𝑖,𝑗,𝑚) ∗ 12)
(7)
where ni is the number of months in the year when there is a given wind speed category
(e.g. 10 m s-1). The categories of the months were determined by equating the model
geostrophic speed categories to approximate monthly averaged hub height wind speeds
from
the
Iowa
Energy
Center
wind
resource
maps
(available
http://www.iowaenergycenter.org/renewable-energy/wind/iowa-wind-maps/).
at:
For this
climatological year average wind speeds range from 4 out of the 10 geostrophic speed
classes tested in the model (6 m/s, 8 m/s, 10 m/s and 12 m/s).
A summation of the
daily electrical production for each PBL scheme and ensemble average for each
roughness category is then combined to a weighted annual accumulated production using
the climatological wind categories for the corresponding months.
However, since not all tillage and roughness elements are uniformly distributed over
a calendar year each of the climatological wind categories are combined with variations
in surface roughness representing changes in crop tillage and crop type during a full
calendar year. This approach is quite basic for making the assumption of no diabatic
heat, moisture effects on the crop during the growing season. However, I wish to
illustrate a plausible method in assessing power loss/gain from crop management
decisions in annual and 30-yr impacts. A variety of tillage and crop type methods are
166
explored corresponding to the surface roughness categories tested. For each tillage/crop
condition shown in Table 3, I calculate each PBL scheme and ensemble mean weighted
production for the climatological year. Several combinations of tillage and crop type are
tested from Table 3 but Figure 2 demonstrates the seasonal change in roughness and
wind speed that is explored for corn and soybean production.
The annual production
for these tillage-crop type categories is then combined into a 30-yr comparison of tillage
and crop-type rotation sequences on total production and I assume the same annual wind
climatology in the 30-yr period. Table 4 depicts a sampling of possible crop rotation and
tillage methods that are tested for the impact on power production. The 30-year period
is chosen to equally divide both two and three-year crop rotation patterns equally
although I recognize that wind farms may be operational only for a period of up to 20 or
25 years. In addition to the summation of the net yearly income I also subtract a modest
yearly factor $40,000 /yr for Operation and Maintenance fees and other miscellaneous
costs (e.g. warranty, insurance, administrative costs) per turbine. I assume that not all
turbines would need maintenance so other factors could be included in the O and M
costs. The lifetime revenue totals are then given for the various crop rotation and tillage
options in the 30-yr period to guide marketing decisions and land contracts for the siting
of future wind farms.
4. Results
PBL scheme inter-comparison of wind speed profiles
Baseline plowed soil surface
I present snapshots of the nocturnal wind profile for all the PBL schemes and the
ensemble mean for a gestrophic wind of 6 m s-1 and a smooth plowed soil surface
(z0=0.005m). In the onset of the evening in Figure 2a, all the PBL schemes except YSU
indicate a strong reduction of surface wind speed likely from PBL surface coupling of
sensible heat flux.
The low-level maximum in speed is occurring at the 80-m hub
height and Banta et al. (2005?) confirms this jet pattern does occur in the CASES-99
dataset. The jet continues strengthening throughout the night in all the local-closure
167
PBL schemes with maximum speeds of 7.5 m s-1 (Figure 2b, c) whereas for YSU the
peak speed (6.5 m s-1) is absent until after the other schemes depict the jet descending
(Figure 2d). Another aspect of the jet evolution demonstrates that the MYNN2.5 scheme
more closely follows the elevated YSU jet development a few hours before sunrise. A
jet-like profile at the turbine blade top height (120 m) is retained among the YSU and
MYNN2.5 in the early morning even up to 2 hours after sunrise (Figure 2e). The last
frame of the simulation around 1100 LST (Figure 2f) depicts a uniform surface layer
profile according to the onset of the new daytime mixed layer and so there is at most a
0.5 m s-1 difference in wind speed between schemes. At faster geostrophic speeds (Vg=
10 m s-1, Figure not shown), we observe a higher development of the nocturnal jet as
confirmed in previous studies by Davies (2000) and Pichugina et al. (2005). The YSU
jet maximum occurs at least 500 m above the surface such that it will have little
influence on the turbine layer wind speeds for the nighttime. Among the other schemes
the jet forms around 150 m, slightly higher than what level applies to the turbine blade
rotor top travel pass. As in the lower geostrophic category of 6 m/s, the QNSE and
MYJ for the 10 m/s category are the earliest in the onset of the jet and also the quickest
to decay. There is a similar lateral shift of the jet a few hours before sunrise to shortly
after sunrise in the MYNN2.5 as presented in panel Figure 2d.
I present in Table 6 the mean and maximum power characteristics for the
geostrophic speed categories among the individual PBL schemes and the ensemble
mean.
For slow speeds (near or slightly above the turbine cut-in speed) the local
schemes of the QNSE and MYNN2.5 simulate the highest mean power content for the
48-hr period. As geostrophic speed increases above 10 m s-1 there is a transition from
the highest power generation among the local MYJ-closure schemes to the non-local
closure scheme (YSU). Less difference in calculated power among the PBL schemes is
illustrated at the geostrophic speed category of 16 m s-1 or near a hub-height rated speed
of 13 m s-1.
A similar pattern is depicted among instantaneous maximum power; at low
speeds the local closure schemes depict a stronger nocturnal jet than for the non-local
scheme.
The variability in maximum power is also reduced at higher geostrophic
168
speeds. Although the local-closure schemes forecast a larger power resource it can be
expected that a lower position of the nocturnal jet of the MYJ, QNSE, and MYNN2.5
will have increased speed and directional shear than for the higher position jet in the
YSU (Table not shown).
The MYJ and QNSE have similar shear exponents for all
speed categories and these are well above the wind industry threshold of avoidance of
0.2. The YSU only has high shear when boundary top wind speeds are low, whereas the
MYNN2.5 features a between range of the high shear of the other local schemes and the
non-local YSU. A similar pattern of maximum wind directional shear among the PBL
schemes follows the behavior of the shear exponent. The most negative directional
shear not displayed illustrates the least impact in most of the PBL schemes except for
QNSE at moderate to high wind speed categories. There is evidence of 15-20° of
vertical backing of winds with height during the breakup of the morning temperature
inversion.
Roughness change to the wind profile
Figure 3 (a-f) indicates the differences in the nighttime profile for no-till,
soybean, and corn type of roughness. The profile charts are focused on the rotor layer
depth to better depict changes in wind speed caused by roughness. In the early evening
(Figure 3a) the profiles among the local closure schemes are similar shape and intensity
of speed. The YSU depicts a more linear shaping and therefore weaker wind shear
within the rotor layer whereas the other schemes show indications of the accelerating jet
beginning. Near midnight in Figure 3b a bulge in the profile begins to form in the local
closure schemes (MYJ, QNSE, and MYNN2.5) and this jet core is displace upward
about 20 m for a corn surface as compared to the no-till roughness. As the jet matures
and reaches max extent the local schemes depict a 0.5 to 1.0 m s-1 reduction in speed for
soybean and corn surfaces as compared to the no-till in the lower half of the rotor depth.
However there is a 0.5 m s-1 enhancement in speed from corn to no-till at and above the
jet max for winds at the top portion of the blade travel, particularly in the MYNN2.5
scheme. In Figure 3d during the descent of the jet there is little to no enhancement of the
169
rotor-layer wind with increased roughness and instead speed deficits of 0.5 to 1.0 m s-1
are found in the MYNN, QNSE, and YSU schemes and close to 2 m s-1 reductions occur
with the MYJ. Near the completion of the breakup of the nighttime jet profile (Figure
3e), there is consistency among all schemes showing 0.5 to 1.0 m s-1 reduction in speeds
for soybean and corn surfaces with respect to no-till at the lower half of the rotor with
still some enhanced or less departures in speed in the upper blade pass height. Shortly
before the return of a uniform daytime mixed -layer the schemes depict near-uniform
reductions of 0.5 to 0.75 m s-1 in speed for all levels in the rotor height as the roughness
increases from a no-till surface to a corn canopy (Figure 3f).
PBL-scheme sensitivity to surface roughness and thermal stratification
The effect of surface roughness on wind power is determined by comparing the
plowed soil normalized power to select cases of roughness at certain speeds.
The
roughness categories examined are for no-till surface (z0=0.025 m), a soybean canopy
(z0=0.075 m) and a corn canopy (z0=0.25 m). Instead of determining the effect through
time height profiles, I indicate the normalized power change according to the turbine
layer Richardson number (Ri). All the panels of Figure 4 depict high daytime scatter of
enhanced or decreased power resource so I will focus the results on the nighttime period.
In Figure 4a no-till condition depicts the highest spread of normalized power among the
PBL schemes. This occurs during the nighttime when turbine-layer temperature gradient
supports a moderately stable stratification (0.5<Ri <1.5). The QNSE depicts the highest
power reduction (45%) whereas the ensemble mean is about 15%. Power departures
from the plowed soil condition are reduced for strongly stable conditions (Ri >1.5) when
the turbine layer wind is underneath the influence of the jetting profile. The pattern is
similar to the soybean type roughness in Figure 4b except that the slightly higher crop
surface reduces strongly stable conditions in the rotor layer (Ri near 0.5). This leads to
nearly a 60% in power simulation among the QNSE scheme and the ensemble mean is
depicting a 30% reduction in the wind resource. For the corn-like roughness of 0.25 m
the PBL ensemble indicates 30-45% power reduction as there is equally strong influence
170
of surface roughness among all the PBL schemes (Figure 4c). Except for the MYNN2.5
the nocturnal power differences occur when the turbine–layer stability is weakly to
moderately stable (0<Ri<0.5). As geostrophic speed is increased the power reduction
indicates weaker rotor-depth stratification (Ri<0.25) for much of the night. For no-till
the mean reduction is about 30% (Figure 4d), for soybean this is similar (Figure 4e)
whereas in Figure 4f the rougher surface of corn indicates up to a 45% power depletion
from the plowed soil condition.
The daytime no-till surface depicts a slight
improvement to the power resource for the ensemble mean near 30% whereas for
soybean this improvement is reduced to 15% for weakly unstable conditions (-1.0<Ri<0)
and supports a 15-30% deficit for moderately unstable conditions (Ri<-1.0) in the
turbine rotor. For the corn surface the daytime energy resources is depleted by 30 to
50% for weakly or moderately unstable conditions and this pattern is similar to the
nighttime power reduction. As speeds increase to near the rated turbine speed of 13 m
s-1 (Figure not shown) there is less variability to the power difference and the lack of
strong stability or instability indicates a simplified linear effect with surface roughness
as compared to the non-linear impacts noted for moderately unstable and stable
stratification. The no-till surface indicates weak power enhancement (15%), the soybean
surface, indeterminate, and the corn surface would yet promote reductions of at least
20%, and near 40% for the MYNN2.5 scheme. These differences illustrate the complex
role of thermal stratification and roughness on changing the wind power resource.
Idealized roughness impact on electrical production
I next present the 48-hr normalized differences for all roughness categories and
accumulated power in kWhr for the three turbine conditions (Vg=6 m/s: near cut-in
speed; Vg=10 m/s, slightly less than rated speed; Vg=16 m/s, at or above the rated
speed). At low speeds (Figure 5a) the MYJ is the most bullish on short tillage elements
(mulch till, short grass) on boosting the power resource by 2,000 to 3,000 kWhr,
whereas the QNSE depicts the highest power drop of 2,500 to 3,500 kWhr for tall crop
canopies (corn, sorghum). The ensemble variability is also larger for short tillage
171
elements than for tall crop canopies. For speeds less than the rated speed there is higher
production by as much of 8,000 kW hr for all tillage elements (mulch-till to no-till) and
an equal deficit of electrical generation for very tall sorghum canopies (Figure 5b).
As
depicted in the turbine-layer stratification plots of normalized power differences, the
higher wind speed near the rated speed in Figure 5c supports minimal benefit (<
5,000kWhr) for low tillage surfaces and for the highest roughness element the power
production is reduced by 10,000 to15,000 kWhr.
Taking the information from the 48-hr power departures to plowed soil for the same
speed categories and determining the daily income spread there is a $50-$100 per turbine
enhancement in power on mulched to low roughness surfaces (all tillage practices) as
shown in Figure 6. A soybean surface offers less power depletion of under $40/day and
corn about $40 to $75/day deficit as compared to a plowed soil surface. The largest
power deficits occur for the higher 2 out of 3 speed categories of 10 m s-1 and 16 m s-1 in
Figure 6b and Figure 6c. Using the climatology weighted wind speed over the calendar
year (Figure 6d) anywhere from $10,000 to $30,000 per turbine of increased revenue is
plausible if a landowner/crop producer would maintain a mulched soil surface over the
calendar year. For a soybean surface the cost margin difference would be about ±
$5,000 per turbine whereas if corn was raised at left un-harvested over an entire year the
additional roughness would decrease power sales by $10,000 to $30,000 from the
plowed soil condition.
However, because not all roughness categories or wind speeds
are uniform throughout the growing season I next present the results with the combined
wind climatology and seasonal changes in roughness.
Crop tillage management strategies and their impact on wind power
Following the example of Figure 2, I present appropriate seasonal changes in
roughness and the corresponding impact on the wind resource for the Iowa climatology
year.
Electrical production is the least for scenarios that begin a growing season with
172
plowed smooth soil as shown in Figure 7a. The baseline-planting scenario for corn on a
plowed soil would be about $85,000 per year.
The highest energy production
($110,000) is related to a short mulch surface before planting. No-till practices follow in
between the plowed and short-tilled mulch strategies.
Corn planted on
plowed/mulched soil indicates about $5,000-$7,000 less electric production per turbine
than for soybean cultivated on the same soil or tillage surface. For the no-till comparison
between corn and soy this difference increases to about $10,000 in lost revenue.
Over a 30-hr lifetime of the wind farm there are again three main regimes: (1) low
production scenarios (mixed rotation sequences of corn/soy over plowed ground), (2)
maximum production scenarios (low roughness mulch under all corn-soy rotation), and
(3) intermediate benefit/loss of electrical production (no-till and mulch combinations
with corn/soy rotation). Common tillage and crop rotation are indicated by the last two
categories on the right side of Figure 7b. Corn planted in mulch followed by a no-till
year of soybeans is about as favorable a scenario of planting no-till soybeans
continuously. Additionally a sequence of corn planted in mulch followed by another
year of no-till corn and then a year of no-till soybeans offers about the same benefit of
planting soybeans in no-till continuously.
Therefore, the common tillage and crop
rotation methods used by agricultural producers at least do not deplete the wind resource
as largely as for corn cultivated on no-till continuously.
The various mulch practices
illustrate upwards of $400,000 to $800,000 in enhanced energy revenue per turbine in a
30-yr installation period of the wind farm.
In locations where soil erosion is not a
strong environmental concern, wind operators may encourage crop producers in
reducing field residue heights during the off-season. Common land rentals for turbines
are from $2,000 to $10,000 and/or up to 2% of the annual revenue from the wind farm.
For a potential increase in annual revenue of up to $30,000 per turbine wind farm
operators may consider offering a higher lease return (a few additional $1,000 /turbine or
another 1-2% addition in gross revenue) to land owners who have turbines installed on
cropland and who practice short mulch tillage.
173
5. Concluding remarks
In the last two sections of the results, I present a combination of changes in surface
roughness and wind speed according to the growing season and a climatological wind
map for the state of Iowa. In the summer, hub-height wind speeds have the lowest
speeds for the entire year. Although a tall corn crop is grown during this period, there is
less power deficit in the generally weak synoptic flow months of July and August as
compared to the fall or spring months with strongly forced synoptic systems. What is
important for wind operators to consider is requesting agricultural producers to practice
moderate tillage methods to support a horizontal mulch cover to the soil. In both the day
and night there is an anticipated power benefit by increasing hub height speeds by 0.5 to
1.0 m s-1.
The longer period in the fall that a crop remains un-harvested there is an
increased possibility of lost power revenue because hub height winds are on average at
or slightly below the rated speed of most operational scale turbines.
No-till practices
offer some increased power benefit for both yearly and a 30-year lifetime of a wind
farm, however mulched tillage would be the most favorable to harnessing the wind
resource. Strategies like those suggested here could be expanded on and refined as more
realistic parameterized crop growth effects within a meteorological model are provided.
The current study has assumed crop heat and water fluxes are negligible but this
would only be true in the few weeks leading to harvest when the crop is has lost
significant moisture. I have not provided a seasonal transition in temperature and
moisture profiles to better illustrate the concepts that are raised in these simulations. A
full 3-D version of seasonal changes in crop roughness, phenology, and other time
variant characteristics (as in WRF-crop) would be more suitable to reinforce or clarify
the claims raised in this simplified conceptual scaling study.
Any modeling study
should receive careful scrutiny with available measurements and more atmospheric
profile measurements of wind speed and temperature are necessary to improve
understanding of linked crop canopy and PBL processes.
Acknowledgments
174
I recognize several contributions from Moti Segal and Gene Takle in the early stages of
beginning the project.
I thank Moti for his assistance in many hours of modeling
foresight, modifications, and, debugging techniques to the WRF_1d simulations.
Additional modeling contributions were provided by Dr. Eric Aligo and Adam Deppe.
Data analysis was supported in part by the National Science Foundation under the State
of Iowa EPSCoR Grant 1101284,
NSF grant BCS0618823, DOE grant #13-450-
141201, Ames Laboratory Project 290-25-09-02-0031,and the University of Iowa Center
for Global and Regional Environmental Research grant#400-60-12.
6. References
Adams M, Keith D (2007) A wind farm parameterization for WRF. 8th WRF Users
Workshop, Boulder, CO.
American Wind Energy Association, cited 2013: U.S. wind industry year-end
2012market report. [Available online at
http://www.awea.org/suite/upload/AWEA_USWindIndustryAnnualMarketReport20
12_ExecutiveSummary.pdf]
Banta, RM, Newsom, RK, Lundquist JK, Pichugina YL, Coulter, RL and Mahrt L,
(2002) Nocturnal low-level jet characteristics over Kansas during CASES-99.
Bound.-Layer Meteorol. 105:221-252.
Barthelmie RJ,(1999) The effects of atmospheric stability on coastal wind climates.
Meteorol. Appl. 6:39–47.
Davies PA, (2000) Development and mechanisms of the nocturnal jet. Meteorol. Appl.
7:239–246.
Draxl C, Hahmann AN, Pena Diaz A, Giebel G (2012) Evaluating winds and vertical
wind shear from Weather Research and Forecasting model forecasts using seven
planetary boundary layer schemes. Wind Energy n/a–n/a. doi: 10.1002/we.1555.
Deppe AJ, Gallus WA, Takle ES (2013) A WRF Ensemble for Improved Wind Speed
Forecasts at Turbine Height. Weather and Forecasting 28:212–228. doi:
10.1175/WAF-D-11-00112.1.
Emeis S (2009) A simple analytical wind park model considering atmospheric stability.
Wind Energy 13:459–469. doi: 10.1002/we.367.
175
Fagan B, Chang M, Knight P, Schulz M, Comings T, Hausman E,Wilson R (2012) The
potential rate effects of wind energy and transmission in the Midwest ISO Region.
Synapse Energy Economics, Inc. report downloaded May 2013. [Available online at
http://cleanenergytransmission.org/wp-content/uploads/2012/05/]
Gallus Jr WA, Deppe AJ (2011) Wind ramp events at an Iowa wind farm: A climatology
and evaluation of WRF ensemble forecast skill.
General Electric Energy, cited 2009: 1.5 MW Wind Turbine. [Available online at
www.ge-energy.com/wind.]
Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit
treatment of entrainment processes. Monthly Weather Review 134:2318–2341.
Horton R, Kluitenberg GJ, Bristow KL, (1994). Surface crop residue effects on the soil
surface energy balance (PW Unger, Ed.). In Managing Agricultural Residues
(pp.143-162). Boca Raton, Lewis Publishers.
-----, Bristow KL, Kluitenberg GJ, Sauer TJ (1996) Crop residue effects on surface
radiation and energy balance – Review. Theor. Appl. Climatol. 54:27-37.
Iowa State University Extension (2005) Resource conservation practices: Tillage
management and soil organic matter. Publication PM1901 April 2005. Accessed
March 2012. [Available online
http://www.extension.iastate.edu/Publications/PM1901I.pdf]
Janjić ZI (2002) Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in
the NCEP Meso model. NCEP office note 437:61.
Kelley ND, Shirazi M, Jager D, Wilde S, Adams J, Buhl M, Sullivan P, Patton E. (2004)
Lamar low-level jet project interim report. National Renewable Energy Laboratory
technical report NREL/TP-500-34593.
-----, Jonkman BJ, Scott GN (2006) The Great Plains turbulence environment: its
origins, impact and simulation. National Renewable Energy Laboratory technical
report Conference paper NREL/CP-500-400176 for AWEA 2006 WindPower
Conference, Pittsburgh, Pennsylvania, 4-7 June 2006.
Marquis M, Wilczak J, Ahlstrom M, et al. (2011) Forecasting the wind to reach
significant penetration levels of wind energy. Bulletin of the American
Meteorological Society 92:1159.
176
Nakanishi M, Niino H (2009) Development of an Improved Turbulence Closure Model
for the Atmospheric Boundary Layer. Journal of the Meteorological Society of Japan
87:895–912. doi: 10.2151/jmsj.87.895
Meyers J, Meneveau C (2012) Optimal turbine spacing in fully developed wind farm
boundary layers. Wind Energy 15:305–317. doi: 10.1002/we.469
Pleim JE (2007) A Combined Local and Nonlocal Closure Model for the Atmospheric
Boundary Layer. Part II: Application and Evaluation in a Mesoscale Meteorological
Model. Journal of Applied Meteorology and Climatology 46:1396–1409. doi:
10.1175/JAM2534.1
Park J, Basu S, Manuel L (2013) Large-eddy simulation of stable boundary layer
turbulence and estimation of associated wind turbine loads. Wind Energy n/a–n/a.
doi: 10.1002/we.1580
Pichugina, Y. L., and Banta, R. M., (2010) Stable boundary layer depth from highresolution measurements of the mean wind profile. J. Appl. Meteor. Climatol., 49,
20–35.
Rajewski DA, Takle ES, Lundquist JK, Oncley SP, Prueger JH, Horst TW, Rhodes ME,
Pfeiffer R, Hatfield JL, Spoth KK, Doorenbos RK,2013a. CWEX: Crop/Wind
Energy Experiment: observations of surface-layer, boundary-layer and mesoscale
interactions with a wind farm Bull. Am. Meteorol. Soc. In press
(doi:10.1175/BAMS-D-11-00240).
-----,2013b. Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm. To
be submitted to Agric. and Forest Meteorol.
-----,2013c. Air temperature and heat flux measurements within two lines of turbines in
a large operational wind farm. To be submitted to Bound.-Layer Meterol.
Segal, M, Arritt RW, Heim JH, (1993) A simple and efficient scheme for performing
one and two dimensional numerical model sensitivity experiments. Mon. Weather.
Rev., 121, 1871-1873.
Shin HH, Hong S-Y (2011) Intercomparison of Planetary Boundary-Layer
Parametrizations in the WRF Model for a Single Day from CASES-99. BoundaryLayer Meteorology 139:261–281. doi: 10.1007/s10546-010-9583-z
Sim C, Basu S, Manuel L (2009) The Influence of Stable Boundary Layer Flows on
Wind Turbine Fatigue Loads. Proceedings of the 47th AIAA Aerospace Sciences
Meeting Including The New Horizons Forum and Aerospace Exposition. Orlando,
FL, USA
177
Storm B, Basu S (2010) The WRF Model Forecast-Derived Low-Level Wind Shear
Climatology over the United States Great Plains. Energies 3:258–276. doi:
10.3390/en3020258
Stull, R (1988) An Introduction to Boundary Layer Meteorology. Kluwer Academic
Publishers, 666 pp.
Sukoriansky S, Galperin B (2008) Anisotropic turbulence and internal waves in stably
stratified flows (QNSE theory). Physica Scripta T132:014036. doi: 10.1088/00318949/2008/T132/014036
Tardiff R, Hacker JP (2006) Description of the WRF-1d planetary boundary layer
model. [Available online at
http://sisla06.samsi.info/compmod/climate/group4/josh3.pdf]
Tegen S, Lantz E, Hand M, Maples B, Smith A, Schwabe P (2013) 2011 Cost of Wind
Energy Review. National Renewable Energy Laboratory technical report NREL/TP5000-56266
Traiteur JJ, Callicutt DJ, Smith M, Roy SB (2012) A Short-Term Ensemble Wind Speed
Forecasting System for Wind Power Applications. J. Appl. Meteorol. and Climat.
51:1763–1774. doi: 10.1175/JAMC-D-11-0122.1
US DOE (2008): 20% Wind Energy by 2030: Increasing Wind Energy’s Contribution to
U.S. Electricity Supply. July 2008. [Available online at
http://www.20percentwind.org/20percent_wind_energy_report_revOct08.pdf].
Walter K, Weiss CC, Swift AHP, et al. (2009) Speed and Direction Shear in the Stable
Nocturnal Boundary Layer. Journal of Solar Energy Eng. 131:011013. doi:
10.1115/1.3035818
Wharton S, Lundquist JK (2012) Assessing atmospheric stability and its impacts on
rotor-disk wind characteristics at an onshore wind farm. Wind Energy 15:525–546.
doi: 10.1002/we.483
Zhou B, Chow FK (2011) Turbulence Modeling for the Stable Atmospheric Boundary
Layer and Implications for Wind Energy. Flow, Turb. Combust. 88:255–277. doi:
10.1007/s10494-011-9359-7
178
Figure 1 (a) rated power curve for the GE 1.5 MW SLE and XLE turbine and (b)
calculated power coefficient using the GE 1.5 MW SLE power curve
179
C-plow
C-mulch
C-no-till
S-plow
S-mulch
S-no-till
14
0.3
12
0.25
10
0.2
8
Surface roughness, z0 (m)
Geostrophic wind speed class (m/s)
Wind category
0.15
6
0.1
4
0.05
2
0
0
Jan
Feb March April May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 2. Seasonal changes in wind speed category determined from monthly
average wind speed maps from the Iowa Energy Center and changes in roughness
according to a typical field tillage and planting schedule for corn and soybean with
plowed soil, mulch till, and no-till surfaces during the off-season.
180
Figure 3. Nightly progression of the low-level wind speed profile for the YSU
(cyan) MYJ (red) QNSE (green), MYNN2.5 (blue) and mean of the schemes (black).
Snapshots correspond to 2000 (LST) in (a), 2200 (LST) in (b), 0200 (LST) in (c),
0700 (LST) in (d), 0900 (LST) in (e), and 1100 (LST) in (f) .
181
Figure 4. As in Figure 3 but zoomed in on the 40 to 120-m rotor layer surface (solid
thick line), soybean surface (thick dashed line), no-tilled surface (solid thick line),
soybean surface (thick dashed line), and corn surface (thin line with X stiple).
182
Figure 5. Normalized power change from the plowed soil condition according to
the rotor layer stability for a geostrophic speeds of 6 m s-1 for (a) no-till, (b)
soybean, and (c) maize roughness and for a geostrophic speed of 10 m s -1 for (d) notill, (e) soybean, and (c) maize PBL and ensemble colored boxes same as thick
colored lines of Figure 3.
183
Figure 6. Normalized power difference from plowed soil condition and
accumulated power difference for a 48-hr period to analyze PBL scheme sensitivity
to roughness for geostrophic wind speed categories of (a) 6 m s-1, (b) 10 m s-1, and
(c) 16 m s-1 . Standard deviations of the PBL ensemble averaged quantities are
denoted with black error bars.
184
Figure 7. Daily net revenue change from plowed soil condition vs. roughness for
each PBL scheme and the ensemble mean for speed categories of (a) 6 m s-1, (b) 10
m s-1, and (c) 16 m s-1 and annual net revenue change for (d) climatological wind
year. The standard deviations of the PBL ensemble averaged quantities are
denoted with black error bars.
185
Figure 8. Combination scenarios of (a) annual revenue for the climatological wind
year for multiple crop type corn (C) and soybean (S) and for tillage methods (plow,
mulch, and no-till) and for (b) the 30-yr lifetime of the wind farm for various
sequence differences of crop rotation and tilling methods as in (a). The standard
deviation of the PBL ensemble averaged quantities are denoted with black error
bars.
186
Table 1. Average (maximum) hub height speeds for each PBL scheme and the
ensemble averages for geostrophic speed categories from 6 m s-1 to 16 m s-1.
Hub-height speed average (maximum) [m s-1]
Wind speed
category
Vg=6 m/s
Vg=8 m/s
Vg=10 m/s
Vg=12 m/s
Vg=14 m/s
Vg=16 m/s
YSU
5.1 (5.5)
6.3 (6.8)
7.7 (8.3)
9.1 (9.7)
10.7 (11.3)
12.0 (12.7)
MYJ
5.1 (6.5)
6.5 (8.5)
7.9 (9.6)
8.9 (10.6)
10.4 (12.1)
11.4 (13.3)
QNSE
6.0 (8.0)
6.5 (8.3)
7.9 (8.9)
8.4 (9.5)
9.8 (10.8)
10.6 (11.6)
MYNN2.5
6.0 (7.4)
7.3 (8.9)
8.7 (10.1)
9.6 (11.2)
10.6 (11.8)
11.5 (12.4)
PBL_MEAN
5.5 (6.9)
6.7 (8.1)
8.0 (9.2)
9.0 (10.3)
10.3 (11.5)
11.4 (12.6)
Table 2. Comparison of model surface roughness (z0) to characteristic roughness
type in a cropland agricultural setting.
z0 (m)
0.005
0.0075
0.01
0.025
0.05
0.075
0.1
0.25
0.5
Surface roughness
type
plowed smooth soil
plowed mulch soil
short grass
no-till
soybean canopy
maize canopy
sweet sorghum
187
Table 3. Average power (a) and maximum power (b) for the 48-hr simulations for
each PBL scheme and speed category for the baseline smooth soil condition
(z0=0.005 m).
a)
P_avg (MW)
Vg=6 m/s
Vg=8 m/s
Vg =10 m/s
Vg =12 m/s
Vg= 14 m/s
Vg=16 m/s
YSU
0.10
0.19
0.38
0.63
0.90
0.99
MYJ
0.11
0.23
0.42
0.59
0.84
0.94
QNSE
0.19
0.25
0.42
0.50
0.76
0.88
MYNN2.5
0.18
0.33
0.55
0.71
0.88
0.97
AVG
0.1
0.4
0.6
0.6
0.8
0.9
STDEV
0.0
0.1
0.1
0.1
0.1
0.0
b)
P_max (MW)
Vg=6 m/s
Vg=8 m/s
Vg =10 m/s
Vg =12 m/s
Vg= 14 m/s
Vg=16 m/s
YSU
0.13
0.23
0.44
0.74
0.99
1.07
MYJ
0.21
0.53
0.71
0.90
1.05
1.08
QNSE
0.44
0.44
0.60
0.71
0.94
1.02
MYNN2.5
0.32
0.60
0.83
0.99
1.03
1.06
AVG
0.3
0.6
0.8
0.8
1.0
1.1
STDEV
0.1
0.2
0.1
0.1
0.1
0.0
188
Table 4. Tillage crop type combination for three tillage methods (plow, mulch, notill) and two crops corn (C) and soybean (S) to evaluate the effect of seasonal
change of roughness on the annual production of wind power.
Tillage before/after planting
for one year of production
plow- C- plow
plow- C- no-till
plow-C-mulch
plow- S-no-till
plow- S- mulch
plow -S-plow
mulch- C- no-till
mulch- C- mulch
mulch- C- plow
mulch- S-no-till
mulch-S-mulch
mulch-S-plow
no-till- C-no-till
no-till- C-mulch
no-till- C-plow
no-till- S- no-till
no-till-S-mulch
no-till- S-plow
189
Table 5. Tillage and crop-type combinations for two or three year sequences
corresponding to the effect of roughness over an assumed 30-yr lifetime of the wind
farm.
Tillage/crop rotation
sequence
for 30-yr lifetime of wind
farm
C-plow
S- plow
C-S plow
C-C-S plow
C-mulch
S -mulch
C-S mulch
C-C-S mulch
C - no till
S -no-till
C-S no-till
C-C-S no-till
C-mulch S_no-till
C-mulch C-S-no-till
190
CHAPTER 6. GENERAL CONCLUSIONS
General Discussion
This dissertation has highlighted evidence for wind turbine and wind farm
modification of crop microclimate from the CWEX measurements and has also proposed
plausible but idealized changes in wind power due to crop management and tillage
practices. In Chapter 2, I demonstrated that turbine wakes reduce daytime speed at some
distance tens of D downstream and enhance nighttime speeds for several locations
downwind of the turbine line depending on the strength of the ambient surface
stratification.
The combination surface flux and LiDAR profiling measurements
indicate flux perturbations when turbine wakes may be directly overhead of the surface
but not intersecting the surface or when the wake turbulence has reached the crop
canopy. In conditions where a turbine wake is not indicated over the surface and yet the
turbines are operating I alluded to the importance of the perturbation pressure field
between the upwind and downwind side of each turbine. Similar to a shelterbelt, the
wind turbines provide an obstacle to the flow and therefore perturbations caused by the
lower pressure on the back side of the rotor may enhance speed or TKE at the nearest
downwind station. The effect of the perturbation pressure on the line of turbine or even
an individual turbine is likely indicated in recent Ka-band radar simulations of wind
speed for a grouping of turbines (Hirth and Schroeder 2013).
As demonstrated by Wang et al. (2001) for a conceptual framework of windbreak
flow, the turbines deflect air that is not passing through the rotor and so the depletion of
momentum in the wake is balanced by an over-speeding of air around the edges of the
turbines. Likewise, the conservation of momentum determined from the wind break
studies requires that the loss of momentum to drag, electric production, and other turbine
inefficiencies causing friction must be balanced by the generation of turbulence.
This
conceptual pattern additionally alludes to the presence of convergence/divergence for
various distances downstream of turbines.
If for both in a dry and wet year the CWEX-
10/11 measurements indicate similar flow perturbations for the first station north of the
191
turbine line, then it is very likely aerodynamic changes to the wind field are attributed to
both the turbine line and each wake zone behind a turbine.
Additional variations of
flow aerodynamics by turbine wind speed, wind direction and stability with both surface
and LiDAR profiling data should be jointly analyzed in detecting wake impacts on
crops.
In Chapter 3, I addressed the modification of crop energy and CO2 fluxes by wind
turbines and wind farms and presented a refined period of characteristics day and night
periods for different turbine wake positions from the first downwind tower. These
positions lined up favorably from the determination of the wake wind direction windows
presented in Chapter 1. Daytime fluxes of water vapor appear slightly increased on the
downwind side of the turbines, and the turbines also enhance the downward flux of CO 2,
presupposing that there is increased drawdown into the canopy (higher GPP). Daytime
heat flux does not respond to the turbine-scale of mixing but is rather perturbed by larger
boundary eddies especially when winds are near the turbine cut-in speed. At night the
sensible heat is directly influenced by the turbine mixing of above canopy air and the
turbine mixing releases higher than ambient CO2 from the crop (larger Re). This chapter
also revealed some of the subtleties of distance from the turbine and the lateral position
within the wake on surface modification of the flux. For the case of wind direction of
flow between two turbine wakes the measurements corroborate previous field studies of
higher turbulence intensity on the edges of the wake than in the center. The study also
demonstrated the importance of measuring crop fluxes on the same managed cropland
for both the upwind and downwind side of a line of turbines. Field-scale heterogeneities
particularly in a dry year amplify differences in the fluxes as the CWEX team observed
high flux variability in the 2011 year with three downwind stations in two differently
managed corn fields.
The statistical analyses of the downwind-upwind differences in flux suggest strong
confidence that there is higher nighttime respiration north of the turbine line than for the
daytime assimilation. The daytime and nighttime partitions of the CO2 energy flux
192
demonstrate slightly lower differences that what is reported for flux differences between
differently one irrigated and one dry-land corn crop (e.g. Suyker et al. 2004). However
the plausible changes to the flux do not demonstrate a substantial yield difference.
Additional measurements of the in-canopy biophysical processes are needed to better
address the question if and how turbines and large wind farms change crop yield.
Sampling of plant biomass and recording of canopy leaf area and other bio-physiological
parameters (e.g. biomass wet/dry weight, chemical composition) would help determine if
in-canopy CO2 concentrations are significantly different on the downwind side of
turbines. It is understood however that as a C4 crop, corn would not benefit from
increased concentrations in CO2. Rather soybean, which has a C3 pathway in its
photosynthetic production, may be responsive to elevated concentration levels created by
turbines and wind farms. Recent projects in the SOYFACE experiment (Leakey et al.
2009; Moran et al. 2009; Rascher et al. 2010) are determining the role of warmer canopy
temperatures and elevated CO2 concentrations above a canopy and the overall impact
demonstrates less fertilization benefit to the crop with warming nighttime temperatures.
In Chapter 4, I expanded upon the analysis techniques raised in previous two
chapters and applied those metrics to determine the air temperature and heat flux
differences between one or two lines of turbines. Nearly all downwind towers report the
same average flux or temperature difference as those from LES predictions and wind
tunnel modeling. There are zones of enhanced difference (a near doubling of flux from
the ambient) which indicate the flux tower measures the edge of a turbine wake.
A
high zone of speed shear along the wake is also dependent on surface stratification in
which some daytime events the wake has meandered away from the centerline direction
but may also intersect the surface within a few D downstream of the turbine line.
Conversely at night the wake may maintain its characteristic swirling vortex in the crosswind direction and yet perturb surface momentum and other scalars in weakly stable
situations. During events of strong surface cooling occurs the turbine wake remains
lofted above the surface. However, if there is evidence of vertical wind shear in the upwind profile the decoupling of surface and low-level flow induces 20-30° of veering of
193
the wind direction with height and the wake may exhibit these shared characteristics.
The strength of the hub-height wind speed demonstrates an important relationship to the
characteristics of the wake expansion and the amount of turbulence generation within the
wake. Nighttime perturbations of temperature or heat flux at the near wake tower were
repeated at the firs flux tower north of two turbine lines. A near doubling of ambient
flux in this location is reported slightly higher than the existing results from prior field
and modeling studies. The conceptual results from LES and wind tunnels describe a
decrease in scalar flux for a deep-wind farm effect of wind passing through several lines
of wind turbines. I offered a preliminary scope of CWEX measurements for SE wind
direction, which indicates a plausible pattern of reduced flux differences when multiple
turbine wakes are passing through several turbine lines. Additional measurements are
needed within the middle or far end of the wind farm to justify this claim.
The patchwork changes in roughness and scalar flux over the study area were an
additional impetus to the study of cropland management on wind power as presented in
Chapter 5. I simulated daytime reductions in power for tall tillage or crop surfaces and
only sight improvements in the nighttime wind resource when the jet maximum was
located near the hub height or top of the blade travel pass (e.g. 120 m).
Low surface
roughness as in the case of a mulched surface provides an optimal agricultural
management to increase both daytime and nighttime power resources. Not all roughness
categories occur continuously over the calendar year and therefore during the summer
when hub height speed is near the cut-in, a tall crop depletes the wind resource much
less than during the off season with hub height speeds approaching a rated speed for a
given turbine power specification. Wind operators may strategize for a boost in power
production shortly after crop harvest.
No-till management offers some nighttime
enhancement to wind power but a slight daytime reduction.
Wind operators could
increase land lease payments by a few thousand dollars per year per turbine if each
turbine would have an increase in yearly revenue of $15,000 to $30,000 per year from
the land-owner applying short height, mulched tillage. Such savings over the lifetime of
the wind farm may equate to several hundred thousand if not millions of dollars per
194
turbine. This revenue short fall/profit is comparable magnitude to the expected power
saving over the lifetime of the wind farm when errors in wind speed forecasting are
reduced to within <1%.
However, this conceptual model evaluates only a typical diurnal cycle for fallseason profiles of temperature and moisture and large scale forcing is absent. PBL top
wind speeds are less ‘static’ and depend on the type of synoptic system and season of the
year. A more realistic measure of roughness perturbation could be applied as in the
method of Hacker (2010) or in the full 3-D WRF crop which may improve seasonal
roughness variation and crop-based heat and water transports (C.A. Anderson 2011,
personal communication. The importance of scale to the surface becomes critical in
higher resolution forecast of wind speed to improve the wind-energy resource
characterization. However, another realistic factor must be considered in this roughness
parameterization on the wind-farm scale. As mentioned in Chapter 1, the variable
structure in the leading line of turbine wakes is important to determine power departures
at succeeding downwind lines of turbines.
The aforementioned power drop (e.g.
Barthelmie et al. 2010) at the 4th turbine line within a large wind farm may be reduced
or shifted to a different line when wake flow is parameterized according to ambient-scale
turbulence that has appropriate patchwork changes in roughness and scalar flux for
different crop types.
These types of field heterogeneities could be applied to existing
LES and finite element parameterizations of wind turbine wake flow over a large wind
farm. Nevertheless, the increasingly more complex representation of turbine wakes and
wind farm aerodynamics require validation by measurements.
Recommendations for Future Research
From the results gained in the CWEX-10/11 studies, the team should develop
standards in the deployment and the post-experiment data post processing techniques.
Any additional CWEX-related experiment should have consistent instrumentation and
all like-measurements should be placed at the same height. In 2011 the daytime results
195
indicate a stronger canopy effect on turbulence, heat flux, and moisture flux from
placing the sonic anemometers at 4.5 m instead of the 6.5-m height used in 2010.
Absolute differences in temperature are slightly higher for the non-aspirated sensors
used in 2010 for the NLAE stations than for the aspirated sensors provided by NCAR for
the 2011 deployment. Water vapor flux measurements should be determined from a LiCor 7500 gas analyzer or similar product. Krypton hygrometers are sensitive to canopy
dew and the high moisture content above a crop canopy and there is possible electrical
noise interference within the wind-farm environment. Perhaps in future experiments the
sonic anemometer could be placed higher (at 8 m) to measure less canopy turbulence
and instead more contributions from turbine-turbulence.
However, the higher
displacement would require a substantial increase in horizontal fetch. Unfortunately, a
uniform fetch of 800 m upwind of the reference station is difficult to control with mixed
crop rotation practices surrounding a particular parcel of land in which measurements
are desired. In addition, an upwind fetch from the downwind flux station would require
the position of that tower to possibly be in another field that does not have the same
management practices as the upwind station.
Another consideration is the time interval of measurement sampling and output
archived in CWEX data sets. In 2010, 5-minute averaged quantities were saved for all
the temperature and wind speed probes and the 20-Hz data was archived for the sonic
and gas analyzers. In 2011, 1-Hz information was retained for all the sensors except for
the sonic and gas analyzer, which output at 20 Hz.
There is a wide range of temporal
scales to consider from the CWEX datasets. In Chapter 2 and 3, 15-minute and 30minute flux calculations were developed to correspond to the aerodynamic and scalar
flux variations during the experiment. These large-averaged temporal variations in flux
did not give an adequate depiction of rapid turbine adjustments to changes in wind speed
and direction. In Chapter 4, I used the 10-minute averages to better collocate changes in
the surface flux to turbine SCADA information also collected in 10-minute intervals.
This partitioning of the data provides a larger pool of samples for the statistical testing of
heat flux and temperature differences among three stratification regimes (daytime-
196
neutral, daytime-unstable, nighttime-stable).
However, the high frequency 20-Hz data
may further unlock clues to the spatial and temporal variation of turbine-turbulence as
dependent on the aforementioned factors of ambient hub-height wind speed, wind
direction, and thermal stratification. A case study period of the overnight hours of 27-28
August in 2010 (unpublished) reveals that turbine turbulence kinetic energy and heat
flux perturbations are rapidly dissipated shortly after the beginning of a 57-minute
shutdown period. The turbines were brought offline due to lightning detection 65 miles
northwest of the wind farm. A return to a near doubling of the downward heating and
75% increase in TKE was noted at the northernmost flux tower (NLAE 4) for the rest of
the night when the turbines were taken out of curtailment. Other opportunities of
investigation of the differences in downwind-upwind fluxes of turbines on vs. off are
available from the nine day shutdown of the wind farm during a transmission upgrade
from 26 Jul to 5 Aug 2010.
There remains a substantial gap in knowledge of boundary layer scale phenomenon
(e.g. ramping events and intermittency of nocturnal turbulence) and the overall
implications on wind power and turbine reliability.
Profile tall tower and remote
sensing measurements are necessary tools in wind farm research.
Tall tower
measurements facilitate duplicate research main thrust from CWEX in optimizing crop
and wind. The data provide understanding of turbine wake aerodynamics, which are tied
to the wind turbine and wind-farm impact on crops. Additionally the profile data
enhance knowledge of the ambient wind resource in which turbine wind speed,
turbulence, and thermal stratification are linked to exchanges of momentum and scalars
between the PBL and crop canopies or soil/tillage surfaces. The benefit of the tall tower
profile measurements calibrating PBL schemes and other high-resolution simulations of
turbine wakes leads to overall understanding of surface layer, boundary layer, and
plausible mesoscale/synoptic-scale connections to wind farm flow.
An improved
science of the atmospheric interactions between crop and wind power not only decreases
error in wind forecasts but also benefits operational-scale forecasts for many industry
197
venues. The insights gained from CWEX-related studies provide a gateway to learning
about the complex energy discontinuities and other transient properties of the PBL.
References
Barthelmie RJ, Pryor SC, Frandsen ST, et al. (2010) Quantifying the Impact of Wind
Turbine Wakes on Power Output at Offshore Wind Farms. J. Atmos. Oceanic. Tech.
27:1302–1317. doi: 10.1175/2010JTECHA1398.1.
Hacker JP (2010) Spatial and Temporal Scales of Boundary Layer Wind Predictability in
Response to Small-Amplitude Land Surface Uncertainty. J. Atmos. Sci. 67:217–233.
doi: 10.1175/2009JAS3162.1.
Hirth BD, Schroeder JL (2013) Documenting Wind Speed and Power Deficits behind a
Utility-Scale Wind Turbine. Journal of Applied Meteorology and Climatology
52:39–46. doi: 10.1175/JAMC-D-12-0145.1
Leakey, ADB (2009) Rising atmospheric carbon dioxide concentration and the future of
C-4 crops for food and fuel. Proc. Royal Soc. Biolog. Sci., 276: 2333-2343.
Moran KK, Jastrow JD (2010) Elevated carbon dioxide does not offset loss of soil
carbon from a corn-soybean agroecosystem. Environ. Pollut., 158: 1088-1094.
doi:10.1016/j.envpol.2009.07.005.
Rascher U, Biskup B, Leakey ADB, McGrath JM, Ainsworth EA (2010) Altered
physiological function, not structure, drives increased radiation-use efficiency of
soybean grown at elevated CO2. Photosyn. Res., 105: 15-25.
Suyker AE, Verma, SB, Burba, GG, Arkebauer TJ, Walters DT, Hubbard KG (2004)
Growing season carbon dioxide exchange in irrigated and rainfed maize. Agric. and
Forest Meteorol., 124:1-13. Doi: 10.1016/j.agrformet.2004.01.011.
Wang, H., E. S. Takle, and J. Shen, 2001: Shelterbelts and windbreaks: Mathematical
modeling and computer simulation of turbulent flows. Ann. Rev. Fluid Mech., 33,
549-586.