Using Satellite-Derived PBL Properties
to Estimate Land Surface Fluxes
Dr. Joseph A. Santanello, Jr.
ESSIC – University of Maryland
&
NASA/GSFC Hydrological Sciences Branch
14 July 2005
Ph.D. Committee
Mark Friedl, Principal Advisor
Bruce Anderson
Guido Salvucci
Elena Tsvetsinskaya
William Kustas (USDA-ARS)
Motivation
• Land surface energy balance is an important component of atmospheric and
hydrologic models, the global water cycle, and the Earth’s radiation budget:
Rnet – G = lE + Hs
(Net Radiation – Soil Heat Flux = Latent Heat Flux + Sensible Heat Flux)
• Observations of lE and Hs are limited to sparse networks of in-situ, point-scale
measurements from flux towers.
• The convective planetary boundary layer (PBL) grows throughout the day in
response to turbulent fluxes of heat and moisture and acts as a ‘short-term memory’
of land surface processes on regional scales.
3
The Convective PBL
Free Atmosphere
PBL
Height
Mixed
Layer
Residual Layer
20 UTC
12 UTC
14
17
4
The Convective PBL
Entrainment
h
Advection
Advection
RFD
12 UTC
14
20
17
Surface
sensible heat flux
5
The Convective PBL
• Conservation methods to solving heat budgets in the PBL cannot be
applied on diurnal timescales due to variability in entrainment and
advection.
– RFD can be significant but depends on specific humidity.
– STREAMER model (J. Key et al. 2002)
• Observable properties of the PBL are strongly related to surface
conditions and fluxes.
– Observations of the PBL can be used to estimate soil water content using a
statistical approach.
6
The Convective PBL
• Examine properties that are not directly included in the conservation equation:
–
Maximum PBL Height (h)
· responds directly to the flux of heat
into the PBL
–
Atmospheric Stability (g)
· dq/dz: change in pot. temperature
with height
–
Soil Water Content (w)
· controls partitioning of surface fluxes
–
h
g
Change in 2-meter potential temperature (Dq2m)
· sensitive to heat input and PBL height
qi
qf
7
Relationship of stability, soil moisture, 2m pot. temperature and specific humidity to PBL height
Stability
h(m)
Soil water content
h(m)
g (K m-1)
w (% vol)
2m Sp. humidity
2m Pot. temp
h(m)
h(m)
Dq2m (K)
Dq2m (g kg-1)
8
w (% vol)
-1)
g (K m(K/m)
9
Predictions of PBL height using trend surfaces and observations of soil moisture, stability and 2m
potential temperature change.
R2 = 0.85
10
Motivation
• Remote Sensing now offers the ability to monitor conditions in the lower
troposphere on diurnal timescales with unprecedented spectral resolution.
– Vertical temperature profiles are retrieved
from observed radiances in a number of
spectral bands/channels.
– MODIS and AIRS have a high spatial and
diurnal resolution compared to synoptic
radiosonde networks.
– Potential for remotely-sensed estimates
of PBL structure to serve as a surrogate for
land surface conditions.
11
Background
‘Original expectation’ of MODIS and AIRS temperature retrievals based on support product
levels applied to observed radiosonde measurements.
12
Background
• ARM-SGP Central Facility (Lamont, OK)
– PBL data
· Radiosondes launched 4 times daily: 6:30am, 9:30am,
12:30pm, 3:30pm
· Profiles of temperature, humidity, pressure, and wind (10m)
** Storage, advection, mixed-layer temperature
– Land surface data
·
·
·
·
Bowen ratio flux towers
Surface meteorological data
5 soil moisture probes (0-5 cm)
Averaged across three sites surrounding the central facility
** Surface fluxes, soil moisture
13
Background
• MODIS and AIRS Data
– 44 clear days in JJA 2003.
– Temperature profile retrievals (L2) and cloudcleared (L1B) radiances.
– 2 complete soil dry-downs
– Daily PBL heights from 300 - 3650 m.
Methodology:
a)
First-order evaluation of MODIS/AIRS temperature profiles in the lower
troposphere using ARM-SGP radiosonde data.
b)
Derive key PBL properties (height, mixed-layer temperature, stability) from
MODIS/AIRS.
c)
Estimate land surface conditions and fluxes from satellite-derived properties.
14
MOD-T Evaluation
Profiles of potential temperature retrieved from radiances measured by MOD-T compared with those
measured by radiosondes at the ARM-SGP site.
MOD-T
1130 UTC
2330 UTC
MOD-T
1130 UTC
2330 UTC
h (m)
h (m)
Theta (K)
5 July 2003 1755 UTC (12:55pm)
Theta (K)
10 July 2003 1635 UTC (11:35am)
- MOD-T simulates the free atmosphere and ‘mean’ lapse rate of temperature well.
- Near-surface profiles are very similar to 1130 UTC soundings and no variation in PBL.
15
MOD-A Evaluation
Profiles of potential temperature retrieved from radiances measured by MOD-A compared with those
measured by radiosondes at the ARM-SGP site.
MOD-A
1130 UTC
2330 UTC
MOD-A
1130 UTC
2330 UTC
h (m)
h (m)
Theta (K)
5 July 2003 1930 UTC (1:30pm)
Theta (K)
10 July 2003 1945 UTC (1:45pm)
- MOD-A responds to the heating of the mixed-layer (bend point ~4 km).
- Spectral resolution is not sharp enough to capture mixed-layer structure of PBL height.
16
AIRS-d Evaluation
Profiles of potential temperature retrieved from radiances measured by AIRS-d compared with those
measured by radiosonde at the ARM-SGP site.
AIRS-d
1130 UTC
2330 UTC
AIRS-d
1130 UTC
2330 UTC
x
x
x
x
x
15 June 2003 1930 UTC (1:30pm)
28 July 2003 1930 UTC (1:30pm)
- AIRS-d has a negative temperature bias between 700 and 850 mb.
- As a result, AIRS-d cannot accurately represent the mixed-layer or PBL height.
17
AIRS-d Variability
500
550
600
650
Pressure (mb)
700
750
800
850
900
950
1000
300
302
304
306
308
310
312
314
316
318
320
322
324
326
Pot. Temp (K)
18
AIRS-n Evaluation
Profiles of potential temperature retrieved from radiances measured by AIRS-n compared with those
measured by radiosondes at the ARM-SGP site.
AIRS-n
1130 UTC
2330 UTC
4 June 2003 0840 UTC (4:40am)
AIRS-n
1130 UTC
2330 UTC
27 July 2003 0745 UTC (3:45am)
- Overall initial lapse rate is captured well by AIRS-n.
- Responds to stability in the mixed-layer (and presence of a residual layer).
19
Modeling Approaches
Use retrieved profiles to initialize and constrain a PBL model
–
Use AIRS-n to initialize the OSU 1-D PBL model
· Mixed layer height and structure simulated well
· h +/- 200 m at w extremes
OSU simulations of potential temperature initialized using AIRS-n
data compared with radiosonde measurements.
20
MODIS and AIRS Evaluation
AIRS
1130 UTC
2330 UTC
MODIS/AIRS
1130 UTC
2330 UTC
P
(mb)
MOD-T
P
(mb)
AIRS-d
MOD-A
AIRS-n
AIRS-d
Pot. Temp (K)
Pot. Temp (K)
- Bias correction for AIRS is not uniform and depends on PBL and free atmosphere structure.
- There still is a signal of PBL structure and evolution in the retrievals.
- The ‘bulk’ storage of heat could be estimated with more accurate AIRS-d profiles.
21
Empirical Approaches
Estimating PBL properties from AIRS profiles
– PBL Height
· Vertical change in q from the lowest (2m) level to the inflection point
(700-850 mb) of the AIRS-d profiles.
– Correlates to observed h (R2 = 0.90)
– Change in 2m-Potential Temperature
· Initial q can be estimated by extrapolating the slope of free-atmosphere
temperature retrieval to the surface.
· Final q estimated from lowest level AIRS-d estimate
– Correlates with observed Dq2m (R2 = 0.72).
– Stability
· AIRS-n profiles correlate with observed g (R2 = 0.61).
22
AIRS Radiances
Investigate the correlation of Level-1B radiances with surface properties
a) Principal Component Analysis (Diak et al. 1990’s)
b) Correlation and regression of individual channels
12 hour changes in AIRS radiances on three days in 2003 along with observed PBL
and surface variables
h
7 July
w
2695 5
Hs
g
182 .004
Storage
210
14 June 290 18
80
.02
-52
3 June 1291 23
15 .006
-104
23
AIRS Radiances
a)
Principal Component Analysis (PCA)
– Need to reduce the data (2085 channels):
·
Use PCA and eigenvector approach (Diak et al. 1994)
·
Correlate and regress PC’s against PBL variables of interest
· Results:
– PC’s are loosely correlated with surface temperature.
– Regressions explain less than 60 percent of the variance in surface
fluxes.
24
AIRS Radiances
12-h changes in AIRS Radiances
w
Hs
lE

Rn
G
g
Dq2m
h
Storage-1
Storage-2
PC1
-.29
.50
.04
.30
.56
.47
-.19
.56
.41
.36
.36
PC2
-.57
.34
-.42
.35
.03
-.03
.08
.19
.09
.07
.16
PC3
-.11
.08
-.22
.07
.04
.02
-.01
-.04
.05
-.01
-.07
R2
.43
.37
-----.04
.36
.19
---
AIRS-d Radiances
w
Hs
lE

Rn
G
g
Dq2m
h
Storage-1
Storage-2
PC1
-.64
.64
-.10
.43
.59
.36
-.16
.55
.54
.40
.35
PC2
.22
-.23
.05
-.23
-.24
-.30
.03
-.23
-.15
-.30
-.35
PC3
-.31
-.09
-.26
.01
-.37
-.30
.14
-.21
-.10
-.22
-.24
R2
.55
.47
-----.09
.40
.32
---
Correlations of the PC’s computed from AIRS radiances and the and R2 values of the linear
multiple regressions of the PC’s with PBL and land surface variables (Diak et al. 1994).
25
AIRS Radiances
b) AIRS-d Radiances
– 5 channels selected
– Linear regression model predicts PBL and land surface variables
2ch-window
5ch-g
5ch-Hs
5ch-h
5c-w
5ch-Dq2m
w
.63
.81
.92
.77
.82
.43
Hs
.53
.24
.69
.49
.80
.44
.68
.53
.75
.63
.36
.49
g
.34
.09
.43
.37
.52
.20
.50
.22
.46
.25
.52
.28
WN1
933.040
667.773
667.773
667.773
667.773
651.283
WN2
2530.89
--
696.603
701.617
903.775
992.448
896.183
922.731
693.028
903.775
694.948
937.907
---
796.038
2272.92
2278.82
2519.99
2273.90
2501.67
2278.82
2519.99
2285.74
2491.02
h
Dq2m
WN3
WN4
WN5
R2 values and wavenumbers of the 5 AIRS-d channel radiances used in linear multiple
regressions with PBL and land surface variables
26
AIRS Radiances
Observed soil moisture versus that predicted by the AIRS-d 5 channel multiple regression.
w (% volumetric)
R2 = 0.92
w (% volumetric)
Three channels in the ‘window’ region + 15 mm CO2 channels that peak near 2 and 14 mb
27
Physical Rationale
- Soil drying and surface heating increase radiances from window regions of the spectrum, but
this signal obtained by AIRS is weaker than that predicted by Diak et al. (1994).
- A secondary and stronger signal exists near the 15 micron CO2 band, which is negatively
correlated with surface heating and positively correlated with soil moisture.
Why…………….?
X Direct surface signal is impossible in these channels
X Weighting functions peak too high to capture clouds or water vapor effects, confirmed by
radiosonde observations.
? Seasonal warming and raising of the troposphere from May-Sept results in an actual decrease
in ~2-14 mb temperature during the summer.
- How sensitive is this effect to variability at the surface?
- Applicable at shorter than-seasonal time scales, and other regions?
28
Summary
• MODIS and AIRS retrievals are poorly resolved in the lower troposphere,
but do contain significant information on the general structure and
evolution of the PBL that can be used to infer surface conditions.
(Much room for improvement in profile retrieval, bias removal, and vertical resolution)
• Radiances from five individual AIRS-d channels can be used to predict
surface moisture, fluxes, and PBL properties.
29
Future Research
MODIS and AIRS recommendations:
• Assess the improvement possible in retrieving PBL information using
current technology (AIRS) with different weighting functions, bend
points, etc.
• AIRS-d retrieval bias correction
· Same result seen in other regions?
· Mixed-layer assumption
· PBL model constraints
• Investigate AIRS-d channel correlations
· Other locations and conditions
· Incorporate MODIS land cover products
30
Thank You !
NOAA/CIMSS
Jeff Key
ESSIC - University of Maryland
NASA Earth System Science
Fellowship
31
Empirical Approaches
MODIS Brightness Temperatures
· Weighting functions for bands 27, 28, and 30-36 peak at different vertical
levels.
· Differences between brightness temperatures at the surface (31,32) and
these levels are related to PBL structure.
· PBL height is correlated to changes in band differentials
400mb
650mb
980mb
40
875mb
Change in Brightness Temp (K)
30
780mb
20
950mb
10
0
-10
0
500
1000
1500
2000
2500
3000
3500
4000
-20
-30
-40
-50
-60
PBL Height (m)
35