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A Case Study on the Effects of Heterogeneous Soil Moisture on Mesoscale
Boundary Layer Structure in the Southern Great Plains, U.S.A. Part II:
Mesoscale Modelling
Brian P. Reen
Department of Meteorology, The Pennsylvania State University, University Park, PA, U.S.A.
David R. Stauffer
Department of Meteorology, The Pennsylvania State University, University Park, PA, U.S.A.
Kenneth J. Davis
Department of Meteorology, The Pennsylvania State University, University Park, PA, U.S.A.
Ankur R. Desai
Department of Meteorology, The Pennsylvania State University, University Park, PA, U.S.A.
Corresponding Author:
Brian Reen
Department of Meteorology
The Pennsylvania State University
503 Walker Building
University Park, PA 16802
U.S.A.
Telephone: 814-863-1036
Fax: 814-865-3663
Email: reenb@meteo.psu.edu
Running title: Effects of soil moisture on ABL
Article type: Article
Keywords: Boundary layer depth, land surface heterogeneity, mesoscale modelling, soil
moisture
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ABSTRACT
The importance of soil moisture inputs and improved model physics in the prediction
of the daytime boundary-layer structure during the Southern Great Plains Hydrology
Experiment 1997 (SGP97) is investigated using the nonhydrostatic Fifth-Generation
Pennsylvania State University / National Center for Atmospheric Research (PSU/NCAR)
Mesoscale Model MM5. This paper is part II of a two-part study examining the relationship
of surface heterogeneity to observed boundary layer structure. Part I focuses on observations
and utilizes a simple model while Part II uses observations and MM5 modelling. Soil
moisture inputs tested include a lookup table based on soil type and season, output from an
offline land surface model (LSM) forced by atmospheric observations, and high-resolution
(≈800 m) airborne microwave remotely sensed data. Model physics improvements are
investigated by comparing a LSM directly coupled with the MM5 to a simpler force-restore
method at the surface. The scale of land surface heterogeneities is compared to the scale of
their effects on boundary layer structure.
The use of more detailed soil moisture fields allowed the MM5 to better represent the
large-scale (~100’s of km) and small-scale (~10’s of km) horizontal gradients in surface layer
weather and to a lesser degree, the Atmospheric Boundary Layer (ABL) height, which was
evaluated against observations measured by Differential Absorption Lidar (DIAL). The
benefits of coupling a LSM to the MM5 were not readily evident in this summertime case,
with the model having particular difficulty simulating the timing of maximum surface fluxes
while underestimating the depth of the mixed layer.
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1. Introduction
It has long been known that surface moisture is a potentially important surface
characteristic affecting boundary layer structure (e.g., Zhang and Anthes, 1982). Surface
moisture content influences the partitioning of energy between sensible heat flux (SHF) and
latent heat flux (LHF), which in turn helps to determine Atmospheric Boundary Layer (ABL)
properties such as temperature, moisture, and ABL depth (e.g. McCumber and Pielke, 1981;
Pan and Mahrt, 1987). Soil moisture can be a factor in cloud formation (Ek et al., 2004), and
correctly representing soil moisture heterogeneity can be important in determining where
moist convection occurs (Lanicci et al., 1987) and can even noticeably impact situations with
stronger forcing such as a frontal passage (Fast and McCorcle, 1991). Modelling research
suggests spatially varying soil moisture can create mesoscale circulations which may result in
rainfall (e.g. Anthes, 1984; Yan and Anthes, 1988; Lynn et al., 1998). Mesoscale circulations
due to land surface heterogeneities have been observed (e.g. Mahrt et al., 1994; LeMone et
al., 2002) but the degree to which such circulations commonly influence regional
meteorology has been questioned (Shaw and Doran, 2001). Famiglietti et al. (1998)
identified soil properties, topography, type of vegetation, density of vegetation, mean
moisture content, solar insolation, water table distance below the surface, and the magnitude
of precipitation as factors that can be sources of soil moisture variability. Further study of the
impacts of soil moisture variability on boundary layer structure and heterogeneity, and our
ability to model such land-atmosphere interactions will improve our ability to design new soil
moisture observing systems as well as our ability to construct effective modelling
frameworks that utilize these observations.
Although soil moisture content is one of several variables that can be important in
determining the partitioning between SHF and LHF measured by the Bowen ratio
(SHF/LHF), other surface characteristics can also be important. Soil type is potentially
important as it determines important parameters such as wilting point and field capacity (e.g.
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Wilson et al., 1987). In vegetated areas, Sun and Bosilovich (1996) note that aerodynamic
roughness length, canopy resistance to water transfer, root density (or distribution), and leaf
area index (LAI) are among the factors proposed to influence the Bowen ratio. The LAI (the
number of layers of vegetation in a vertical column) influences the interchange of radiation
and moisture among the soil, vegetation, and atmosphere (e.g. Pitman, 1994), with increases
in LAI typically correlated with increased transpiration and decreased sensible heat flux
(Crawford et al., 2001).
While spatial variations in surface flux partitioning through factors such as soil
moisture heterogeneities can cause heterogeneities in ABL properties (depth, moisture, and
temperature; Desai et al., 2004 [hereafter referred to as Part I]; Doran and Zhong, 1995),
other factors such as convergence, shear production of turbulence, and horizontal advection
can also be important (Zhong and Doran, 1995). In addition, Mahrt (2000) indicates that the
influence of surface heterogeneity is affected by various factors when he conjectures that the
height at which a surface heterogeneity ceases to significantly affect the ABL is a function of
the size of the heterogeneity, wind speed, thermal structure, updraft strength, and ABL depth.
Since soil moisture observations are usually not available at high spatial and temporal
resolution, one must determine a reasonable way to represent initial condition soil moisture in
models. Some intensive study areas provide soil moisture data through in-situ measurement,
such as the Oklahoma Mesonet (Brock et al., 1995), and some provide soil moisture data
through remotely sensed data (e.g. Jackson et al., 1999), but data density and areal coverage
are limited. Satellites such as the Special Sensor Microwave/Imager (SSM/I; Lakshmi et al.,
1997; Jackson et al., 2002) and more recently the Advanced Microwave Scanning
Radiometer (AMSR-E) on the Earth Observing System Aqua satellite (Njoka et al., 2003) are
sensitive to soil moisture and can cover large areas, but with limited spatial and temporal
resolution. Some models, such as the Fifth Generation Pennsylvania State University /
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National Center for Atmospheric Research Mesoscale Model, known as MM5 (Dudhia, 1993;
Grell et al., 1994), can use values of surface moisture from a lookup table (Dudhia et al.,
2000) based on land surface type and season, while other models use lookup tables based on
monthly climatological values (van den Hurk et al., 1997). Given past observed precipitation
and assuming a drying rate, one may attempt to diagnose current soil moisture through an
antecedent precipitation index (API; Wetzel and Chang, 1988).
Land surface models (LSMs), which represent land processes and land-atmosphere
interactions, may also be run to determine temporally and spatially varying soil moisture.
Capehart and Carlson (1994) demonstrate that an offline LSM forced by observed
meteorological conditions over a past time period produced accurate soil moisture values that
could then be used as initial conditions for an atmospheric model (Smith et al., 1994). As
will be described in more detail later in this paper, this study uses remote-sensing, a lookup
table, as well as an offline LSM to determine soil moisture for the model simulations.
In principle, the best way to represent the temporal evolution of soil moisture content
within a model is to directly couple a LSM with a mesoscale atmospheric model. LSMs such
as the Parameterization for Land-Atmosphere-Cloud Exchange (PLACE; Wetzel and Boone,
1995) and the National Center for Environmental Prediction (NCEP)/Oregon State University
(OSU)/Air Force/Office of Hydrology (NOAH) LSM (Pan and Mahrt, 1987; Chen et al.,
1996; initially the OSU LSM) have been coupled to the MM5 (Lynn et al., 2001; Chen and
Dudhia, 2001a; Chen and Dudhia, 2001b). Although some studies show improvement with
the use of coupled models (e.g. Chen et al., 2001), a LSM’s input requirements and
uncertainty as well as the additional model physics may make it difficult for a coupled model
to outperform a simpler atmospheric model (Crawford et al., 2001). This study includes an
examination of the added value of a MM5-PLACE coupling.
Mohr et al. (2000) ran PLACE from 9-16 July 1997 during the Southern Great Plains
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1997 field program (SGP97; Jackson, 1997) over the Little Washita watershed and
investigated issues related to modelling soil moisture and surface fluxes. The initialization
scheme only accounted for soil moisture variability due to soil type, and the modelled soil
moisture did not capture variability well until after significant rain. Their results indicated
that the model sensible heat flux peaked later than observed, the peak SHF was too large with
peak LHF too small, and the soil dried too slowly.
Crawford et al. (2001) used an MM5-PLACE coupling to study various days during
SGP97. Soil moisture was initialized using the API method earlier noted. They found that
noncoupled experiments generally outperformed coupled experiments even when Advanced
Very High Resolution Radiometer (AVHRR) derived land surface data were used to improve
the coupled result. The temperatures of the PLACE runs were generally too high, surface
temperature biases on the day of their study most relevant to the current study (12 July 1997)
are -0.15°C for the default run, +2.59°C for the MM5-PLACE with climatological land
surface data, and +1.77°C for the MM5-PLACE run with AVHRR land surface information.
These results suggest that “improved” model physics may require more extensive initial
conditions and calibration in order to function better than simpler model physics.
This paper examines the benefits of both improved soil moisture data and more
sophisticated land-surface physics for numerical simulation of ABL structure during the
SGP97 time period 1200 UTC 12 July 1997 through 0000 UTC 13 July 1997. This study
covers a larger area than that of Mohr et al. (2000) and evaluates a broader range of variables
than Crawford et al. (2001). In addition, the scale of the land surface heterogeneities
compared to the scale of their effects on boundary layer structure is also investigated.
Special data used in this study include high resolution remotely sensed soil moisture
data (Electronically Scanned Thinned Array Radiometer; Jackson et al., 1995), a 2D surface
flux map derived from observations (from Part I), high resolution remotely sensed ABL
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properties (Lidar Atmospheric Sensing Experiment; Browell et al., 1997), and the Oklahoma
surface mesonet (Brock et al. 1995). Few studies have compared model with lidar-derived
ABL-depth and corresponding observed surface variables to the extent done in the current
study.
Experiments are first performed using MM5 with a simplified land-surface scheme and
three different sources of soil moisture data: 1) soil moisture based on the default lookup
table values (Dudhia et al., 2000), 2) values predicted by an offline LSM forced by
meteorological observations (Muñoz 2002), and 3) soil moisture determined from microwave
remote sensing data (Jackson et al., 1999). In addition, MM5 is coupled to a LSM (PLACE;
Wetzel and Boone, 1995) in order to examine the potential benefits of more sophisticated
land-surface physics.
Both MM5 and PLACE are described in Section 2. Section 3 presents the experimental
design by describing the sources of soil moisture used, other input changes made to the
PLACE component of MM5-PLACE, and the related numerical experiments performed with
these model configurations. An overview of the observed conditions for this ABL study day
is provided in Section 4. Section 5 discusses results, and Section 6 summarizes the paper.
2. Model Description
The MM5 modelling system used in this study utilizes inputs from an offline land-surface
model (external to the MM5 and forced by observations) in some experiments. Offline
PLACE will be discussed followed by a description of the MM5 using a Slab force-restore
model (MM5-Slab) and the PLACE land-surface model directly coupled to the MM5 (MM5PLACE).
2.1. OFFLINE PLACE
An offline version of the Parameterization for Land-Atmosphere-Cloud Exchange
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(PLACE; Wetzel and Boone, 1995) LSM is one source of input soil moisture data for this
study as well as the source for MM5-PLACE soil temperature. Results from the Project for
Intercomparison of Land-Surface Parameterization Schemes indicate that PLACE produces
adequate land-surface fields (e.g. surface radiative temperature, surface heat and moisture
fluxes, and precipitation runoff) relative to other LSM’s (Chen et al., 1997). The offline
PLACE is integrated here from 1 June 1997 over a domain with 36-km resolution covering
much of the continental United States (Figure 1). Observed meteorological conditions used to
force PLACE are obtained from the 12-hourly conventional meteorological data used to force
the Soil Hydrology Model (SHM; Capehart and Carlson, 1994) that was run real-time over
this time period at Penn State (Smith et al., 1994) along with 3-hourly surface data (Muñoz,
2002).
There are seven PLACE soil temperature levels (0-2, 2-5, 5-10, 10-15, 15- 50, 50-90,
and 90-130 cm) and five soil moisture levels (0-2, 2-5, 5-15, 15-50, and 50-130 cm) with
increased resolution near the surface. The initial soil moisture profile for offline PLACE is
based on the archived realtime SHM model output. Initial surface soil temperature is based
on air temperature and the lower boundary soil temperature is based on climatological values
used in MM5 with a vertical interpolation between these two layers to obtain the initial soil
temperature profile. Parameters such as surface roughness and albedo are obtained from a
lookup table based on land use / vegetation type used in MM5. Vegetation fraction is based
on monthly AVHRR-based climatological values as used by MM5. Surface sensible and
latent heat fluxes are computed separately for vegetated and bare-soil conditions, and the
vegetation fraction is used to compute a weighted-average flux for any given grid cell.
Some default parameters for offline PLACE are based on consultations with the
developer of PLACE, Peter Wetzel. The LAI is set uniformly to 7 over the vegetated fraction
of the cell, and root fraction is assumed to be 50 percent in the second soil moisture layer, 25
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percent in the third layer, and 25 percent in the fourth layer. Further details regarding the
setup of PLACE for this study can be found in Muñoz (2002).
2.2. MM5
The nonhydrostatic Fifth-Generation Pennsylvania State University / National Center
for Atmospheric Research (PSU/NCAR) Mesoscale Model (Dudhia, 1993; Grell et al., 1994)
MM5v3.3 is used to simulate the atmospheric response to heterogeneous surface moisture in
this study. Figure 1 shows the locations of the three one-way nested domains of 36-km, 12km, and 4-km resolution with the inner two domains centred over the Oklahoma/Kansas,
U.S.A. area that is the focus of this study. There are 62 vertical sigma layers, with the first
layer 30 m Above Ground Level (AGL), 50-m resolution through the lowest 2 km (within the
ABL), and the model top at 50 hPa. The initial conditions and lateral boundary conditions for
the outermost domain are obtained from Eta analyses enhanced by surface and rawinsonde
data via a modified successive scan objective analysis method (Benjamin and Seaman, 1985).
Analysis nudging Four Dimensional Data Assimilation (FDDA; adding a small non-physical
term to model tendency equations to nudge the model state towards an analysis; Stauffer and
Seaman, 1990, 1994; Stauffer et al., 1991) is applied on the 36-km and 12-km domains and
only above 2 km AGL (ABL-top) to provide improved large-scale lateral boundary
conditions for the 4-km domain where no FDDA is applied. The 36-km and 12-km domains
are both initialized at 12 UTC 11 July 1997, the 4-km domain started at 00 UTC 12 July
1997, and all three domains are integrated through 00 UTC 13 July 1997.
Since the study period of 12 UTC 12 July through 00 UTC 13 July 1997 was
generally dry and without significant cloudiness, the use of explicit microphysics including
simple ice processes (no mixed phases; Dudhia, 1993) and the Kain-Fritsch convective
parameterization (Kain and Fritsch, 1990) on the outer two domains is probably not
significant. The PSU 1.5-order (level 2.5) turbulent kinetic energy (TKE)-predicting scheme
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(Stauffer et al., 1999; Shafran et al., 2000) is used to represent turbulent processes and the
height at which the predicted TKE drops below a threshold value (0.1 m2s-2 if maximum TKE
in vertical column is at least 0.2 m2s-2) is diagnosed as the ABL height.
Surface latent and sensible heat fluxes as well as the ground temperature are
determined in MM5-Slab using MM5’s default force-restore land surface physics (Slab
model) that follows Deardorff (1978) and Zhang and Anthes (1982). This model has two soil
layers, a thin near-surface layer that directly interacts with the atmosphere and a deep soil
substrate layer that remains at a constant temperature. As with the offline PLACE model, the
United States Geological Survey (USGS) 24-category land-use / vegetation characterization
and the National Cooperative Soil Survey 16-category State Soil Geographic (STATSGO)
soil map are used and vegetation fraction is based on monthly climatological values based on
AVHRR data. Parameters such as roughness length, albedo, and wilting point are based on
lookup tables. The surface moisture availability in MM5-Slab is by default defined from a
lookup table based only on season and landuse type and remains constant throughout the run.
In the experimental configurations described in Section 3, additional methods for
initialization of soil moisture will be discussed.
MM5-PLACE is configured as MM5-Slab except PLACE land-surface physics (whose
offline equivalent was previously described) is used inline within MM5 (coupled) instead of
the Slab force-restore model. Previous results from this coupling (e.g., Lynn et al., 2001;
Muñoz et al., 2001, Crawford et al. 2001) have demonstrated its capabilities under timevarying soil moisture conditions and thus suggest that MM5-PLACE is a reasonable
modelling tool to use in this experiment. The MM5-PLACE is used here to investigate the
two-way interaction of the atmospheric ABL and time-varying soil-moisture heterogeneities.
3. Experimental Configurations
A set of experiments is conducted to investigate the scale response of the ABL to surface
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heterogeneity and to explore the sensitivity of the model-predicted ABL structure to the landsurface scheme (Slab or PLACE) and the source of the initial soil moisture. The general
region that is the focus of these experiments is seen in Figure 2, which also shows aircraft
transects and the area covered by the high-resolution soil moisture data described in the
following section. The sources of soil moisture and determination of two potentially
important land-surface parameters will be summarized first, followed by the MM5
experimental design.
3.1. SOURCES OF SOIL MOISTURE INFORMATION
Surface moisture in MM5-Slab is measured by moisture availability which ranges
from 0 (dry) to 1 (wet). In MM5 moisture availability is traditionally taken from a lookup
table based on land surface type and season (Dudhia et al., 2000). The moisture availability
derived by this method is used in Exp. SLABC (See Table I) and can be seen in Figure 3 to
be generally limited to three different values over most of the domain, with 97% of all grid
cells having moisture availabilities of 0.15 (72% of grid cells), 0.25 (9% of grid cells), or
0.30 (16% of grid cells). An alternate method to determine soil moisture uses the offline
PLACE model described in Section 2.1. The MM5-Slab runs not utilizing the lookup table
(Exps. SLABO and SLABOE) use a two-day average of offline PLACE soil moisture over
the simulation period (11-12 July 1997) to specify soil moisture over at least part of the
domain. In contrast, all of the coupled MM5-PLACE simulations use the offline PLACE soil
moisture only at the initial time to specify soil moisture over at least part of the domain.
The PLACE model’s soil moisture content predictions are converted to moisture
availability through the following formula derived from Giorgi and Bates (1989):
MA 
  wp
sat  wp
where M A is moisture availability,  is soil moisture content, wp is wilting point, and sat
(1)
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is saturation soil moisture. The values of wilting point and saturation soil moisture for each
soil type are from Garratt (1992). The offline PLACE is run at 36-km resolution (Figure 1),
and moisture availability is computed and interpolated to the 12-km and 4-km domains.
Figure 4 shows the offline-PLACE soil moisture availability on the 4-km domain to have
generally moister conditions to the north where it had rained previously.
During SGP97 an L-band passive microwave radiometer known as ESTAR
(Electronically Scanned Thinned Array Radiometer) was flown on a P-3 aircraft and used to
derive surface (0-5 cm) soil moisture (Jackson et al., 1995; Jackson et al., 1999). These soil
moisture data have an effective resolution of 800 m and cover a limited area approximately
10,000 km2 over Oklahoma and Kansas (Figure 2). The high-resolution soil moisture content
ESTAR data for 12 July 1997 are shown in Figure 5 and are used in all MM5-PLACE
experiments except Exp. PLACEO. For the MM5-Slab Exp. SLABOE, the ESTAR soil
moisture content is converted to soil moisture availability using the following method
described by Capehart and Carlson (1994):

  
M A  025  1  cos 
   

 fc  
2
(2)
where:
 fc  075 sat
and  fc represents field capacity. Values for saturation based on soil type are taken from
Garratt (1992). The ESTAR-derived soil moisture availability data are shown in Figure 6 and
contain considerably more detail than that in Figure 3 or Figure 4 while still showing the
large-scale north-south moisture gradient.
3.2. DEFINITION OF LSM PARAMETERS
The coupled MM5-PLACE requires the specification of subgrid scale soil moisture
(3)
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heterogeneity. The degree of heterogeneity in soil moisture content is demonstrated by
Famiglietti et al. (1999) who found that, in the same region and time period of this study and
within 0.64 km2 plots, the coefficient of variation1 for in-situ measurements of soil moisture
almost always exceeded 0.1, with a mean value of approximately 0.3. This is specified in
MM5-PLACE by assuming that the coefficient of variation of soil moisture remains constant
until a specified maximum standard deviation is reached (Wetzel and Chang, 1988). The
constant coefficient of variation and maximum standard deviation were determined based on
ESTAR variability in the 36 km MM5 grid cells to be 0.33 and 0.10 m3 m-3 respectively and
compare to values suggested by Wetzel and Chang (1988) of about 0.6 and 0.08 m3 m-3. The
ESTAR based measures of subgrid variability were used in Exps. PLACEOE_S and
PLACEOE_SN instead of the default of no subgrid variability in soil moisture used in the
rest of the MM5-PLACE experiments. During SGP97 Famiglietti et al. (1999) finds that
standard deviation decreases with increasing soil moisture content, which is contrary to the
assumption used in representing subgrid heterogeneity in PLACE and contrary to results such
as Famiglietti et al. (1998). The disagreement between ESTAR-derived statistics utilized in
PLACE and those found by Famiglietti et al. (1999) may be due to the much larger temporal
range (~one month versus ~one day) and much smaller spatial scale (49 measurements per
0.64 km2 vs. 1 measurement per 0.64 km2) studied by Famiglietti et al. (1999).
MM5-PLACE also uses vegetative fraction and leaf area index (LAI) as inputs.
Experiments that use spatially varying 4-17 July 1997 AVHRR normalized difference
vegetation index (NDVI) to replace the climatological value of vegetative fraction and the
uniform value of LAI (LAI=7) are also performed using MM5-PLACE (Exps. PLACEOE_N
and PLACEOE_SN; see Section 3.3). These data are the same used by Crawford et al.
(2001) but note that the LAI they report is based on the average LAI over each entire grid cell
whereas the value of 7 used here is based on the average LAI within only the vegetated
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fraction of each grid cell. The average vegetated NDVI-based LAI by comparison is ≈5.4.
The originally utilized LAI value of 7 appears high compared, for example, to Norman et al.
(1992) who found values generally between about 2 and 4 for LAI during 1989 in Kansas.
3.3. MM5 EXPERIMENTAL DESIGN
Table I summarizes the MM5 experimental design. The control experiment, SLABC,
uses the default MM5 configuration described in Section 2.2 with the Slab land surface
scheme (MM5-Slab) and soil moisture based on the lookup table (Figure 3). Lookup table
soil moisture is replaced by offline-PLACE soil moisture for Exp. SLABO. Experiment
SLABOE inserts ESTAR soil moisture where available, resulting in the soil moisture field
seen in Figure 6. Since the soil moisture content of the offline PLACE agrees well with that
of the ESTAR data (Figure 7), SLABOE uses offline PLACE where ESTAR is not available.
A similar pair of experiments to determine model sensitivity to ESTAR is run using the
PLACE LSM coupled with MM5 (MM5-PLACE) in Exps. PLACEO (using offline PLACE
soil moisture and temperature initial conditions) and PLACEOE (including ESTAR soil
moisture in the initial conditions). Experiment PLACEOE_S adds subgrid soil moisture
variability into MM5-PLACE (see Section 3.2) and PLACEOE_N uses NDVI-based
vegetation fraction and LAI (see Section 3.2). Experiment PLACEOE_SN adds both sub-grid
moisture variability and NDVI-based vegetation fraction and LAI.
4. Case Description
This study uses data from the Southern Great Plains 1997 (SGP97) Hydrology
Experiment (Jackson, 1997) and the Oklahoma Mesonet (Brock et al., 1995). The daytime
period 1200 UTC 12 July to 0000 UTC 13 July 1997 is chosen because of the generally cloud
free conditions and weak synoptic forcing which are desirable for a boundary layer study.
The only relevant Atmospheric Radiation Measurement Program Cloud and Radiation
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Testbed (ARM CART) sounding collocated with ESTAR coverage is that taken at the Central
Facility in Lamont, Oklahoma (location shown in Figure 2). The soundings launched at 1428
UTC, 1727 UTC, and 2027 UTC, can be seen in Figure 8 and will be referred to as the 1500
UTC, 1800 UTC and 2100 UTC soundings respectively from this point forward. The mixed
layer deepens through the day, and near the surface there is a strong superadiabatic layer. A
potential benefit of the multi-grid nesting strategy used here is illustrated in the elevated
mixed layer structure or “lid” advected from Mexico (e.g. Carlson et al., 1983) and seen
around 700 hPa in Figure 8a and Figure 8b.
On 12 July 1997 a 500 hPa ridge was in place over the eastern half of the United
States and a trough was located over the West with lower troposphere flow from the
southwest (Figure 8). The surface winds over the Oklahoma-Kansas study region were
south-southwesterly at 5-10 ms-1 and a weak trough was analyzed over western Kansas and
the panhandle of Oklahoma (not shown). This weak synoptic forcing produced minimal
cloudiness over Oklahoma and southern Kansas from 1200 UTC 12 July through 0000 UTC
13 July 1997 (e.g., Figure 8).
Oklahoma Mesonet surface temperatures at the beginning of the period (1200 UTC 12
July) ranged from 20 to 25°C (68 to 77°F) over Oklahoma (excluding the panhandle) and
reached highs from 32 to 37°C (90 to 99°F) before 0000 UTC (not shown). The mesonet did
not record any precipitation during this time period and there was no extensive cloud cover
reported. Surface winds averaged about 6 ms-1 from the south to southwest over Oklahoma.
The ESTAR data in Figure 5 indicate wetter soil in the north and dryer soil in the
south with small scale heterogeneities including an area of relatively higher soil moisture
near the southwest corner of the ESTAR data region over the Little Washita River Basin. In
Part I, examination of the 12 flux sites within the ESTAR region on 12 July indicates that the
Bowen ratio is more strongly correlated with soil moisture than NDVI and so soil moisture
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effects are more dominant in determining surface fluxes in this case than vegetative
distribution.
A 2-D map of surface LHF and SHF was developed in Part I using a linear fit
between ESTAR and station Bowen ratio. To facilitate a reasonable comparison of these
“observed” ESTAR-derived surface fluxes with MM5 4-km gridded data, the ESTARderived fluxes are averaged over the 4-km MM5 grid cells and used henceforth unless
otherwise noted. The averaged ESTAR-derived fluxes along all of the aircraft transects (see
Figure 2 for transect locations) indicate mean SHF somewhat larger than LHF with generally
higher SHF and lower LHF in the south. The P-3 aircraft that carried ESTAR made nine
transects on 12 July 1997 from 1437 UTC to 1831 UTC with the closest hour of each transect
used here in comparisons with other data.
Oklahoma Mesonet data are used with Cressman-type weighting to obtain observed
values for surface temperature and mixing ratio for each model grid cell along the transects
and within 50 km of an Oklahoma Mesonet site. The north-south surface temperature
structure is fairly consistent from transect to transect with a relative maximum between 36
and 37°N, a minimum around 35.5°N, and the maximum temperature located at the southern
end of the transect (e.g. Figure 9a, Figure 10a). Surface mixing ratio along the transects
indicates local maxima just north of 35.5° N and at about 34.75°N and a decrease in surface
moisture towards the southern extreme of the transects (Figure 9b).
The P-3 aircraft that carried ESTAR also contained the Lidar Atmospheric Sensing
Experiment (LASE; Browell et al., 1997) which is a water vapour differential absorption lidar
(DIAL). The depth of the ABL is determined from the LASE data as described in Davis et al.
(2000). The ABL depth derived from the LASE data generally showed deeper ABL depths in
the south where the mesonet surface temperatures were higher and mixing ratios were lower.
17
5. Results
Overall model accuracy in the ABL is evaluated first followed by sections which
investigate the added value of more detailed soil moisture, more sophisticated physics, and
the scale response to soil moisture heterogeneity. Results will focus on the 4-km model
domain because the ESTAR data do not cover a significant portion of either the 12-km or 36km domain.
The ESTAR-derived fluxes averaged to MM5 grid cells indicate latent heat flux
peaking approximately 2.5-h later than sensible heat flux in the average over all 12 flux sites
(Table II). The site-averaged maximum three-hour mean (MTHM) SHF was observed to
occur at 1730 UTC compared to Slab runs that peak on average at about 1800 UTC (≈0.5 h
late) and PLACE runs that peak at about 1900 UTC (≈1.5 h late). Slab model results were
consistent with observations of site-averaged MTHM LHF occurring at ≈2000 UTC whereas
PLACE model runs indicate a peak between 1600 and 1700 UTC (3-4 h early). Table II also
shows that MTHM SHF is overestimated by 44 to 104 W m-2 with PLACE runs generally
producing larger SHF than the Slab runs. LHF MTHM is overestimated by inline PLACE by
22 to 56 W m-2 whereas the Slab runs range from being nearly correct for SLABC to being
underpredicted by 71 W m-2 for SLABOE.
The observed and model mean values of ABL height over all nine aircraft transects
are listed in Table III and the mean errors are shown in Table IV. The observed LASE ABL
height over all nine transects (1500-1800 UTC) averages 1084 m but all of the model
experiments underestimate this value by 121 to 368 m. The Slab runs predict mean ABL
heights better than the inline PLACE experiments. The Slab run that utilizes ESTAR
(SLABOE) does the best in predicting mean ABL depth with a mean error of -121 m while
the subgrid inline-PLACE experiments (PLACEOE_S and PLACEOE_SN) produce the
largest errors and strongly underpredict the height of the ABL with mean errors of -368 and -
18
362 m respectively.
Based on Oklahoma Mesonet data, Table IV shows the mean surface temperature
error along the transects ranges from -1.0 to +1.3 °C for all experiments, and all experiments
underpredict the transect-mean surface water mixing ratio by 0.4 to 2.8 g kg-1. This surface
bias in the model may be partially due to the strong vertical gradient in mixing ratio near the
surface related to the superadiabatic layer that can not be resolved well by the model (Figure
8). Model results range from underpredicting ABL average LASE mixing ratio by 0.6 g kg-1
to overestimating ABL average mixing ratio by 1.1 g kg-1.
Table IV shows that the experiment that best predicts ABL height (SLABOE) still
underpredicts it by 121 m and yet overpredicts surface sensible heat flux by 55 W m-2 and
surface temperature by 1.3 °C. Comparison of the closest observed sounding (Central Facility
at Lamont, Oklahoma, Figure 8) with the skew-T from Exp. SLABOE (not shown), indicates
that the moisture in the ABL is well mixed (except very near the surface) whereas the model
skew-T moisture profile dries somewhat with height in the boundary layer. This moisture
profile suggests that the model is not mixing as vigorously as the atmosphere on this day and
this may have resulted in lower modelled ABL heights. Other work has shown that
increasing the mixing length scales within the TKE scheme can reduce this tendency of the
model to underestimate ABL mixing (Schroeder, 2002; Deng and Stauffer, 2004).
Although Experiment SLABOE best predicts the ABL height, it underpredicts
moisture fields such as surface latent heat flux (-67 W m-2), surface mixing ratio (-2.8 g kg-1),
and ABL average mixing ratio (-0.6 g kg-1) and overpredicts surface sensible heat flux (≈55
W m-2). Consequently, surface latent heat flux is underpredicted in order to maintain surface
energy balance. This suggests that perhaps the model runs that best predict ABL height such
as SLABOE may have soil moistures that are still somewhat too dry which may be the result
of not calibrating the conversion of soil moisture content to moisture availability.
19
Nevertheless, the spatial structures of the various model fields are still valuable for studying
scale interactions and model sensitivities to more detailed soil moisture inputs.
5.1. UTILITY OF DETAILED SOIL MOISTURE INPUT
The addition of offline-PLACE and ESTAR-derived soil moisture into the model
initial conditions provides the MM5 with soil moisture data with different transect mean
values as well as different spatial variations. Table III shows that the average soil moisture
availability over all the transects in the Slab experiments changes little with the addition of
the offline PLACE soil moisture in Exp. SLABO, as compared to the values in the default
lookup tables in Exp. SLABC. However, average soil moisture availability decreases
significantly (0.222 to 0.151) with the addition of the ESTAR soil moisture availability data
in Exp. SLABOE. The inline PLACE runs also show a decrease in soil moisture content with
the addition of ESTAR data (Exp. PLACEO to Exp. PLACEOE) but the decrease is not as
significant. The dry and warm bias of Exp. SLABOE may indicate that the mean soil
moisture availability is simply too low as suggested earlier.
Although differences in model results due to different mean soil moisture can be
important, this study focuses on the ability of the model to reproduce the “signatures” of
various fields (i.e., their spatial structures, their local maxima and minima) as an important
potential benefit of better soil moisture data. To quantitatively evaluate signature fit, the
mean absolute error is calculated after removing the mean error and is referred to as an
adjusted mean absolute error, with results summarized in Table V. The percentage reduction
in the adjusted mean absolute error indicates a large improvement for surface temperature,
surface mixing ratio, and surface latent and sensible heat flux with near neutral effects in
ABL-average mixing ratio and ABL height with the addition of the ESTAR soil moisture
data in the Slab experiments. Much smaller improvements due to ESTAR are found in the
inline PLACE experiments. The improvements seen in LHF and SHF must be interpreted
20
carefully since the “observed” fluxes are based on the same soil moisture field used to drive
the model fluxes.
Subjective evaluation of spatial structure will focus on fields shown in Figure 9 and
Figure 10 from aircraft transects 3 and 8. For example, one can see for transect 3
improvements in signature or trend due to the addition of ESTAR in surface mixing ratio
(Figure 9b) and surface temperature (Figure 9a). The surface temperature improvements are
particularly noticeable in the northern portion of the transect where the observations indicate
nearly constant surface temperature but SLABO shows a gradient of 2.5 °C / degree latitude
compared to a SLABOE gradient of less than 0.5°C / degree latitude. However, the model
ABL height generally decreases southward between 36 and 35 N while the model and
mesonet temperatures become cooler and have similar gradients, but the observed ABL
height for transect 3 does not decrease southward. Although ABL height does not show clear
improvements over this transect with the addition of ESTAR soil moisture data, subjective
improvements are apparent on other transects such as transect 8.
The mixed layer depth is often correlated with soil moisture and surface
temperature. However, consider the transect 8 ABL height (Figure 10c) and soil moisture
(Figure 10d) for Exps. SLABO and SLABOE. Although model and observations agree that
ABL height is generally higher farther south where it is drier and warmer, the observed
signature in ABL height with a secondary maximum near 37°N is not expected, with the
locally higher soil moisture field there. This ABL feature is better simulated with the use of
ESTAR soil moisture in SLABOE than without it in SLABO (and better simulated than with
the ABL model of Part I). Surface temperature and mixing ratio patterns are also simulated
better in SLABOE than SLABO. It should also be noted that less benefit from the ESTAR
data is apparent near the edges of the ESTAR coverage at the ends of the transects as
expected.
21
The secondary maximum in ABL height illustrates that local soil moisture alone
cannot explain all of the observed boundary-layer structure in this case. Figure 11 shows the
Exp. SLABOE potential temperature structure along this transect with its secondary
maximum in ABL height towards the left (north). The secondary maximum in ABL height
coincides with a region of maximum upward vertical motion (Figure 12) in the warm air and
a baroclinic zone immediately to the north. This is evidence of a secondary circulation,
although the along-transect wind does not reverse direction due to the strength of the
background flow. This circulation appears to be the result of the soil moisture gradient
observed near this location by ESTAR and utilized in Exp. SLABOE since Exp. SLABO,
which differs from Exp. SLABOE only in the soil moisture used, shows only a very small
secondary maximum in ABL at this location (Figure 10) and a much weaker secondary
circulation (not shown). Thus the model produces an inland breeze circulation (e.g., Mahrt et
al. 1994), forced by the surface moisture heterogeneity and consistent with the observed ABL
structure.
The Little Washita River Basin can be seen in the ESTAR derived soil moisture
availability field seen in Figure 6 as the area of higher soil moisture availability near the
southwestern edge of the ESTAR data region, but the offline PLACE resolution is too coarse
to capture this. The flux site LW07 is located in this region and Figure 13 shows that
SLABOE does much better in modelling the SHF and LHF here than SLABO. These Little
Washita flux values are representative of a local effect and indicate the potential benefit of
high resolution soil moisture to predicted surface variables such as fluxes.
5.2. UTILITY OF MORE SOPHISTICATED PHYSICS
Inline-PLACE explicitly models processes not included in the Slab MM5 experiments
and thus provides the possibility for more accurate representation of land surface processes
22
by allowing for temporally varying soil moisture. Differences between MM5-PLACE and
MM5-Slab can be seen in Table IV, which shows the mean error (ME) statistics, and Table
VI, which indicates the mean absolute errors. First, note that because the ESTAR data are
used only as initial conditions in inline PLACE, its influence is much less than that in Slab.
This can be seen by comparing differences between Experiments SLABO and SLABOE with
those between PLACEO and PLACEOE in Table IV. Also note the differences in ME
between MM5-PLACE and MM5-Slab experiments. The magnitude of the mean sensible
heat flux error in PLACEO (16 W m-2) is approximately the same as that in SLABO (15 W
m-2), but the sensible heat fluxes in PLACEO and PLACEOE MTHM peak ≈1.5 h late (≈1900
UTC) compared to 0.5 h late (≈1800 UTC) for SLABO and SLABOE (Table II). The inline
PLACE runs more closely match observed surface temperatures than their Slab counterparts
in the mean. Experiment PLACEO is only slightly too cold (-0.2 °C) contrasted to SLABO’s
somewhat larger warm bias (+0.7 °C), and PLACEOE is slightly warm (+0.1 °C) compared
to SLABOE’s more substantial warm bias (+1.3 °C). In spite of this, PLACE experiment
ABL heights are significantly worse than those in corresponding Slab experiments.
The latent heat flux was nearly correct for SLABO (mean error -5 W m-2) and much
smaller for SLABOE (mean error -67 W m-2) but was too high for both PLACEO and
PLACEOE (mean error +49 and +39 W m-2 respectively). The MTHM LHF peaks occur ≈4 h
early (≈1600 UTC) for PLACEO and PLACEOE in contrast to the Slab experiments which
on average peak very close to the observations (≈2000 UTC; Table II). As compared to the
Slab experiments, surface and ABL mixing ratios are higher in the inline PLACE
experiments with the surface still too dry but the ABL now too moist.
Summarizing the MM5-PLACE versus MM5-Slab differences, the inline PLACE
makes the model ABL too moist and cools the surface temperatures such that they are closer
to observations, but this results in a predicted ABL height much lower than that in Slab and
23
thus even lower than observations. In comparing MM5-PLACE and MM5-Slab, the
“signature” spatial structure for surface sensible and latent heat flux, surface mixing ratio,
and surface temperature for MM5-PLACE fits the observed signatures more poorly than the
MM5-Slab experiments (Table V).
These differences between MM5-Slab and MM5-PLACE motivated further attempts
to improve the results of MM5-PLACE. As indicated in Section 3.2, NDVI data are utilized
to determine vegetative fraction and LAI for Exps. PLACEOE_N and PLACEOE_SN. The
addition of NDVI data does not significantly affect the mean errors (Table IV) and so does
not significantly improve or degrade the model results, including mean absolute errors. As
was discussed in Section 4, surface fluxes appear to be more strongly correlated with soil
moisture than NDVI for 12 July 1997 in this region as reported by Part I, which is consistent
with the minimal effect of NDVI data in this case.
The addition of subgrid variations in soil moisture (Exp. PLACEOE_S), however,
generally increases the mean errors (Table IV) as well as the mean absolute errors (not
shown). Larger mean errors are seen in PLACEOE_S versus PLACEOE sensible heat flux (43 W m-2 vs. -5 W m-2), surface temperature (-1.0 °C vs. +0.1 °C), latent heat flux (+95 W m2
vs. +39 W m-2), ABL average moisture (+1.1 g kg-1 vs. +0.3 g kg-1), and ABL height (-368
m vs. -258 m). The timing of the SHF MTHM showed little change whereas a slight
improvement was seen in the LHF MTHM with mean error decreasing from ≈-4.0 h to ≈-3.5
h (Table II). Surface mixing ratio is also improved (Table IV) but there is not a clear overall
improvement in the simulations with the addition of subgrid soil moisture data.
5.3. SCALE RESPONSE TO SOIL MOISTURE HETEROGENEITY
Since SLABOE has the best soil moisture input and reproduces the spatial “signature”
of model fields well, it will be used to examine the atmospheric response to surface
heterogeneities from the smallest resolvable scale (~10’s of km) to larger scales (~100’s of
24
km). Observations in this study indicate that surface temperature is generally positively
correlated with ABL height on scales of 100’s of kilometres and indicate that surface mixing
ratio and ABL-average mixing ratio are negatively correlated with ABL height as expected
since available moisture will mix through a larger volume if ABL height is higher. Transect 8
is of particular interest because it is one of the longer transects, providing more spatial
coverage, as well as being one of the later transects (≈1800 UTC), and thus providing data
when surface heterogeneities have had longer to influence the ABL. Transect 3 is also
examined since it covers the southern section of transect 8 two hours earlier.
The ESTAR soil moisture (used by SLABOE) indicates that soil moisture shows a
decrease from north to south (Figure 10d). As expected, surface temperature and ABL height
also generally increase toward the south (Figure 10a,c) and surface and ABL averaged
mixing ratios decrease towards the south (Figure 10b and Figure 14). The strong gradients in
some fields (e.g., ABL height; Figure 10c) at the southern boundary are not well represented
in the model, possibly due to the proximity of the edge of the ESTAR data region. Note that
not all features that occur in the detailed soil moisture field are reflected in these other fields,
thus suggesting some scale at which heterogeneities in soil moisture become significant for
affecting the low-level atmospheric structure. Even though soil moisture is low and relatively
uniform to the south of 36.5° N in SLABOE (Figure 10d), noticeably lower ABL-average
mixing ratios (Figure 14) and higher ABL heights (Figure 10c) are seen between 34.5 and
35° N, indicating that the spatial structure of ABL properties is dependent on other factors
besides surface moisture. These factors may include the depth of the ABL, local gradients,
local winds, and advection. In general, however, one sees that a large scale (here ~400 km)
soil moisture gradient results in similar large scale gradients in surface fields and to a lesser
degree ABL fields.
The area of higher resolution ESTAR soil moisture is clearly visible in the modelled
25
surface flux fields (Figure 15), but the scale of the heterogeneity in the ABL height field is
much larger than that of the surface fluxes (Figure 16). Surface fluxes are clearly affected by
small scale soil moisture features as partially demonstrated by the LW07 flux evaluation and
discussed in Section 5.1. If the soil moisture feature is “small” (10’s of km) it may not
noticeably affect other variables such as ABL depth or ABL average mixing ratio.
Examining transect 3 suggests that features such as the local maximum in soil
moisture availability at about 36.1° N (Figure 9d) with an approximate width of 18 km do not
appear to have a noticeable signature in the observed mesonet surface mixing ratio (Figure
9b) or temperature (Figure 9a) but this may be due to the limited resolution of the mesonet
stations. The SLABOE model surface mixing ratio (surface temperature) does appear to show
a very small local maximum (minimum) near this location. The higher resolution of the
LASE ABL-average mixing ratio (not shown) and ABL height fields (Figure 9c) may show
some effect from this local maximum but these fields are less likely to show a difference
because vertical mixing strongly influences them.
Another soil moisture maximum is seen in Figure 9d along transect 3 at
approximately 34.9° N and is about 20 km in length. Observations (Figure 9b) and SLABOE
show a local maximum surface mixing ratio at this location but a relatively constant mesonet
surface temperature (Figure 9a). SLABOE appears to simulate a local minimum in surface
temperature near this location. The ABL height and ABL-averaged mixing ratio are again not
as noticeably affected by this small-scale soil moisture maximum.
The largest surface moisture anomaly on transect 3 is approximately 30 km in length
and located in the area around 35.6° N where surface mixing ratio (Figure 9b) shows a clear
maximum in observations and SLABOE. Experiment SLABOE and the mesonet also show a
local minimum surface temperature near this soil moisture maximum (Figure 9a). The
observed ABL height trend (Figure 9c) shows a clear local minimum in transect 3 at 35.6° N
26
and extending further north to 35.8° N (downwind) while SLABOE shows ABL height to be
lower than that just north of the soil moisture maximum.
Two hours later, part of transect 8 covered the same area as transect 3 and the features
at 35.6 and 34.9°N can be seen again (Figure 10d). The observed ABL height (Figure 10c)
may also be showing the effects of these two soil moisture maxima. The 4-km smoothed
ABL height field shows a relative minimum immediately downwind (northward) of 35.6° N.
Also, there is a large gradient in observed ABL height towards the south at the southern end
of the cross section that becomes flat with a local minimum just downwind of the 34.9° N
soil moisture maximum. Thus model results and observations suggest that small-scale soil
moisture maxima may be causing small-scale features in surface and ABL structure in these
regions, although the signals are rather subtle.
6. Summary and Recommendations for Future Research
This study investigated the potential benefits on simulated ABL structure of a better
specification of soil moisture and related parameters within a mesoscale model with different
land-surface physics options. In general, comparisons between experiments revealed a large
degree of intervariable consistency, where changes in one variable were usually accompanied
by the expected changes in other variables. It was found that slightly overheating the model
surface (≈1 °C) was necessary to approach observed ABL height in this case. This may be
caused by too little parameterized mixing which may be mitigated in the future through
increasing the mixing length scales in the TKE ABL parameterization (Deng and Stauffer,
2004).
Offline PLACE LSM soil moisture values compared well with grid-averaged ESTAR
on 12 July 1997 and validated ESTAR as an attractive source of high-resolution soil moisture
data. The addition of more detailed ESTAR soil moisture produced noticeable improvements
in the model’s representation of surface temperature and mixing ratio gradients. Gradients in
27
fields strongly affected by mixing such as ABL height and ABL average mixing ratio showed
smaller, more subtle differences due to the addition of ESTAR. Mean model fields other than
ABL height were not generally improved by ESTAR but this may be due to model biases
related to the soil moisture content to soil moisture availability conversion, because no
calibration of the surface fluxes with the moisture availability was done. Ignoring the biases
in the model-simulated fields revealed much improved spatial structures in the experiments
using ESTAR. This improvement included a local maximum in model ABL height in one
transect due to a mesoscale circulation (inland breeze). This demonstrates an advantage of
using a 3D mesoscale model rather than a 1D ABL model that is unable to recreate features
such as local circulations (as in Part I).
Although PLACE provided more sophisticated land-surface physics, the coupled
MM5-PLACE experiments in general did not match observations as well as experiments
using the simpler force-restore Slab model. This result emphasizes the difficulties involved
in effectively utilizing a LSM in a mesoscale model due to the additional input parameters
that must be well defined in order to produce improved model output. Attempts were made
unsuccessfully to improve MM5-PLACE results through better specification of LAI,
vegetative fraction, and subgrid effects.
Large-scale differences (~100’s of kilometres) in soil moisture were often associated
with expected atmospheric responses in the model and observations such as deeper, drier
boundary layers along with higher surface temperatures and larger Bowen ratios under drier
soil moisture conditions. Homogenization of the ABL apparently resulting from vertical
mixing and advective effects minimizes the atmospheric structure caused by smaller scale
(~10’s of kilometres) surface moisture anomalies. Nevertheless, it appears that observed and
modelled surface mixing ratio and temperature were affected by small-scale soil moisture
features at least 20 km in length as long as the magnitude of the soil moisture maximum was
28
sufficiently large. The effects of these 20- and even 30-km soil moisture features on ABL
height are more subtle but generally consistent with the anomalies in surface temperature,
surface mixing ratio, and the prevailing low-level wind direction.
It should be noted that the results of this paper are based on one case day involving
limited vegetation and generally cloud-free conditions. Generalization of the results found
here would involve further exploration of the scale interactions between surface and
atmospheric properties, particularly through field campaigns such as the International H2O
Project (IHOP; Weckwerth et al. 2004). Although ESTAR data are not available for IHOP, a
multitude of other data should allow for detailed study of surface-atmospheric interactions
over multiple case days.
29
Acknowledgments
The authors thank Ricardo Muñoz for providing both the coupled MM5-PLACE model and
the offline PLACE results and Peter Wetzel for assistance with PLACE parameter
specifications. The authors also acknowledge George Young for helpful discussions.
Funding for this research was provided by NASA Grant NAG5-8735 and Oklahoma Mesonet
data was provided through this grant. Additional funding was provided through NASA
grants NAG5-6398 and NAG8-1530 and NSF grant ATM-0130349. Some data were obtained
from the Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S.
Department of Energy, Office of Science, Office of Biological and Environmental Research
Climate Change Research Division.
30
Footnotes
1
Standard deviation divided by the mean
31
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41
FIGURE CAPTIONS
Figure 1. Location of the 36-km, 12-km, and 4-km MM5 model domains.
Figure 2. Areal coverage of ESTAR soil moisture data and aircraft transects on 12 July 1997.
Note that transects 3, 8, and 9 are nearly coincident at their southern extreme, with the
northern extreme of each located near the respective label.
Figure 3. Soil moisture availability on MM5’s 4-km domain as determined by the lookup
table. Outline shows area of ESTAR coverage. The dominant soil moisture availability values
are 0.15 (72% of cells), 0.25 (9% of cells), and 0.30 (16% of cells).
Figure 4. Soil moisture availability derived from soil moisture content predicted by offline
PLACE for 11-12 July 1997 and interpolated to the 4-km model domain. Outline shows area
of ESTAR coverage.
Figure 5. ESTAR derived soil moisture content (%) for 12 July 1997.
Figure 6. Soil moisture availability for the 4-km domain on 12 July 1997 resulting from the
use of ESTAR data where available and offline PLACE data elsewhere. The outline shows
the approximate area that contains ESTAR data.
Figure 7. Soil moisture content comparison between 12 July 1997 ESTAR data (≈1400-1900
UTC) and 1500 UTC 12 July 1997 Offline PLACE (layers one and two averaged) both
processed to the 4-km MM5 model domain. Each point plotted represents the soil moisture
averaged across the east-west width of the ESTAR data area.
Figure 8. Soundings taken at the ARM-CART Central Facility on 12 July 1997 at (a) 1428
UTC, (b) 1727 UTC, and (c) 2027 UTC. Wind is represented as in the standard station model
with one full barb indicating 5 ms-1.
42
Figure 9. Model-predicted transects for Exps. SLABO and SLABOE at t = 16 h (1600 UTC
12 July 1997) along transect 3. (a) surface temperature (°C) compared to Oklahoma Mesonet,
(b) surface mixing ratio (g kg-1) compared to Oklahoma Mesonet, (c) diagnosed ABL height
(m) compared to LASE observations, and (d) soil moisture availability.
Figure 10. Model-predicted transects for Exps. SLABO and SLABOE at t = 18 h (1800 UTC
12 July 1997) along transect 8. (a) surface temperature (°C) compared to Oklahoma Mesonet,
(b) surface mixing ratio (g kg-1) compared to Oklahoma Mesonet, (c) diagnosed ABL height
(m) compared to LASE observations, and (d) soil moisture availability.
Figure 11. Model-predicted cross section of potential temperature (K) for Exp. SLABOE at t
= 18 h (1800 UTC 12 July 1997) along transect 8. Potential temperature contours are solid
lines (contour interval of 0.5 K) and the heavy dashed line indicates the top of the modeldiagnosed ABL.
Figure 12. Model-predicted cross section of vertical motion (ms-1) for Exp. SLABOE at t =
18 h (1800 UTC 12 July 1997) along transect 8. Vertical motion contours (contour interval
of 0.1 ms-1) with solid contours representing neutral and upward motion and dashed contours
representing downward motion. The heavy dashed line indicates the top of the modeldiagnosed ABL.
Figure 13. Observed and model-predicted surface fluxes (W m-2) (a) sensible heat flux and
(b) latent heat flux at Little Washita River Basin (LW07) at t = 13-22 h (1300-2200 UTC 12
July 1997) for Exps. SLABO and SLABOE.
Figure 14. Observed and model-predicted Exp. SLABOE boundary layer averaged mixing
ratio at t = 18 h (1800 UTC 12 July 1997) along transect 8.
Figure 15. Model-predicted surface fluxes (W m-2) for Exp. SLABOE at t = 18 h (1800 UTC
43
12 July 1997). (a) latent heat flux (W m-2) and (b) sensible heat flux (W m-2) at 1800 UTC 12
July 1997. Contour interval is 100 W m-2 and heavy solid line indicates the location of
transect 8.
Figure 16. Model-predicted ABL height (m) for Exp. SLABOE at t = 18 h (1800 UTC 12
July 1997). Contour interval is 200 m and heavy solid line indicates the location of transect
8.
SLABOE
PLACEO
PLACEOE
PLACEOE_N
PLACEOE_S
PLACEOE_SN
X
X
X
X
X
X
X
X
X
SLABO
SLABC
X
X
X
Name
X
4
12
Experiment
36
Resolution (km)
X
X
PLACE
Table
X
Offline
Lookup
X
X
X
X
X
PLACE+ESTAR
Offline
Initial Soil Moisture
Table I. Experimental Design.
X
X
X
Slab
X
X
X
X
X
PLACE
Land Surface
X
X
Yes
X
X
X
X
X
X
No
Subgrid Var.
X
X
Yes
X
X
X
X
X
X
No
NDVI
44
45
Table II. Site-averaged maximum three hour mean (MTHM) surface heat flux timing and
magnitude.
Site-Averaged MTHM Surface Heat Flux
Timing (UTC) Magnitude (W m-2)
SHF
LHF
SHF
LHF
SLABC
1800 2000
289
364
SLABO
1800 1950
296
338
SLABOE
1810 2000
328
292
PLACEO
1920 1610
347
385
PLACEOE
1850 1620
336
393
PLACEOE_N 1930 1620
335
408
PLACEOE_S
1900 1650
287
419
PLACEOE_SN 1910 1650
293
419
Observed
1730 2010
243
363
46
Table III. Model and observed mean values over all the transects. Soil moisture availability
(SMA) is denoted by the “*” superscript and soil moisture content (SMC) is denoted by the
superscript “+”.
Mean Values
SLABC
SLABO
SLABOE
PLACEO
PLACEOE
PLACEOE_N
PLACEOE_S
PLACEOE_SN
Observations
SHF
Sfc. Temp
ABL Ht.
LHF
Sfc.
QV
ABL
QV
SMA*
Wm-2
C
m
W m-2
g kg-1
g kg-1
SMC+
258
251
290
220
230
228
192
192
236
30.1
31.4
32.0
30.5
30.8
30.8
29.7
29.7
30.7
831
893
964
805
827
828
717
722
1084
268
261
199
315
305
309
361
362
266
16.2
15.0
14.5
16.0
15.7
15.7
16.8
16.7
17.2
15.0
13.8
13.4
14.6
14.4
14.4
15.1
15.1
14.1
0.216*
0.222*
0.151*
0.215+
0.200+
0.201+
0.193+
0.193+
X
47
Table IV. Model mean errors and observed mean values over all the transects.
Mean Errors
SHF
W m-2
SLABC
SLABO
SLABOE
PLACEO
PLACEOE
PLACEOE_N
PLACEOE_S
PLACEOE_SN
Observations
22
15
55
-16
-5
-7
-43
-44
236
Sfc. Temp ABL Ht.
C
-0.6
0.7
1.3
-0.2
0.1
0.1
-1.0
-1.0
30.7
m
-253
-191
-121
-279
-258
-256
-368
-362
1084
LHF
Sfc. QV
ABL
QV
W m-2
g kg-1
g kg-1
2
-5
-67
49
39
43
95
96
266
-1.1
-2.2
-2.8
-1.3
-1.5
-1.5
-0.4
-0.5
17.2
0.9
-0.2
-0.6
0.5
0.3
0.3
1.1
1.0
14.1
48
Table V. Adjusted mean absolute error over all of the transects after adjustment to eliminate
mean error. The percent reduction (%R) in error due to the addition of ESTAR is shown by
positive values.
Adjusted Mean Absolute Error
SLABO SLABOE %R PLACEO PLACEOE %R
SHF (W m-2)
77.0
29.6
62
118.2
114.6
3
-2
LHF (W m )
94.1
38.3
59
150.6
147.3
2
0.82
0.44
46
1.39
1.28
8
Sfc. Temp. (C)
-1
Sfc. QV (g kg )
0.87
0.73
16
1.35
1.28
5
-1
ABL Avg. QV (g kg )
0.92
0.89
3
0.98
0.99
-1
ABL Height (m)
228
234
-3
212
213
0
49
Table VI. Model mean absolute errors and observed mean values over all the transects for
experiments relevant to the examination of the differences between inline PLACE and Slab
results.
SHF
W m-2
SLABO
15
SLABOE
55
PLACEO
119
PLACEOE 115
Obs.
236
Mean Absolute Errors
Sfc. Temp ABL Ht. LHF
C
1.0
1.3
1.4
1.3
30.7
m
283
254
322
302
1084
W m-2
95
82
154
148
266
Sfc. QV
g kg-1
2.3
2.8
1.8
1.9
17.2
ABL
QV
g kg-1
0.9
1.1
1.1
1.0
14.1