MAPS Special Issue: Land Atmospheric Interactions
Land-Surface Scheme Validation using the Oklahoma Atmospheric Surface-Layer
Instrumentation System (OASIS) Program and Oklahoma Mesonet Data:
Preliminary Results
J. A. Brotzge and D. Weber
Center for Analysis and Prediction of Storms, Norman, Oklahoma
1. Introduction
This paper describes the initial use of Mesonet and OASIS data for use in verifying the
Interactions Soil Biosphere Atmosphere (ISBA) land-surface model (LSM). Previous routine,
real-time measurements of surface fluxes and soil moisture were limited to field projects with
short, high-intensive observational periods. Field programs such as HAPEX-MOBILY (Andre et
al. 1986), the First ISLSCP Field Experiment (FIFE; Sellers et al. 1988), and MONSOON 90
(Kustas et al. 1991) provided rich data sets for advancing knowledge of land-surface interactions.
However, these field experiments were limited in size and scope, and did not allow for long-term
(multi-seasonal) nor large-scale (regional to statewide) estimates of surface and ground
parameters. The notable difference with the present study is the completeness of the data
collected in addition to the spatial and temporal extent of the new data set. As described within,
the available data will allow researchers to scrutinize the numerical frameworks over a complete
set of weather phenomena and improve the methods associated with land surfaces schemes. The
current research efforts at the Center for Analysis and Prediction of Storms (CAPS) includes a
rigorous validation of a number of physics components used by the Advanced Regional
Prediction System (ARPS) described by Xue et.al. (2000a).
The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS; Brotzge et al.,
1999) is a unique, statewide network sponsored by the National Science Foundation which
collects, quality controls and archives radiation, surface fluxes, and soil data in real-time on a
continuous basis. OASIS enhanced the existing observational capabilities of the Oklahoma
Mesonet to allow for continuous monitoring of the total surface energy budget. OASIS data have
been collected and archived from 90 Mesonet sites statewide every 5 to 30 minutes since 1
January 2000. The primary objective of OASIS is to develop a long-term, large-scale data set of
fine spatial and temporal resolution against which remote sensing algorithms and numerical
models can be verified. This is the first such study to use data from OASIS for model
verification.
The Advanced Regional Prediction System is a three-dimensional, nonhydrostatic mesoscale
model developed by the CAPS at the University of Oklahoma. The ARPS is used as both a
forecasting and research tool primarily but not limited to the prediction of convective storms. An
accurate land-surface scheme for use in a mesoscale model is critical to forecasting cloud
formation and convection. The land surface and soil package of ARPS (Xue et al., 2001b)
includes the Interactions Soil Biosphere Atmosphere (ISBA) scheme described by Noilhan and
Planton (1989) and Pleim and Xiu (1995). While the ISBA scheme has been extensively tested
against atmospheric data (Jacquemin and Noilhan, 1990; Noilhan et al., 1991; Bougeault et al.,
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1991; Mahfouf et al., 1995; Noilhan and Mahfouf, 1996; Xiu and Pleim, 2001), the land surface
model (LSM) has not been as thoroughly tested against soil observations. In addition, several
recent improvements to ISBA (Noilhan and Planton, 1996; Xiu and Pleim, 2001) have not yet
been included into the ARPS scheme.
This paper outlines the many challenges in developing, maintaining, and using in-situ data
for model validation. Section 2 presents an overview of the Mesonet and OASIS data including
the limitations and operational challenges with the collection and representativeness of in-situ
data. Instrument limitations and failures, spatial representativeness, and inconsistent closure of
the surface energy budget each contribute to measurement error and uncertainty. In addition,
model sensitivity to initialization and surface heterogeneity makes model verification even more
difficult. Each of these problems is discussed, and preliminary results from model validation of
ARPS using OASIS and Mesonet data are provided in Section 3.
2. Mesonet and OASIS Data Overview
The Oklahoma Mesonet (Brock et al., 1995) provides the infrastructure upon which the
OASIS is built. Approximately 90 Mesonet sites have been enhanced to allow net radiation,
ground and sensible heat fluxes to be estimated directly (Fig. 1). Latent heat flux is estimated as
the residual of the surface energy budget. Skin temperature and soil moisture also are measured
directly at the 90 sites. Ten of the 90 OASIS sites, termed “super sites”, have additional sonic
anemometry and 4-component net radiometers, which allow more accurate and precise
measurements of net radiation and sensible and latent heat fluxes. The ten super sites are
equipped with redundant instrumentation and multiple methods of measurement to allow for
improved quality control of the data. A super site has been selected in each of Oklahoma’s nine
climatic zones, to provide a diverse yet simultaneous set of observations from across the state.
2.1 Instrumentation and measurement procedures
The measurement methods used by OASIS are summarized in Table 1. A detailed
description of the sensors is found in Brotzge (2000). What follows is a brief summary of the
assumptions and unique problems associated with each measurement method. This review is
used foremost to develop an approximate error estimate associated with the data set. Second,
problems in measurement are important to be understood in context when used for comparison
against modeled data. As discussed by Noilhan et al. (1991), model results most likely will not
exactly reproduce the surface observations, either because of nonrepresentativeness of the
measurements or model deficiencies. Thus, a correct interpretation of the observations and
modeled data require a complete understanding of both observational and modeling deficiencies.
2.1.1 Net radiation
Shuttleworth (1991) describes net radiation as one of the most difficult parameters to
measure accurately. For this reason, net and four-component radiometers were installed at the
OASIS super sites. The net radiometer used by OASIS is the NR-Lite, a recently developed
sensor, which is relatively low-cost, is nearly maintenance-free and does not require the use of
polyethylene domes which can degrade over time. However, the simplicity of the design permits
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greater sensitivity to operational errors. Brotzge and Duchon (2000) identified several
limitations of the sensor. These problems include poor initial calibration, and a degradation of
performance during high winds and low sun angles, and during precipitation.
The four-component radiometer, the CNR1, comprises four separate sensors housed in a
single unit, allowing incoming and outgoing shortwave and longwave radiation to be directly
observed. A heating element, embedded within the body of the sensor, is activated during
certain weather periods to minimize the effects of dew, frost or precipitation. The sensor is much
more expensive than the NR-Lite but does not incur the operational problems associated with
wind or precipitation. Both radiation sensors are mounted at a height of 2 m.
Data from the NR-Lite and CNR1 were collected and archived from the ten super sites
(Brotzge, 2000). Comparisons between the co-located sensors revealed minimal differences
between them (< 5%). Multiple sensors at a site proved to be valuable in identifying a failed
sensor or poor data.
2.1.2 Ground heat flux
A combination approach (Tanner 1960) is used to estimate the total ground heat flux and
includes separate estimates for the ground flux and storage terms:
dT 
dT
G     
 Cdz2 

 dt 
dz
where  [W (m K)-1] is the thermal conductivity, dT [K] is the temperature difference across the
plate, dz [m] is the plate thickness, C [J (m3 K)-1] is the soil heat capacity, dz2 [m] is the depth of
the soil layer, and dT/dt is the temporal rate of change in the integrated soil temperature between
0 and 5 cm (Fritschen and Gay, 1979). Two Platinum Resistance Temperature Detectors
(PRTDs) and two heat flux plates are installed at each of the 90 sites at a depth of 5 cm. An
average of the 2 PRTDs is used to estimate dT/dt; likewise the mean of the two flux plates are
used to estimate the first term. The soil heat capacity is estimated at each site as a function of the
measured volume fraction of minerals, organic material, and soil moisture. For more details, see
Brotzge (2000).
Massman (1993) identified several assumptions used in estimating ground heat flux. The
specific heat capacity of the soil must be assumed constant in depth and in time, and horizontal
heat flow is neglected. The thermal conductivity of the ground flux plate must match that of the
soil (Fritschen and Gay, 1979). Because the conductivity of the plates generally do not match
that of the soil, Fritschen and Simpson (1989) developed a correction for the soil thermal
conductivity as a function of plate conductivity, size, and shape, which has been applied to this
data set. Finally, the flux plates themselves may impede the vertical flow of heat and moisture
within the soil (Tanner, 1960; van Loon et al., 1998). Air gaps between the soil and plates also
can lead to significant measurement errors (Fritschen and Gay, 1979).
Significant errors are more likely to occur from large variability in ground flux properties
and surface heterogeneity than from instrument error. The effects of heterogeneity are described
in greater detail in section 2.2.1, however, significant variability has been observed in soil
moisture and soil properties within a relatively small (20 x 20 m2) area (Basara, 2001). A
comparison study conducted by Brotzge (2000) revealed differences of nearly 100 Wm -2
between two sets of co-located ground flux measurements. These two sets of measurements
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were located approximately 100 m apart. Thus, while instrument errors generally are limited to
less than 5%, errors due to surface heterogeneity can lead to much greater uncertainty. Using the
mean from two heat flux plates and the mean from two PRTDs reduces the error in ground heat
flux from spatial heterogeneity. However, the exact magnitude of the error remains unknown.
2.1.3 Sensible and latent heat fluxes
The ten super sites also have been equipped with sonic anemometry to directly measure
the sensible heat flux, in part to verify the gradient method used by the standard sites. The sonic
anemometer is installed at a height of 4.5 m and is mounted to the west of the tower. Data from
the sensor cannot be used during or immediately following precipitation. Foken and Wichura
(1996) have listed a host of sensor configuration and meteorological problems that can occur
when using an eddy correlation technique. For this study, sonic measurements have been
corrected for moisture dependence (Schotanus et al., 1983; Stull, 1988) and for tilting of the
sensor (T. Horst, personal communication 1999).
Each of the 90 standard OASIS sites is equipped with similar cup anemometers and
thermistors to monitor vertical gradients in wind and temperature. The vertical gradients of heat
and momentum are applied to Monin-Obukhov similarity theory to derive an estimate of sensible
heat flux (Brotzge and Crawford, 2000). Brotzge and Crawford identified three major
difficulties in estimating gradient fluxes using the Mesonet infrastructure. First, the method is
extremely sensitive to instrument errors. Second, the temperature sensors are only naturally
aspirated, meaning that during high solar radiation, low wind conditions, radiational heating of
the sensors creates a bias in temperature measurements. Third, nearby trees and topography
create subtle fetch problems, thus biasing flux estimates. McAloon et al. (2000) and McAloon
(2001) have found that the gradient technique is much more reliable during unstable and neutral
conditions, and have identified those Mesonet sites where fetch is not a problem. Furthermore,
McAloon (2001) has found a significant sensitivity (> 100 Wm-2) to the theoretical constants
used.
Latent heat flux is measured directly at the super sites using the sonic anemometer and a
krypton hygrometer, mounted 10 cm below the sonic anemometer. The latent heat flux
measurement has been corrected for oxygen absorption (Tanner et al., 1993), density fluctuations
(Webb et al., 1980), and for sonic tilt. Latent heat flux is estimated as the residual of the energy
balance (Rn – G – H = LE) at the standard sites.
An estimate of the uncertainty of the sonic-derived flux measurements is approximately 5%
for the sensible heat flux and 10% for latent heat flux. The measurement uncertainty of the
gradient method varies widely according to site fetch and atmospheric stability.
2.1.4 Soil moisture and temperature
Soil matric potential is measured at the 90 OASIS sites at the depths of 5, 25, 60, and 75 cm
where possible. The soil matric potential is measured using 229-L heat dissipation sensors. The
soil water potential is converted to soil water content, based upon soil properties (Basara, 1998).
Basara and Crawford (2000) identified a problem with preferential water flow seeping down
along the sensor cables to the lowest depths after some heavy rain events. Nevertheless, these
instances were rare and were quality controlled. Soil temperatures also were measured from the
229L sensors located at the depths of 5, 25, 60, and 75 cm.
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The surface skin temperature is measured at all sites using infrared sensors mounted at an
angle of 45 and installed at height of 2 m. Recent quality control of the data has shown that
dust and spider webs can cover the optical sensor, reducing the accuracy of the measurement.
2.1.5 Quality control
All data were corrected and quality controlled as described by Brotzge et al. (1999).
Standard Mesonet data were further quality assured by a series of automated and manual checks
(Shafer et al., 2000). Redundant instrumentation allowed most missing flux data to be replaced;
4-component net radiation estimates from the CNR1 were replaced by data from the NR-Lite
when needed, and sensible heat flux estimates from the sonic anemometer were replaced by
gradient profile estimates. When any one of the four components of the energy budget were
missing, the residual was used.
2.2 Challenges associated with model verification
2.2.1 Surface heterogeneity
A major problem in evaluating model performance when using single site, in-situ data is
the impact of surface heterogeneity. The natural variability in topography, vegetation, and soils
decreases the representativeness of an observation. The degree of this variability is dependent
upon the measurement type. Atmospheric parameters are well mixed due to advection and
turbulent mixing and exhibit significant correlations across large-scale regions. Surface and soil
parameters such as vegetation, soil moisture and soil type, exhibit much less correlation across a
pixel that represents several hundred square meters.
Basara (2001) quantified the variability of soil moisture within a 20 m x 20 m area during
the summer of 1999. Over 2,700 samples were collected between 1 June and 12 August at 12
random locations within the sample area up to a depth of 80 cm. Basara found the greatest
variability nearest the surface immediately following rainfall events; the greatest homogeneity
was observed during extended dry periods (Fig. 2). Next, Basara (2001) quantified the impact of
the observed soil moisture and soil texture variability upon modeled fluxes as estimated by the
Oregon State University (OSU) 1-D PBL scheme used by the operational ETA model. Basara
found a significant impact; for example, a change in soil texture from clay loam to silt loam
increased latent heat fluxes by over 350 W m-2. Ek and Cuenca (1994) and Cuenca et al. (1996)
examined the effects of modeled changes in soil water content and soil texture on the OSU PBL
scheme and found similar results.
If natural small-scale variability exists across a large-scale region, then data from a single
site cannot be used for validation of a model grid area. To verify which variables could be used
for model validation, Brotzge and Richardson (2002) estimated the spatial correlation among all
90 OASIS sites for most measured parameters. Data archived from all of 2000 were included in
the study, which comprised approximately 15 million observations. The results, summarized by
Figures 3a and 3b, showed that atmospheric parameters were much greater correlated than were
surface and soil variables. Nevertheless, all variables showed decreasing correlation with
decreasing distance even among those variables influenced by surface heterogeneity (Fig. 3b).
This heterogeneity is expected to vary with season, however, as stratiform rains give way to
convective rainfall during the spring and summer periods (Fig. 4), increasing the spatial
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heterogeneity in soil moisture. Overall, the results from Brotzge and Richardson agree with
Shuttleworth (1991), that single point, in-situ data can be used for both calibration and diagnosis
of a numerical model if the data represent a large, uniform region and are collected over an
extended period of time.
2.2.2 Closure of the surface energy balance
A second major problem with measurement of near-surface parameters is the difficulty in
closure of the surface energy balance. The four components of the surface energy balance rarely
are found to be closed (i.e., Rn – H – LE – G = 0) when measured independently (Nie et al.,
1992; Stannard et al., 1994; Twine et al., 2000). Such data cannot be used as “truth” against
which model data is to be compared. Brotzge (2000) quantified the closure of OASIS data at the
ten super sites and found significant annual variations in closure which varied with the
magnitude of the latent heat flux. Closure was observed closest to 100% during extended clear,
dry periods. Diurnal cycles indicated some nonclosure during the early morning hours.
Examination of all data indicated no significant problems with measurements of net radiation or
sensible heat fluxes; ground heat fluxes were in general too small to account for significant nonclosure. For this model evaluation study, latent heat flux was estimated as a residual to ensure
closure of the surface energy budget.
3. Verification of the ISBA scheme
A series of controlled runs were made with ARPS in a 1-D vertical column mode.
Stretching was applied to the 33 levels of the vertical column, and the 1.5 turbulent kinetic
energy scheme by Sun and Chang (1986) was used. Model runs were initialized using observed
NWS soundings, Mesonet atmospheric data, and soil moisture and radiation data from OASIS.
Vegetation and soil parameters were initialized to match estimated values from the observation
site. The model was then forced every 10 minutes with observed net and solar radiation, air
temperature, and relative humidity. . This configuration allowed only the soil model variables to
vary during the simulation. These tests represent the first step in a series of tests aimed at
validating the soil and boundary layer schemes in the ARPS.
A comprehensive data set was compiled for this study from one year of OASIS data archived
and collected from a single OASIS super site located at Norman, Oklahoma (NORM). NORM
(Lat. 35 15’ 20”; Lon. 97 29’ 0” ) is characterized by flat terrain (~ 0.0 slope), and short grasses.
Standard atmospheric Mesonet and OASIS flux data were collected every 5 minutes; soil data
were collected every 30 minutes. Atmospheric data included air temperature, relative humidity,
atmospheric pressure, rainfall, and wind speed and direction. Surface parameters (what is a
parpameter?) included incoming and outgoing shortwave and longwave radiation, sensible and
latent heat fluxes, ground heat flux, skin temperature, and soil moisture and soil temperatures at
5, 25, 60, and 75 cm depths. Snowfall depths were included into the data set as estimated at a
nearby National Weather Service (NWS) office (approximately 3.03 km distant). Vertical
soundings from 00 and 12 UTC from the NWS were archived and collected as well. Vegetation
data were obtained from bi-weekly NDVI values from which the appropriate 1 km x 1 km pixel
was extracted to coincide with the site location. These estimates were interpolated to daily
values. Soil information for the Norman site was provided by the Oklahoma Climatological
Survey.
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Several clear days were chosen from the year data set to provide a precursory review of
the performance of the ISBA scheme. Data from 20 May, 2000, represented a synopticallyquiescent spring day characterized by warm temperatures (maximum temperature of 28 C), a
moderately moist soil and vigorous vegetation growth (NDVI = 0.61). High pressure also
dominated during the 1 – 3 August, 2000, collection period. This data represented a dry-down
period with similar soil wetness but hot air temperatures (maximum temperature of 37 C) and
stressed vegetation (NDVI = 0.5).
3.1 The ISBA land-surface scheme
The ISBA scheme is a “force-restore” method (Deardorff, 1978) and for simplicity is
limited to five prognostic equations for soil temperature and moisture (Noilhan and Planton,
1989). These five prognostic equations are:
Ts
2
 CT (Rn  LE  H) 
T  T 
t
 s 2
(1)
T2 1
 T  T 
t  s 2
(2)
wg
C
C
 1 Pg  Eg  2 wg  wgeq 
t
w d1

(3)
w2
1

P  Eg  Etr 
t
w d2 g
(4)
Wr
 vegP  Ev  Etr   Rr
t
(5)
where the five prognostic variables include the surface skin temperature (Ts, K), the mean-layer
soil temperature (T2, K), the canopy wetness (Wr), the soil surface wetness (Wg), and the meanlayer soil moisture (W2). These time-dependent parameters are forced by the net radiation (Rn),
precipitation (P), bare soil (Eg) and evapotranspiration (Etr) in the form of latent heat flux (LE),
sensible heat flux (H), and surface runoff (R). The surface temperature and moisture are restored
to equilibrium by heat and moisture sources from the soil layers below. The time scale at which
these variables act are prescribed a priori in the form of a time constant  (set to one day) and the
soil-layer depths (d1 = 0.1 m; d2 = 0.9 m). Furthermore, the interaction between the soil and
atmosphere varies as a function of the fractional vegetation cover (veg), vegetation type, soil
type, heat capacity, and thermal conductivity. These properties are specified by the diagnostic
variables C1, C2, and CT. These five equations provide memory from the land surface to the
atmospheric system.
3.2 Model sensitivity
3.2.1 Vegetation cover
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Jacquemin and Noilhan (1990) conducted a detailed sensitivity analysis of the ISBA
scheme. They quantified the influence of soil texture, moisture, and vegetation cover upon the
progged values of surface fluxes. In summary, Jacquemin and Noilhan found that for daily time
scales, vegetation cover (veg) was the most sensitive parameter; initialization of the deep-layer
soil moisture, w2, was most important for longer time scales. For a mesoscale model such as
ARPS, proper characterization of the vegetation varies as a function of grid resolution. Currently
in ARPS, vegetation cover is specified as a function of vegetation type, which remains constant
throughout the year. Nevertheless, vegetation cover cannot be readily known.
Likewise, estimation of vegetation cover from an observational site is also rather
subjective. From the OASIS site at Norman, a vegetation cover of 75% was estimated visually.
Due to the uncertainty in this term and its importance to the ISBA scheme, model data from 20
May were examined by varying the vegetation cover (Fig. 5). Observed NDVI was 0.61. The
vegetation cover was varied from 0% to 100%. The observed soil water content (m3 m-3), plotted
in bold, was nearly constant during the 24 hour period. Model results show significant (> 0.10)
diurnal oscillations with fractional bare soil; only with a complete vegetation cover do model
results approach those of the observations. Thus, even a visual estimate at the site may not lead
to the most correct model results.
The diurnal cycle of the observed soil water content at 5 cm was opposite in phase to
model values, and was counter-intuitive to what might have been expected. Basara (1998) gave
an explanation of this rather unexpected diurnal cycle. A combination of thermal and potential
gradients act to moisten the 5 cm soil layer during afternoon heating and dry the layer during
nocturnal cooling. A potential gradient is induced as upward diffusion balances surface
evaporation; the upward diffusion moistens the 5 cm layer during the period of greatest surface
heating (at midday). Likewise, at night water is diffused upward from the 5 cm level to the
surface, drying the 5 cm layer. Thermal gradients induce a similar pattern in soil moisture.
According to Basara, under certain conditions water flows from regions of high to low
temperature. Thus, during the afternoon hours of greatest surface heating, water flows from the
skin surface down to the cooler 5 cm depth; during the evening, water flow reverses from the 5
cm depth upward to the cooler skin surface.
A significant problem in evaluating the surface soil moisture parameter, wg, is that the
soil moisture dynamics changes significantly during a diurnal period over the specified
penetration depth of 10 cm. Deardorff (1978) originally defined wg as the soil moisture within
the top few millimeters of the soil layer. He verified model estimates of wg using observed
values collected from the upper 0.5 cm of soil. Therefore, estimates of wg as defined by the
ISBA scheme should not necessarily match those observed by the OASIS sensors installed at a
depth of 5 cm (see section 3.2.3).
3.2.2 Surface roughness
Surface roughness is another input parameter specified a priori by the ISBA scheme as a
function of surface type. Actual estimates of surface roughness from the Norman site are not
known. Jacquemin and Noilhan (1990) found that variations in surface roughness changed
estimates of latent heat flux by less than 10%. Again, because observed values of surface
roughness were not known for the site, model sensitivity was tested by computing the top-layer
(skin) soil temperature. Skin temperature was estimated by varying surface roughness from 0.01
m to 0.60 m (Fig. 6). Skin temperature data from 20 May varied by as much as 9 C as a
function of the roughness length. However, model results matched daytime observations of skin
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temperature best when a surface roughness of 0.01 m was used. An expected roughness of 0.005
m < zo < 0.05 m was estimated visually from the Norman site (Garratt, 1992). NORM is
characterized by flat terrain and short grasses. Nevertheless, model estimates of skin
temperature were > 9 C less than those observed. Such differences were likely due to the
sensitivity of the soil and vegetation type chosen as representative of the site. A second set of
model runs (not shown) assumed a soil type of silty clay. These results showed model estimates
of skin temperature during mid-afternoon to within one degree Celsius with a zo of 0.01 m. The
sensitivity of soil type to top-layer soil moisture and temperature is examined greater detail in
section 3.2.3. The time lag observed at sunrise between the observed and model values are
discussed in section 3.4.
3.2.3 Soil type
Soil type dictates the ability of the soil to transfer heat and moisture throughout the soil
column. The current version of ISBA used by ARPS assigns several soil parameters including
the saturated, wilting point, and field capacity volumetric water contents (m3 m-3) and the soil
thermal coefficient at saturation (K m2 J-1) as a function of a single soil type. Noilhan and
Mahfouf (1996) suggested an improvement to the scheme by recommending that all hydraulic
parameters be estimated as a continuous function of the ratio of sand and clay present in the soil.
Before implementation of the Noilhan and Mahfouf (1996) recommendation into ARPS,
known soil values from the Norman Mesonet site were used to quantify this change. Data from
NORM provided an insightful comparison because the soil type changed with depth; the ISBA
scheme does not allow soil type to vary with depth. The fractional soil type was measured from
each of the four soil moisture measurement depths (5, 25, 60, 75 cm) at the Mesonet site. At the
5 cm level, the fraction of clay and sand in the soil was 25% and 15%, respectively; at the 25 cm
level and below, the fraction of clay and sand was approximately 40% and 15%. These two
estimates of soil type were tested against discrete estimates of soil type. The upper 15 cm of the
soil column was classified as silt loam; the soil was largely clay loam below 15 cm. The
influence of the treatment of these hydraulic parameters upon model estimates of surface soil
moisture was determined by comparison against observations (Fig. 7a and 7b).
The impact of soil type was significant upon both surface soil moisture and temperature.
A change in soil type altered the heat and moisture capacities of the soil layer. As shown in
Figure 7a, the current scheme, which uses a discrete soil type, indicates a similar behavior in soil
moisture to that observed at the 5 cm depth. However, an examination of the top-layer soil
temperature shows a significant (> 6 C) underestimation in temperature by the discrete soil type.
On the other hand, the continuous formulations underestimated (overestimated) daytime
(nighttime) soil moisture, but forecasted daytime skin temperatures within several degrees. Such
differences could have a strong impact on daily forecasted minimum air temperatures and PBL
development.
Most likely, the surface soil moisture, wg, is best represented by the continuous
formulations which show large diurnal variability in the wg term. As discussed in section 3.2.1.,
the top-layer is generally representative of the top few millimeters; the observation of soil
moisture is taken at a depth of 5 cm. A comparison of the top-layer soil temperature is likely the
best estimate of the performance of the model.
3.3 Time constant, 
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A time constant, , was chosen in the force-restore scheme equal to one day to represent a
typical diurnal cycle (Deardorff, 1978). However, the same time constant is used to represent
both the short-term variability of the skin temperature, Ts, and the surface soil moisture, wg, as
well as the more slowly varying mean-layer soil temperature, T2. Xiu and Pleim (2001)
recommended setting  = 10 days to improve seasonal variability. Mean-layer soil temperatures
were estimated from ARPS as a function of  and plotted against observed data collected from a
depth of 75 cm during 1 – 4 August (Fig. 8). These data showed that a longer time constant
improved the mean-layer soil temperature estimate by eliminating the diurnal signal.
To maintain the diurnal signal in the restoration term of the skin surface and surface
moisture equations, a time constant of 10 days cannot be used. Instead, Equation (1) was
replaced with:
Ts

(6)
 CT G  Ts  T2 
t
D
where  [W m-1 K-1] is the soil thermal conductivity and D is a damping coefficient [m], as
described by Dickinson (1988). The damping coefficient represents a penetration depth and
varies as a function of thermal conductivity and specific heat capacity. Because D is a function
of soil type and moisture, the coefficient may vary with time and space.
Results from this modification were tested for both the 20 May (Fig. 9a) and 2 August days
(Fig. 9b). Skin temperature was calculated according to Eq. (6) by varying D from 0 m to 1 m.
Daytime temperatures varied little among model values and were much cooler than observations.
Model estimates differed greatest during the night and early morning hours.
Results from the 20 May case showed that a D ~ 0.40 m was closest to observations. Results
from the 2 August data indicated a much smaller damping coefficient of D ~ 0.2 m or less.
These results indicated a much greater penetration depth during May due to wetter soil and
greater thermal conductivity. Likewise, during August a very dry soil limited the thermal
conductivity and which lead to a much shallower damping coefficient.
3.4 Delay in model response
An examination of Figures 6, 7b, 9, 10, and 11 revealed another significant problem with
modeling of the surface skin temperature and surface fluxes by the current ISBA scheme as used
within ARPS. Both the 20 May and 1 - 2 August data showed a significantly lagged (> 1 hour)
model response to the rapid surface heating of the soil at sunrise. Neither changes to the time
response function nor damping coefficient increased this response to early morning heating.
Model and observed sensible and latent heat fluxes for 20 May and 2 August are plotted in
Figures 10a,b and Figures 11a,b, respectively. These plots of the aerodynamic fluxes also
indicated the sluggishness of the model fluxes to morning radiation. Not until after the net
radiation increased to greater than zero did the model fluxes respond. The lack of diffuse energy
in the radiation code of ARPS prevented the immediate response to the sunrise forcing.
The differences between the observed and model (discrete soil type) surface fluxes during 20
May (Figs. 10) and 2 August (Figs. 11) were due to an overestimation of the top-layer soil
moisture by the LSM. This overstimate in soil moisture lead to a significant underestimate in
skin temperature (Fig. 7b, 9). This underestimate in skin temperature lead to a decreased air-soil
temperature gradient, and a decreased sensible heat flux. Likewise, the latent heat fluxes were
overestimated due to the overestimated top-layer soil moisture.
10
The model results improved when the continous soil formulations were used. As shown in
Figures 7a,b, a drier top-layer is warmer, leading to a greater air-soil temperature gradient, which
lead to greater daytime sensible heat flux and lower latent heat flux. These model estimates were
much closer to those observed. Note that this configuration allowed only the soil model
variables to vary during the simulation. These tests represent the first step in a series of tests
aimed at validating the soil and boundary layer schemes in the ARPS.
4. Conclusions
A proper evaluation of the ISBA scheme involved a thorough review of the validation
data set and the model results. A detailed examination of the data set permitted a preliminary
evaluation of the current ISBA scheme used within ARPS. The principal results of our
investigation follow.
1.) The sensitivity of the ISBA scheme to input parameters is detrimental to a microscale model
such as ARPS due to the uncertainty which exists in choosing the surface parameters.
Furthermore, additional uncertainty exists due to the surface heterogeneity within each grid area
represented by the input parameter. As demonstrated in Figures 5 and 6, the impact of
vegetation cover and surface roughness is significant (> 13 C in Fig. 6).
2.) Changes in the classification of soil type significantly alter the soil heat capacity and
conductivity (Fig. 7). A continous formulation proposed by Noilhan and Mahfouf (1996) lead to
noticeable differences in soil temperature and moisture (Fig. 7a and 7b), particularly when
radiative forcing was minimal.
3.) The observations of soil texture indicated decreased clay fraction with depth. The simplicity
of the ISBA scheme does not allow for variations of soil texture with depth. The impact of this
assumption was quantified by the 20 May 2000 example (Fig. 7a and 7b). Modeled estimates of
skin temperature varied less than 0.5 C, however, the top-layer soil water content varied by as
much as 0.4 m3 m-3 during the evening and early morning hours.
4.) This study verified the change in  from 1 day to 10 days, as originally proposed by Xiu and
Pleim (2001). As shown in Figure 8, the observed mean-layer soil temperature exhibited little
diurnal variability; the model results improved by setting  equal to 10 days, which eliminated
the model-imposed diurnal cycle.
5.) To maintain the diurnal signal in the restoration term of the skin surface, a time constant of 10
days could not be used. Instead, the term was replaced as a function of soil conductivity and a
damping coefficient. Results indicated model values of skin temperature improved compared to
the observed estimates, but varied according to the chosen damping coefficient (Fig. 9). The
damping coefficient varied with soil wetness.
6.) A delay in the model response of the sensible and latent heat fluxes was detected when
compared against the observations of May (Fig. 10) and August (Fig. 11). The most likely cause
of such a lag is the lack of diffuse radiation within the model.
11
This work will be expanded in the near future to quantify differences between the model
and observed fluxes on a seasonal and annual basis. In addition, a variety of vegetation and soil
types will be tested using data from other OASIS sites across Oklahoma. Furthermore, the
spatial interaction of surface fluxes, soil wetness, and vegetation will be examined across
regional scales using the 90 standard flux sites. Such validation of the LSM against observed
surface variables is anticipated to lead to further improvements in the ISBA scheme, and which
will ultimately lead to improved prediction of air surface temperature and moisture within ARPS.
Acknowledgments. This research was made possible in part by NSF Grant 125-5645 and the
Williams Energy Corporation Grant xxx-xxxx
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15
Fig. 1. State map displaying 89 OASIS sites (circles and triangles) and ten OASIS super sites
(circles).
Table 1. Measurement methods used at OASIS standard and super sites.
Measurement Technique
16
90 Standard sites
Domeless net radiometer, (NR-Lite)
10 Super sites
Four-compenent net radiometer,
(CNR1)
Sensible heat flux (H)
Gradient method (Paulson 1970;
Brotzge and Crawford 2000)
Eddy covariance using sonic
anometery
Latent heat flux (LE)
Residual from the energy balance,
LE = Rn – H – G
Eddy covariance using sonic
anometer and Krypton
hygrometer
Ground heat flux (G)
Combination method,
using the mean of two heat flux
plates, soil moisture at 5 cm, and
average soil temperature between
0 and 5 cm
Combination method,
using the mean of two heat flux
plates, soil moisture at 5 cm, and
average soil temperature between
0 and 5 cm
Net radiation (Rn)
17
Standard Deviation of Soil Water Content in the
0-5 cm Layer vs. Days Since Rainfall
at Norman, OK (1 June 1999 - 12 August 1999)
0.08
Sta ndard Dev iation
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0
5
10
15
20
Days Since Rainfall
25
30
Fig. 2. The standard deviation of soil water content in the 0-5 cm layer plotted as a function of
days since the previous rainfall (Basara 2001). Figure includes data collected between 1 June
and 12 August 1999 at the Oklahoma Mesonet site in Norman. Figure courtesy of J. Basara.
Table 2: Summary of model experiments.
18
Summary of Model Experiments
Exp. #1
Exp. #2
Soil Type
 [days]
of Silt loam
10 days
Figure 5
10 days
Figure 6
10 days
Figure 7
Varied,
Figure 8, 9
Vegetation %
Zo [m]
Varied,
Function
0 – 100%
veg type (.02)
75%
Varied,
Silt loam
0.01 – 0.60
Exp. #3
75%
Function
of Continuous
veg type (.02)
Exp. #4
75%
Function
function %
of Silt loam
veg type (.02)
1 - 10 days
19
(a)
1
Correlation (R)
0.5
0
Solar Radiation
Air Temperature
Relative Humidity
Wind Speed
Rainfall
-0.5
-1
0
20
40
60
80
100
Distance (km)
(b)
1
Correlation (R)
0.5
0
Net Radiation
Sensible Heat Flux
Ground Heat Flux
-0.5
-1
0
20
40
60
80
Distance (km)
Fig. 3. Estimated correlation between sites plotted as a function of distance.
20
100
1
Jan - Mar
Apr - Jun
Jul - Sep
Oct - Dec
Correlation (R)
0.8
0.6
0.4
0.2
0
0
20
40
60
80
Distance (km)
Fig. 4. Estimated correlation of rainfall between sites plotted as a function of distance.
21
100
Fig. 5. The surface soil water content, wg (m3 m-3), plotted as a function of time of day during 20
May, 2000. The soil water content is estimated while varying the fractional vegetation cover
(veg) from 0.0 to 1.0; observations are plotted as bold circles.
22
Fig. 6. The surface soil temperature, plotted as a function of time of day during 20 May, 2000.
The soil temperature is estimated while varying the surface roughness (Zo) from 0.01 to 0.6;
observations are plotted as bold circles.
23
Fig. 7. The surface soil water content, wg (m3 m-3) (a) and soil temperature, Ts (K) (b), plotted as
a function of time of day for 20 May, 2000. The soil water content and soil temperature are
estimated while varying the soil characteristics; observations are plotted as bold circles.
24
Fig. 8. Observations and model estimates of deep-layer soil temperature, t2 (K), plotted as a
function of time of day from 1 August to 4 August, 2000. The time constant, , is varied from 1
to 10 days; observations are plotted using large circles.
25
Fig. 9. The top-layer soil temperature, Ts (K), plotted as a function of time of day during (a) 20
May, 2000, and (b) 2 August, 2000. The modeled surface temperature is varied as a function of
damping depth, D (m); observations are plotted using large circles. Note the time lag of the
model around 1200 UTC.
26
Fig. 10. Observed and model computed (a) sensible and (b) latent heat fluxes, as observed during
20 May, 2000. Observed data are plotted with bold circles.
27
Fig. 11. Observed and model computed (a) sensible and (b) latent heat fluxes, as estimated for 1
August, 2000. Observed data are plotted with bold circles.
28