The
in situ observations generally have the highest ISM variance for most
quintiles followed by CPC and HRRR, which has the lowest variances of
the three datasets regardless of the wetness regime (Figure 3a-c). The
in situ and CPC quintile mean ISM standard deviations are not
significantly different for all quintiles except L20-40,
while the HRRR quintile mean differences are significant for all
quintiles when compared to both other datasets. Dirmeyer et al. (2016)
showed that spatial scaling differences do not have a large impact
(~10%) on in situ observation standard deviations via
conducting tests where many stations that are separated by several km to
up to 100 km are averaged together. Therefore, the differences between
the in situ and NOAA modeled standard deviations are likely due to other
factors outside of dataset spatial scale differences (e.g., model
processes representation or model input data). Furthermore, the large
variability or scatter in the ISM standard deviation differences between
the in situ data and both models (Figure 3a-b) suggests that the cause
of these differences depends on the specific location. The differences
between HRRR and CPC ISM standard deviations, however, demonstrate a
more systematic bias between these two modeling frameworks (Figure 3c).
Figure 3. Same as Figure 2, except for comparisons of ISM standard
deviations (a-c) and VSM standard deviations (d-g).
5.2 VSM Standard Deviation Comparisons at Varying Depths
VSM standard deviations at four different depths are compared between
HRRR and in situ data (Figure 3d-g) to determine whether a certain depth
is driving the differences in the ISM standard deviations (Figure 3a).
Regardless of the soil moisture regime, the HRRR surface VSM standard
deviations compare well to the near-surface in situ observations. When
averaging over all locations, the mean near-surface percentage
differences in HRRR soil moisture from the in situ value is only +4.2%
(Figure 3d). This is likely related to improvements made in the HRRR’s
RUC LSM and the moderately coupled land data assimilation system that
have been applied (Benjamin et al., 2022). However, at depths of 5 cm
below ground and deeper (Figure 3e-g), most quintiles have statistically
significant differences in the standard deviations. The mean percentage
differences over all locations are -38.7%, -36.4% and -45.2% for the
5 cm, 10 cm and 100 cm levels, respectively, with the HRRR always having
lower standard deviations than the in situ datasets for every quintile.
These differences in VSM standard deviations are larger for wetter soil
moisture regimes (L20-100). While the differences for
the different soil moisture regimes are generally similar for the 5 cm,
10 cm and 100 cm depths, there are more extreme differences for specific
locations at the 100 cm level. To summarize, the lower standard
deviations in the HRRR ISM are being driven by the lower VSM standard
deviations occurring below the surface level. It is important to note
that both ISM and VSM standard deviation differences vary throughout the
year, and the evolution of these differences as a function of month and
season are provided in the supporting information document.
6 Conclusions and Future Work
A comparison of 1.6 m ISM between three different NOAA soil moisture
products is conducted. This analysis uniquely includes the HRRR model
with its RUC LSM, the CPC leaky-bucket model and in situ observations
from two national networks. These soil moisture estimates are used in
many operational and research applications, including atmospheric
forecasting, drought monitoring, and assessing flood and fire risks.
Therefore, quantifying differences in these NOAA models to observational
networks across CONUS is critical.
Several conclusions are drawn from these comparisons.
1) The HRRR and CPC ISMs are both larger (i.e., wetter) in the driest
regions and smaller (i.e., drier) in the wettest regions as compared to
in situ observations.
2) These differences in the HRRR and in situ ISM amounts are largely
caused by deep soil levels (~100 cm below ground).
Shallower layers have similar trends to the deeper layers but have
smaller differences, and thus a weaker contribution to the ISM
differences.
3) The in situ observations have the largest ISM standard deviations,
followed by the CPC leaky-bucket model and the HRRR model.
4) The HRRR soil moisture standard deviations compare well with the in
situ standard deviations near the surface, but large differences are
present at 5 cm below the surface and deeper.
The soil moisture differences presented in this study can be caused by a
variety of reasons. In terms of modeled soil moisture, biases in the
input datasets (i.e., precipitation or radiation), whether they come
from a coupled atmospheric model in the case of HRRR or external sources
in the case of CPC, have been shown to lead to biases in land surface
model calculations (e.g., Mitchell et al., 2004; Min et al., 2021).
Choices in the land surface model structure, such as the number and
thickness of soil layers, the representation of soil and vegetation, and
other model parameters, can also lead to biases in soil moisture
prediction (e.g., Mitchel et al., 2004; Xia et al., 2014; 2015b). Min et
al. (2021) found that snowmelt, freezing/thawing, and/or biases in
precipitation and evapotranspiration led to differences in HRRR soil
moisture as compared to in situ observations in New York and that the
most relevant processes causing these differences varied throughout the
year. The results in this study demonstrate consistent, region-dependent
biases in NOAA modeled soil moisture as compared to in situ observations
across CONUS, and future research should focus on understanding the
model processes that are causing these biases.
Our results also provide important context to the current users of these
models and observations. For example, HRRR’s land data assimilation
system has recently undergone changes that primarily impact the
near-surface soil state (Benjamin et al., 2022). The comparisons
presented in this study do show better performance of HRRR soil moisture
near the surface and thus may provide a first step towards understanding
the impact of these model changes. Furthermore, these results can assist
with the continued development and refinement of soil moisture models
and products. The analyses presented here are currently being utilized
for preparing training and validation data for a machine learning
algorithm that uses data from the Advanced Baseline Imager on-board
NOAA’s Geostationary Operational Environmental Satellite to estimate the
soil moisture state at very high resolution (i.e., on the order of
~1 km). With a recent focus on land-atmosphere coupling
and a continued shift towards higher-resolution models, such a product
could be used as a supplementary input for strongly coupled land
atmosphere data assimilation in the next generation of atmospheric
models.
Acknowledgments
This work was supported by the NOAA FY21 High Performance Computing and
Communications Program’s Information Technology Incubator. We would also
like to acknowledge helpful feedback on and interest in this work from
Liaofan Lin, Tanya Smirnova, Stan Benjamin, Curtis Alexander and Eric
James.
Open Research
Several in situ and model datasets are used in this study. The USCRN
data (Palecki et al., 2013) and the SCAN data (SCAN, 2016) will be
archived upon publication. This archiving process is underway and will
be with the Mountain Scholar repository through Colorado State
University. We have uploaded a copy of these data as Supporting
Information for the review process. The CPC data was accessed via
https://ftp.cpc.ncep.noaa.gov/wd51yf/us/w_daily/ through the U.S. Data
download link on the NOAA CPC product webpage
(https://www.cpc.ncep.noaa.gov/products/Soilmst_Monitoring/). The HRRR
operational model data (HRRRv3) was stored and accessed via the NOAA
Hera supercomputer and is publicly archived at either
https://registry.opendata.aws/noaa-hrrr-pds/ or
https://console.cloud.google.com/marketplace/product/noaa-public/hrrr.
The analysis code used to generate the analyses and figures in this
manuscript are available at
https://github.com/pjmarinescu/CIRA_Soil_Moisture and will also be
archived in the same Mountain Scholar repository as the data upon
publication.