Plain Language Summary
Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for predicting where clouds will form, assessing drought and fire weather risks, and assisting with decisions for agricultural production. There are multiple estimates of soil moisture available, and in this study, we compare three different types of soil moisture estimates from the National Oceanic and Atmospheric Administration (NOAA), including two types of soil moisture models and direct observations, as well as direct observations from the United States Department of Agriculture. Both models have soil moisture values that are too wet in dry regions of the United States and too dry in wet regions. The modeled soil moisture also does not change as rapidly from day-to-day as they do in the direct observations of soil moisture. Further analysis at different soil depths shows which depths have the largest differences between these three soil moisture estimates. Understanding these NOAA soil moisture estimate differences is important for forecasters and decision makers and can be useful for the further development of atmospheric and land surface models.
1 Introduction
Knowledge of soil moisture is essential for many Earth system applications, such as forecasting cloud formation (e.g., Ek and Holtslag, 2004), monitoring drought, flood and fire risks (e.g., Svoboda et al., 2002; Rigden et al., 2020), and providing instrumental information for agricultural production (e.g., Madadgar et al., 2017). As such, several advancements in the estimation and utilization of soil moisture have recently transpired. For example, in efforts to improve the accuracy of numerical weather prediction (NWP) and climate models, model developers have focused on increasing the coupling between the land surface and atmosphere components of their data assimilation systems to eliminate persistent atmospheric prediction biases (e.g., Benjamin et al., 2022). Furthermore, NWP models are also beginning to explore the direct assimilation of new soil moisture observations (e.g., Carrera et al., 2019; Muñoz-Sabater et al., 2019; Lin and Pu, 2020).
For these reasons, it is critical to understand the differences in the available soil moisture estimates from models or observations that are used in science, forecasting or agricultural applications. Different estimates of soil moisture exist across the continental United States (CONUS), each with its own benefits and shortfalls. For example, in situ observations are often considered to be the most accurate and are therefore used as a benchmark. However, they have limited spatial coverage (e.g., Quiring et al., 2016). Other products, such as those from low Earth orbiting satellites, have lower temporal and spatial resolution (e.g., Liu et al., 2016). Soil moisture estimates from models depend on many assumptions and reflect the influence of observation-based data to different degrees (e.g., Smirnova et al., 1997; Huang et al., 1996; Mitchell et al., 2004). Several recent studies have made significant progress in comparing many of the available soil moisture estimates. Some studies have compared soil moisture temporal variability and memory between many large-scale land surface models (LSMs) and in situ soil monitoring networks across the CONUS, noting certain biases and uncertainties in the various estimates (e.g., Robock et al., 2003; Xia et al., 2014; 2015a; Dirmeyer et al., 2016). Recent studies have extended soil moisture comparisons to include soil moisture retrievals from new satellite platforms (e.g., Shellito et al., 2016; Pan et al., 2016; Ford and Quiring, 2019).
While various LSMs have been compared to in situ observations for several decades, these prior studies have primarily focused on models with horizontal resolutions on the scales of ⅛ degree or larger (i.e., the North American Land Data Assimilation System models; Mitchell et al., 2004; Xia et al., 2012). Given the local, mesoscale variability of soil moisture process and the subsequent impacts of soil moisture on atmospheric prediction (e.g., Koster et al., 2004; Taylor et al., 2011), high-resolution models should also be tested. Recently, Min et al. (2021) compared near-surface atmospheric and soil variables from the High-Resolution Rapid Refresh (HRRR) model (Dowell et al., 2022; James et al., 2022), which has horizontal grid spacing of 3 km, to observations from the New York State Mesonet. They found that soil moisture was underestimated in HRRR, which contributed to warm and dry biases in atmospheric forecasts. However, how does soil moisture from the HRRR model (NOAA’s current operational, convection-allowing model) compare to soil moisture estimates across all of CONUS?
In this study, we focus on a comparison of soil moisture estimates from NOAA. In particular, we uniquely include soil moisture estimates across CONUS from the NOAA operational HRRR model, which utilizes the RUC land surface model (RUC LSM, Smirnova et al., 1997). We also include the NOAA Climate Prediction Center (CPC) leaky-bucket hydrological model (Huang et al., 1996; van den Dool et al., 2003) and in situ observations from two nationwide networks: the NOAA/NCEI United States (US) Climate Reference Network (USCRN; Bell et al., 2013) and the US Department of Agriculture Soil Climate Analysis Network (SCAN; Schaefer et al., 2007). This work provides an assessment of the similarities and differences of soil moisture amounts and variance across three different products, which are all used in various operational and research applications.
2 Soil Moisture Data
2.1 In Situ Observations
The study uses in situ soil moisture observations from two nationwide networks. USCRN provides climate monitoring measurements of atmospheric and soil properties. To increase the coverage of the in situ observations, SCAN is also included. SCAN uses similar sensors to USCRN (i.e., Hydra Probe sensors) and typically have volumetric soil moisture (VSM; mwater3 / msoil3) measurements at the same soil depths (~5, ~10, ~20, ~50, and ~100 cm) as USCRN. Only data from these five levels are used for consistency. Dirmeyer et al. (2016) also found that these two networks have similar error variances. The observations represent point measurements of the soil moisture at specific sites across the US. Daily data are used in this study and represent the average VSM of the entire 24-hour period based on local standard time.
2.2 HRRR Model
The HRRR model is NOAA’s operational, convection-allowing model, which has 3 km horizontal grid spacing and covers CONUS with a one-hour temporal refresh rate (Dowell et al., 2022; James et al., 2022). In this study, we use HRRRv3, which was operational between 12 July 12 2018 and 2 December 2020. The HRRR model utilizes a one-dimensional land surface model (RUC LSM; Smirnova et al., 1997), which predicts heat and moisture transfer vertically throughout the soil column. The RUC LSM has undergone several enhancements over the years, including increasing its resolution and incorporating new features, such as snow and ice models (Smirnova et al., 2000; 2016). The current version predicts VSM at nine vertical levels (0, 1, 4, 10, 30, 60, 100, 160 and 300 cm) and utilizes cycling of soil conditions over several years to better capture the soil moisture state. The HRRR utilizes moderately coupled land data assimilation, meaning that near-surface atmospheric data assimilation increments are used to adjust the soil analysis (e.g., Benjamin et al., 2022). Given the recent and continued development of the HRRR data assimilation system and land surface model, it is critical for assessments of HRRR’s soil moisture to other estimates. This study provides a benchmark for HRRR soil moisture estimates in support of the continued development of NOAA’s land surface prediction capabilities in the Unified Forecast System. The focus of this study is on the analyzed soil moisture field, rather than forecast fields, and thus our results are most directly relevant to the LSM and data assimilation system development.
2.3 CPC Leaky-Bucket Model
The CPC soil moisture product utilizes a leaky-bucket model that solves the time tendency equation in soil moisture over a region from several inputs: precipitation minus evapotranspiration, net streamflow divergence and net groundwater loss (Huang et al., 1996; van den Dool et al., 2003). These inputs to the time tendency equation for soil moisture have been improved over the years with new observations and parameterizations (Fan and van den Dool, 2004; Arevalo et al., 2021). The CPC model provides 1.6 m deep integrated soil moisture (ISM, mm), and these estimates are provided daily for each of the NOAA climate divisions across the United States (Guttman and Quayle, 1996). There are typically about 7-10 climate divisions per state, although there are fewer for states with smaller geographical areas, like those in the Northeast United States. The CPC soil moisture data are used as an input to the United States Drought Monitor (Svoboda et al., 2002) and continues to be used as a reference data set in various soil moisture application studies, from assessing soil moisture impacts on carbon fluxes (e.g., Yao et al., 2021) to understanding climate impacts on agricultural production (e.g., Atiah et al., 2022).
3 Methods
3.1 ISM and VSM Comparisons
Since the CPC product only provides 1.6 m ISM, VSM values from the in situ and HRRR data are vertically integrated in order to compare 1.6 m ISM in all three datasets. The VSM values are assumed to represent the mean value over a depth between the midpoints of the specified levels, as has been done in other studies (e.g., Dirmeyer et al., 2016; Ford and Quiring, 2019). For example, the 10 cm VSM observation in the in situ data is assumed to represent the average VSM for the layer between 7.5 cm (i.e., the midpoint between 5 and 10 cm) and 15 cm (i.e., the midpoint between 10 and 20 cm). The 100 cm VSM in the in situ data is also assumed to be constant to the depth of 160 cm. The HRRR ISM calculations are better constrained than the in situ ISM calculations, since the HRRR VSM data span 3.0 m below ground using 9 levels. VSM values are also compared between the HRRR and in situ data to glean whether certain vertical levels are driving differences between these two datasets. An understanding of soil moisture differences at varying depths is also critical since soil moisture’s role in Earth system processes is depth dependent. In terms of spatial comparisons, the HRRR and CPC data are linearly interpolated to the locations of the in situ stations. The analysis is completed over a ~2.4 year period from 12 July 2018 through 2 December 2020, which is the timeframe that HRRRv3 was operational. By confining the analyses to this time frame, uncertainties associated with model version changes are avoided.
3.2 Quality Control
In situ data provide the most direct physical estimate of soil moisture, but it is important to ensure that the in situ data are of the highest quality. As such, a variety of quality control procedures are undertaken. First, in situ data are only included if they have VSM values available at all five vertical levels (~5cm, ~10cm, ~20cm, ~50cm, and ~100cm), since missing data could lead to larger uncertainties in the ISM calculation. Second, in situ stations directly along the coast are removed due to unphysical spatial interpolations from the CPC and HRRR data. From the remaining in situ data, we estimate the ratio of error variance to ISM variance using the method defined in Robock et al. (1995) and used in more recent soil moisture comparisons studies (e.g., Dirmeyer et al., 2016). In essence, soil moisture can be well approximated by a red-noise process (i.e., first-order Markov process; e.g., Delworth and Manabe, 1988; Vinnikov and Yeserkepova, 1991) with the natural logarithm of the soil moisture autocorrelation (r) decreasing linearly with increased lag times (𝜏). For this study, autocorrelations are computed for 𝜏 of 1-30 days for each station’s ISM daily anomalies. A linear fit is applied to the ln(r) versus 𝜏 data and is extrapolated to 𝜏 = 0. Deviations from 1 at 𝜏 = 0 can be used to solve for to the ratio of error variance to ISM variance (e.g., Robock et al., 1995). For this study, stations where this ratio is greater than 0.08 are removed. This error ratio threshold is in-line with estimates of the mean error ratio of the USCRN and SCAN networks (Dirmeyer et al., 2016). While this error variance ratio threshold results in the removal of 63 (~27%) of the 235 in situ stations, it provides more confidence that only the highest quality in situ observations are being used in the analyses. Even with the significant reduction of the in situ data, the stations span the entirety of the CONUS (Figure 1, dots). Different thresholds, autocorrelation lengths and dataset lengths were tested and did not qualitatively impact the results.