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Abstract
Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals (satellite measurements) with respect to true fields. These metrics are related; nevertheless, each has advantages and disadvantages. In this study the authors explore the relation between the RMSE and correlation metrics in the presence of biases in the mean as well as in the amplitude of fluctuations (standard deviation) between estimated and true fields. Such biases are common, for example, in satellite retrievals of soil moisture and impose constraints on achievable and meaningful RMSE targets. Last, an approach is introduced for converting a requirement in an application’s product into a corresponding requirement for soil moisture accuracy. The approach can help with the formulation of soil moisture measurement requirements. It can also help determine the utility of a given retrieval product for applications.
Abstract
Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals (satellite measurements) with respect to true fields. These metrics are related; nevertheless, each has advantages and disadvantages. In this study the authors explore the relation between the RMSE and correlation metrics in the presence of biases in the mean as well as in the amplitude of fluctuations (standard deviation) between estimated and true fields. Such biases are common, for example, in satellite retrievals of soil moisture and impose constraints on achievable and meaningful RMSE targets. Last, an approach is introduced for converting a requirement in an application’s product into a corresponding requirement for soil moisture accuracy. The approach can help with the formulation of soil moisture measurement requirements. It can also help determine the utility of a given retrieval product for applications.
Abstract
The small-scale (10 to 100 m) and local-scale (100 m to 10 km) effects of topography (elevation, slope, and aspect) and snow redistribution by wind on the evolution of the snowmelt are investigated. The chosen study area is the 142 km2 Upper Kuparuk River basin located on the North Slope of Alaska. Two land surface models (LSMs) designed for regional and global climate studies apply different techniques to resolve these additional processes and features and their effects on snowmelt. One model uses a distributed approach to simulate explicitly the effects of topography on snowmelt at a 131-m resolution across the entire Upper Kuparuk watershed. By contrast, the other LSM employs a simple parameterization to implicitly resolve the effects of wind-blown snow on the hydrology of the Upper Kuparuk basin. In both cases, the incorporation of these local- and small-scale features within the LSMs leads to significant heterogeneity in the 1997 end-of-winter spatial distribution of snow cover in the Upper Kuparuk watershed. It is shown that the consideration of subgrid-scale snow-cover heterogeneity over complex Arctic terrain provides a better representation of the end-of-winter snow water equivalent, an improved simulation of the timing and amount of water discharge of the Upper Kuparuk River, and an alteration of other surface energy and water budget components.
Abstract
The small-scale (10 to 100 m) and local-scale (100 m to 10 km) effects of topography (elevation, slope, and aspect) and snow redistribution by wind on the evolution of the snowmelt are investigated. The chosen study area is the 142 km2 Upper Kuparuk River basin located on the North Slope of Alaska. Two land surface models (LSMs) designed for regional and global climate studies apply different techniques to resolve these additional processes and features and their effects on snowmelt. One model uses a distributed approach to simulate explicitly the effects of topography on snowmelt at a 131-m resolution across the entire Upper Kuparuk watershed. By contrast, the other LSM employs a simple parameterization to implicitly resolve the effects of wind-blown snow on the hydrology of the Upper Kuparuk basin. In both cases, the incorporation of these local- and small-scale features within the LSMs leads to significant heterogeneity in the 1997 end-of-winter spatial distribution of snow cover in the Upper Kuparuk watershed. It is shown that the consideration of subgrid-scale snow-cover heterogeneity over complex Arctic terrain provides a better representation of the end-of-winter snow water equivalent, an improved simulation of the timing and amount of water discharge of the Upper Kuparuk River, and an alteration of other surface energy and water budget components.
Abstract
In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere–Land Exchange Inverse (ALEXI) Source–Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source–sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source–sink processes within modern LSMs.
Abstract
In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction, and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, efforts to parameterize these processes within land surface models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy flux predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the top-down remote sensing retrieval. Based on this concept, the authors introduce the Atmosphere–Land Exchange Inverse (ALEXI) Source–Sink for Evapotranspiration (ASSET) index derived from comparisons between ALEXI remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM, version 3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies, and tile drainage density within the contiguous United States verify the ability of ASSET to identify regions where neglected soil water source–sink processes may be impacting modeled surface energy fluxes. Consequently, ASSET appears to provide valuable information for ongoing efforts to improve the parameterization of new water source–sink processes within modern LSMs.
Abstract
Accurately measuring interannual variability in terrestrial evapotranspiration ET is a major challenge for efforts to detect trends in the terrestrial hydrologic cycle. Based on comparisons with annual values of terrestrial evapotranspiration 
Abstract
Accurately measuring interannual variability in terrestrial evapotranspiration ET is a major challenge for efforts to detect trends in the terrestrial hydrologic cycle. Based on comparisons with annual values of terrestrial evapotranspiration
Abstract
In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.
Abstract
In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.
Abstract
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.
Significance Statement
Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.
Abstract
The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.
Significance Statement
Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.
Abstract
Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.
Abstract
Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.
Abstract
Soil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 soil moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with 1/4°, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, 1/2°, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the instrumental variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10–0.11 compared to an increase of 0.02–0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous United States reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.
Abstract
Soil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 soil moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with 1/4°, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, 1/2°, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the instrumental variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10–0.11 compared to an increase of 0.02–0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous United States reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.
Abstract
The assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.
Abstract
The assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.
Abstract
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
Abstract
The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.
