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- Author or Editor: Rolf H. Reichle x
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Abstract
The importance of horizontal error correlations in background (i.e., model forecast) fields for large-scale soil moisture estimation is assessed by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the U. S. Great Plains, gauge-based precipitation data is used to force the “true” model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation datasets is thought to be representative of errors that might be encountered in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatiotemporal pattern and expected errors of retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. After filter calibration, average actual estimation errors in the (volumetric) root zone moisture content are 0.015 m3 m−3 for the 3D-EnKF, 0.019 m3 m−3 for the 1D-EnKF, and 0.036 m3 m−3 without assimilation. Clearly, taking horizontal error correlations into account improves estimation accuracy. Soil moisture estimation errors in the 3D-EnKF are smallest for a correlation scale of 2° in model parameter and forcing errors, which coincides with the horizontal scale of difference fields between gauge-based and reanalysis precipitation. In this case the 3D-EnKF requires 1.6 times the computational effort of the 1D-EnKF, but this factor depends on the experiment setup.
Abstract
The importance of horizontal error correlations in background (i.e., model forecast) fields for large-scale soil moisture estimation is assessed by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the U. S. Great Plains, gauge-based precipitation data is used to force the “true” model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation datasets is thought to be representative of errors that might be encountered in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatiotemporal pattern and expected errors of retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. After filter calibration, average actual estimation errors in the (volumetric) root zone moisture content are 0.015 m3 m−3 for the 3D-EnKF, 0.019 m3 m−3 for the 1D-EnKF, and 0.036 m3 m−3 without assimilation. Clearly, taking horizontal error correlations into account improves estimation accuracy. Soil moisture estimation errors in the 3D-EnKF are smallest for a correlation scale of 2° in model parameter and forcing errors, which coincides with the horizontal scale of difference fields between gauge-based and reanalysis precipitation. In this case the 3D-EnKF requires 1.6 times the computational effort of the 1D-EnKF, but this factor depends on the experiment setup.
Abstract
A newly developed, weakly coupled land and atmosphere data assimilation system for NASA’s Global Earth Observing System model is presented, and used to demonstrate the benefit of assimilating satellite soil moisture into an atmospheric reanalysis. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated into a system that uses the same model, atmospheric assimilation system, and atmospheric observations as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The atmosphere is sensitive to soil moisture only under certain conditions. Hence, while the globally averaged model improvements were small, regionally, the soil moisture assimilation induced some substantial improvements. For example, in a large region spanning from western Europe across southern Russia, the soil moisture assimilation decreased the RMSE against independent station observations of daily maximum 2-m temperature (
Abstract
A newly developed, weakly coupled land and atmosphere data assimilation system for NASA’s Global Earth Observing System model is presented, and used to demonstrate the benefit of assimilating satellite soil moisture into an atmospheric reanalysis. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated into a system that uses the same model, atmospheric assimilation system, and atmospheric observations as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The atmosphere is sensitive to soil moisture only under certain conditions. Hence, while the globally averaged model improvements were small, regionally, the soil moisture assimilation induced some substantial improvements. For example, in a large region spanning from western Europe across southern Russia, the soil moisture assimilation decreased the RMSE against independent station observations of daily maximum 2-m temperature (
Abstract
Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression—utilizing independent variables based on multilayer and temporally lagged SM estimates—can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multilayer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.
Abstract
Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression—utilizing independent variables based on multilayer and temporally lagged SM estimates—can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multilayer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.
Abstract
NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.
Abstract
NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.
Abstract
Assimilation of data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites yielded improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. The authors assimilated GRACE-derived monthly terrestrial water storage (TWS) anomalies for each of the four major subbasins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother from January 2003 to May 2006. Compared with the open-loop CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four subbasins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four subbasins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of eight cases, GRACE assimilation led to increased correlation between TWS estimates and gauged river flow, indicating that data assimilation has considerable potential to downscale GRACE data for hydrological applications.
Abstract
Assimilation of data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites yielded improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. The authors assimilated GRACE-derived monthly terrestrial water storage (TWS) anomalies for each of the four major subbasins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother from January 2003 to May 2006. Compared with the open-loop CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four subbasins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four subbasins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of eight cases, GRACE assimilation led to increased correlation between TWS estimates and gauged river flow, indicating that data assimilation has considerable potential to downscale GRACE data for hydrological applications.
Abstract
Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.
Abstract
Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.
Abstract
In the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here aspects of the MERRA-2 land surface energy budget and 2-m air temperatures 
Abstract
In the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here aspects of the MERRA-2 land surface energy budget and 2-m air temperatures
Abstract
This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge–calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear.
Abstract
This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge–calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear.
Abstract
This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.
Abstract
This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.
Abstract
The efficiency of assimilating near-surface soil moisture retrievals from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations in a Land Data Assimilation System (LDAS) is assessed using satellite rainfall forcing and two different satellite rainfall error models: a complex, multidimensional satellite rainfall error model (SREM2D) and the simpler (control) model (CTRL) used in the NASA Goddard Earth Observing System Model, version 5 LDAS. For the study domain of Oklahoma, LDAS soil moisture estimates improve over the satellite retrievals and the open-loop (no assimilation) land surface model estimates, exhibiting higher daily anomaly correlation coefficients (e.g., 0.36 in the open loop, 0.38 in the AMSR-E, and 0.50 in LDAS for surface soil moisture). The LDAS soil moisture estimates also match the performance of a benchmark model simulation forced with high-quality radar precipitation. Compared to using the CTRL rainfall error model in LDAS, using the more complex SREM2D exhibits only slight improvements in soil moisture estimates.
Abstract
The efficiency of assimilating near-surface soil moisture retrievals from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations in a Land Data Assimilation System (LDAS) is assessed using satellite rainfall forcing and two different satellite rainfall error models: a complex, multidimensional satellite rainfall error model (SREM2D) and the simpler (control) model (CTRL) used in the NASA Goddard Earth Observing System Model, version 5 LDAS. For the study domain of Oklahoma, LDAS soil moisture estimates improve over the satellite retrievals and the open-loop (no assimilation) land surface model estimates, exhibiting higher daily anomaly correlation coefficients (e.g., 0.36 in the open loop, 0.38 in the AMSR-E, and 0.50 in LDAS for surface soil moisture). The LDAS soil moisture estimates also match the performance of a benchmark model simulation forced with high-quality radar precipitation. Compared to using the CTRL rainfall error model in LDAS, using the more complex SREM2D exhibits only slight improvements in soil moisture estimates.
