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The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

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  • 1 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 Science Systems and Applications, Inc., Lanham, Maryland
  • | 3 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland
  • | 4 Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Heverlee, Belgium
  • | 5 Numerical Terradynamic Simulation Group, University of Montana, Missoula, Montana
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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.

Significance Statement

Soil moisture provides an important connection between the land surface water, energy, and carbon cycles. By routinely merging Soil Moisture Active Passive (SMAP) satellite observations and gauge-based precipitation data into a numerical model of land surface water and energy balance processes, NASA generates the global, 9-km resolution, 3-hourly Level-4 soil moisture (L4_SM) data product, which is published with ~2.5-day latency to support Earth science and applications, e.g., drought monitoring. Using additional model simulations and validation against independent measurements, we find that the SMAP and precipitation data contribute similarly, and largely independently, to the accuracy of the L4_SM product. SMAP’s contribution to L4_SM accuracy is particularly large in poorly instrumented regions, including portions of South America, Africa, and central Australia.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0217.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov

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.

Significance Statement

Soil moisture provides an important connection between the land surface water, energy, and carbon cycles. By routinely merging Soil Moisture Active Passive (SMAP) satellite observations and gauge-based precipitation data into a numerical model of land surface water and energy balance processes, NASA generates the global, 9-km resolution, 3-hourly Level-4 soil moisture (L4_SM) data product, which is published with ~2.5-day latency to support Earth science and applications, e.g., drought monitoring. Using additional model simulations and validation against independent measurements, we find that the SMAP and precipitation data contribute similarly, and largely independently, to the accuracy of the L4_SM product. SMAP’s contribution to L4_SM accuracy is particularly large in poorly instrumented regions, including portions of South America, Africa, and central Australia.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0217.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov

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