What question did this study set out to answer?

To assess the benefits of combining radar and radiometer soil moisture data in numerical weather prediction systems.

March 5, 2026Open Access

Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems

Key Points

To assess the benefits of combining radar and radiometer soil moisture data in numerical weather prediction systems.
Utilized ASCAT and SMAP soil moisture retrievals in the Korean Integrated Model (KIM)
Incorporated a weakly coupled data assimilation framework based on NASA's Land Information System (LIS)
Evaluated impacts on soil moisture analysis and numerical weather prediction performance
Assimilating ASCAT improved global soil moisture accuracy by 4.0%
Assimilating SMAP improved accuracy by 10.5%
Multi-sensor assimilation provided balanced improvements in humidity and temperature forecasts
Significant enhancements observed in 2 m air temperature analysis with combined data
Precipitation forecast skill improved but varied across regions and events

Abstract

Abstract. The combined use of independent soil moisture data from radar and radiometer measurements in data assimilation (DA) systems is expected to yield synergistic performance gains due to their complementary strengths. This study evaluates the impact of simultaneously assimilating soil moisture retrievals from ASCAT (Advanced SCATterometer) and SMAP (Soil Moisture Active Passive) into the Korean Integrated Model (KIM) using a weakly coupled DA framework based on the National Aeronautics and Space Administration's Land Information System (LIS). The Noah land surface model (LSM) within LIS, which is the same as that used in KIM, is used to simulate land surface states and assimilate soil moisture retrievals. The impact of soil moisture DA is evaluated using independent reference datasets, assessing its influence on soil moisture analysis and numerical weather prediction performance. Overall, assimilating single-sensor soil moisture data, ASCAT or SMAP, into the LSM improves global soil moisture analysis accuracy by 4.0 % and 10.5 %, respectively, compared to the control case without soil moisture DA, achieving the most significant enhancements in croplands. Relative to single-sensor soil moisture DA, multi-sensor soil moisture DA yields more balanced skill enhancements for both specific humidity and air temperature analyses and forecasts. The most pronounced synergistic improvements by simultaneously assimilating both soil moisture products are observed in the 2 m air temperature analysis and forecast, especially when both soil moisture products have a positive impact. Precipitation forecast skill also improves with multi-sensor soil moisture DA, although the improvements are not consistent across regions and events. This paper discusses remaining issues for future studies to further improve the weather prediction performance of the KIM-LIS multi-sensor soil moisture DA system.

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