Synergistic Impact of Simultaneously Assimilating Radar- and Radiometer-Based Soil Moisture Retrievals on the Performance of Numerical Weather Prediction Systems

Abstract. This study evaluates the impact of simultaneously assimilating soil moisture (SM) retrievals from ASCAT (Advanced SCATterometer) and SMAP (Soil Moisture Active Passive) into the Korean Integrated Model (KIM) using a weakly coupled data assimilation (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 SM retrievals. The impact of SM DA is evaluated using independent reference datasets, assessing its influence on SM analysis and numerical weather prediction (NWP) performance. Overall, assimilating ASCAT or SMAP SM data into the LSM improves global SM analysis accuracy by 4.0% and 10.5%, respectively, compared to the control case without SM DA, achieving the most significant enhancements in croplands. Relative to single-sensor SM DA, multi-sensor SM 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 SM products are observed in the 2-m air temperature analysis and forecast, especially when both SM products have a positive impact. The results also demonstrate that precipitation forecast skill, particularly in predicting precipitation events, can be enhanced by constraining the modeled SM with multiple SM retrievals from different sources. This paper discusses remaining issues for future studies to further improve the weather prediction performance of the KIM-LIS multi-sensor SM DA system.