Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1261-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1261-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2026
Research article |
| 03 Mar 2026
| 03 Mar 2026
Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
Sanghee Jun
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
Hyunglok Kim
Department of Environment and Energy Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Kyung-Hee Seol
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
In-Hyuk Kwon
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
Eunkyu Kim
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
Sujeong Cho
Korea Institute of Atmospheric Prediction Systems, Seoul 07071, South Korea
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Short summary
This study investigates how combining satellite soil moisture data from radar and radiometer measurements influences weather forecasts in the Korean Integrated Model. Using a weakly coupled data assimilation approach—where atmospheric and land observations are separately assimilated for their respective variables—we found that assimilating both types of soil moisture data together improves forecasts of humidity, temperature, and rainfall compared to using data from a single sensor.
This study investigates how combining satellite soil moisture data from radar and radiometer...
