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

Kwon, Yonghwan; Jun, Sanghee; Kim, Hyunglok; Seol, Kyung-Hee; Kwon, In-Hyuk; Kim, Eunkyu; Cho, Sujeong

doi:10.5194/hess-30-1261-2026

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.

https://doi.org/10.5194/hess-30-1261-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 30, issue 4

Research article

|

03 Mar 2026

Research article |

| 03 Mar 2026

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

Yonghwan Kwon, Sanghee Jun, Hyunglok Kim, Kyung-Hee Seol, In-Hyuk Kwon, Eunkyu Kim, and Sujeong Cho

Supplement

https://doi.org/10.5194/hess-30-1261-2026-supplement

Data sets

SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture P. E. O'Neill et al. https://doi.org/10.5067/K7Y2D8QQVZ4L

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.