[A-CG36] Satellite Earth Environment Observation

[ACG36-19] Advancing GSMaP Precipitation by Land Data Assimilation and Data Science

*Shunji Kotsuki¹, Yuka Muto¹, Kenta Shiraishi¹, Daiya Shiojiri¹, Yuina Kataoka², Takuya Funatomi², Kaya Kanemaru³ (1.Center for Environmental Remote Sensing, Chiba University, 2.Nara Institute of Science and Technology, 3.National Institute of Information and Communications Technology)

Keywords:GSMaP, Land Data Assimilation, Deep Learning, Super-resolution

This study aims to improve the Global Satellite Mapping of Precipitation (GSMaP) by incorporating multiple satellite data and leveraging advanced data science techniques. The GSMaP provides near-real-time global precipitation using Microwave Radiometers (MWR) mounted on polar-orbiting satellites, while referring to surface precipitation data from satellite-borne precipitation radars. Owing to the constellation of polar-orbiting satellites, GSMaP issues the global precipitation every hour at the spatial resolution of 0.10 degree. Despite these advancements, GSMaP confronts challenges in accurately estimating precipitation in regions lacking MWR observations. Here, our research aims to improve the GSMaP precipitation by integrating spatially sparse global gauge observations. We succeeded in improving gauge-based global precipitation estimates using the algorithm of the local ensemble transform Kalman filter, and are working to adjust the GSMaP precipitation against the gauge-based precipitation estimates. In addition, our research incorporates data science techniques such as deep learning to bridge the gap by geometrically extrapolating observable MWR-based precipitation based on the atmospheric motion vectors, and conduct super-resolution of precipitation. Preliminary experiments provide encouraging results, showcasing improved spatial precipitation estimates using deep learning models. This presentation will include the most recent progress by the time of the conference.