Proxy Radar Observations from Geostationary Weather Satellite - Enhanced Coverage for Storm Tracking

Ground-based radar is the main means for monitoring and tracking severe storms, however, due to limited coverage, there are not such important data over ocean and mountain areas. On the other hand, geostationary (GEO) weather satellite provides continues observations with seamless coverage with advanced imager, however, such imager has limited capability on penetrating clouds. Combining both satellite and ground radar observations could take both advantages, provides tracking capability close to ground radar with full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation (MORE) to reconstruct radar composite reflectivity (CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B (FY-4B) GEO satellite observations. Two models are developed: an infrared-only (IR-Single) model available for all times, and a visible-infrared (VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, achieving a root mean square error (RMSE) of less than 6 dBZ and a coefficient of determination (R²) greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study provides a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations and offers potential for improved data assimilation in numerical weather prediction (NWP) models.