Geostationary Satellite–Based Proxy Radar Observations: Expanding Coverage for Storm Tracking

Ground-based radar is the primary means by which severe storms are monitored and tracked; however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary (GEO) weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining 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, and both models achieves a root-mean-square error (RMSE) of less than 6 dBZ and a coefficient of determination (R²) of 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 offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction (NWP) models.