Assimilating MWTS-2, ATMS, and AMSU-A Radiances for Rainfall Forecast in an Operational East African NWP System

Microwave radiance data assimilation (DA) enhances initial conditions for numerical weather prediction (NWP) and shows great potential for improving forecasts in tropical regions like East Africa, where observational data scarcity and complex tropical dynamics present significant challenges. Effectiveness of radiance assimilation is a function of variations in channel sensitivity to local atmospheric conditions and region-specific bias characteristics. However, microwave radiance assimilation in Limited-Area Models (LAMs) over East Africa remains largely unexplored. This study investigates the impact of assimilating microwave radiance channels with weighting functions peaking in the troposphere and lower stratosphere on rainfall forecasts over East Africa from a five-satellite constellation: the Microwave Temperature Sounder-2 (MWTS-2) onboard Fengyun-3D (FY-3D), the Advanced Technology Microwave Sounder (ATMS) onboard JPSS, and the Advanced Microwave Sounding Unit-A (AMSU-A) onboard NOAA-15/18/19 satellites. The 6-h cycling DA experiments over a convectively active 15-day period show that assimilation of ATMS and AMSU-A radiances enhances representation of initial conditions, thereby reducing analysis and forecast errors. Assimilation of MWTS-2 radiances improves the analysis and forecasts further, especially for the tropospheric thermodynamic fields. The joint multi-microwave assimilation fills critical observation gaps over East Africa, allowing realistic simulations of diurnal precipitation trends, and capturing rainfall intensities exceeding 50 mm in 24 h, especially for T+12-h to T+24-h lead times. These findings are validated by a high-intensity rainfall case over Mandera, where spatio–temporal consistency is observed in instability and convection triggering. Forecast evaluation metrics have confirmed enhanced rainfall forecast skill for deep and rapidly developing convective systems. The study provides valuable insights into the gains of assimilating microwave radiance data over tropical regions, particularly in East Africa.