Optimizing Synergistic Strategies of IR and MW Radiance in All-sky Data Assimilation for Heavy Precipitation Forecasting

This study explores how different integration strategies for infrared (IR) and microwave (MW) brightness temperatures (TBs) impact precipitation forecasting within a 3D-Var all-sky radiance data assimilation (DA) framework. To improve heavy rainfall forecasts over the Korean Peninsula, we propose an asynchronous assimilation strategy. In this approach, when IR and MW observations overlap, we prioritize MW TBs and intentionally exclude IR TBs to minimize potential redundancies and physical inconsistencies in cloud representation. We compared this proposed method against two common synchronous strategies: one assimilating clear-sky IR with all-sky MW, and another integrating both IR and MW under all-sky conditions. Using a heavy precipitation case over Korea as a testbed, we assimilated AHI (Himawari-8), GMI (GPM), and AMSR2 (GCOM-W) data to evaluate their impacts on hydrometeor analysis and subsequent forecast accuracy. Our results indicate that the asynchronous strategy leads to a more balanced vertical distribution of solid and liquid hydrometeors, resulting in the most reliable precipitation forecasts. In contrast, the all-sky IR+MW strategy tended to overemphasize upper-level clouds while reducing low-level moisture, leading to biased localized rainfall. Meanwhile, the clear-sky IR+MW approach failed to adequately capture upper-level stratiform structures. These findings suggest that an optimized, sensor-specific integration strategy is essential for maximizing the benefits of multi-platform satellite data assimilation.