Physically-Constrained Assimilation of All-Sky Radiances from FY-3 Microwave Sounders for Extreme Precipitation Forecast

Accurate forecasting of extreme precipitation using mesoscale models remains a major challenge, particularly for warm-sector heavy rainfall events driven by complex cloud-microphysical processes that are poorly constrained by conventional observations. Satellite microwave radiances offer critical thermodynamic and hydrometeor information, yet their assimilation under cloudy and precipitating conditions requires careful treatment of radiative transfer errors. This study develops a physically constrained assimilation method for all-sky radiances from microwave temperature and humidity sounders (MWTS and MWHS) aboard the new-generation Fengyun-3 (FY-3) satellites, integrated into the China Meteorological Administration (CMA) MESO system. Within the CMA MESO 3DVAR system, the Advanced Radiative Transfer Modeling System (ARMS) is employed as the fast satellite observation operator, incorporating three key components: (1) a dynamically adaptive land emissivity parameterization, (2) a microphysics-consistent adaptive formulation for hydrometeor effective radius, and (3) a new delta-M multiple-scattering scheme. Three experiments were conducted for a record-breaking rainfall event over Hunan Province, China: a control run without all-sky radiance assimilation (CTRL), an all-sky configuration (EXPR1) utilizing the baseline ARMS, and an enhanced configuration (EXPR2) with an improved ARMS scattering module. Results show that EXPR2 reduces systematic biases in moisture-sensitive channels by approximately 7 K and decreases random errors by nearly 50% relative to CTRL, indicating substantially improved observation-minus-background statistics in cloudy scenes. This improvement further enables more realistic storm predictions, accurately reproducing the observed quasi-stationary convective core (> 45 dBZ) and the maximum rainfall exceeding 400 mm. Spatial verification using the Fractional Skill Score (FSS) demonstrates robust and statistically meaningful skill gains across multiple precipitation thresholds and spatial scales. At the extreme 100 mm threshold, EXPR2 is the only configuration that achieves useful skill at 50–100 km scales, whereas CTRL exhibits negligible skill. These findings underscore the value of physically constrained all-sky operator design for enhancing the prediction of high-impact precipitation events, with implications for operational convection-allowing data assimilation systems.