A Cloud-Dependent 1DVAR Precipitation Retrieval Algorithm for FengYun-3D Microwave Soundings: A Case Study in Tropical Cyclone Mekkhala

A cloud-dependent 1-D variational (1DVAR) precipitation retrieval algorithm applied to FengYun-3D microwave soundings (CD1DVAR-FY3DMS) is developed in this study. Compared with the current 1DVAR precipitation retrieval framework, cloud scene identification (CSI) is first proposed to delineate the different weather conditions including clear sky, stratiform clouds, and convective clouds. Results of this study in a typical tropical cyclone event demonstrate that: 1) the precipitation retrievals considering CSI (correlation coefficient similar to 0.72 and root mean square error similar to 1.63 mm/h) outperform those without distinguishing cloud scenes (correlation coefficient similar to 0.66 and root mean square error similar to 1.82 mm/h); 2) identifying cloud scenes in a variational scheme could significantly improve the accuracy of retrieving heavy precipitation volumes, with the capturing abilities improved by similar to 100%; and 3) the FengYun-3 constellation has great potential to complement global microwave retrievals. In addition, these findings could provide valuable references and pathfinders for further improving the retrieval accuracy of microwave-based precipitation estimates, especially for strong convective zones.