Evaluation of All-Sky Assimilation of FY-3C/MWHS-2 on Mei-yu Precipitation Forecasts over the Yangtze-Huaihe River Basin

All-sky (i.e., clear, cloudy, and precipitating conditions) assimilation of microwave observations shows potentially positive impacts on the improvement of the forecasts of cloud-associated weather processes. In this study, a typical mei-yu heavy precipitation event that occurred in 2017 was investigated, and the Weather Research and Forecasting data assimilation (WRFDA) as well as its 3D-Var assimilation scheme (excluding cloud and precipitation control variables) were applied to assimilate the Fengyun-3C (FY-3C) Microwave Humidity Sounder-2 (MWHS-2) observations under clear-sky (excluding the observations that are strongly affected by ice clouds and precipitation) and all-sky conditions. Three experiments including a control experiment without assimilating any observations, clear-sky, and all-sky experiments with only FY-3C/MWHS-2 observations assimilated were carried out. The results show that the all-sky assimilation approach that provides more cloud and precipitation information and increased more than 10% of the satellite data usage than the clear-sky experiment. Meanwhile, as compared with the control experiment, the all-sky assimilation reduced nearly 0.5% of the root mean square errors in the humidity fields, leading to more accurate forecast performances regarding the distribution and intensity of heavy rainfall; but it exhibited a neutral to negative impacts on the wind and temperature. Although the system used to conduct all-sky assimilation is only able to adjust control variables for moisture-, wind-, and temperaturerelated variables in the presence of cloud and does not benefit directly from cloud or precipitation information, the positive effects on heavy rainfall forecasts achieved in this study indicate a potential future benefit regarding disaster prevention and mitigation.

Automated summary

The all-sky assimilation of microwave observations has potentially positive impacts on improving cloud-associated weather forecasts, particularly regarding heavy rainfall. This approach provides more cloud and precipitation information, leading to more accurate forecast performances in terms of heavy rainfall distribution and intensity.