A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment

Radar reflectivity contains information about hydrometeors and plays an important role in the initialization of convective-scale numerical weather prediction (NWP). In this study, a new background-dependent hydrometeor retrieval method is proposed and retrieved hydrometeors are assimilated into the Weather Research and Forecasting model (WRF), with the aim of improving short-term severe weather forecasts. Compared to traditional approaches that are mostly empirical and static, the retrieval parameters for hydrometeor identification and reflectivity partitioning in the new scheme are extracted in real-time based on the background hydrometeor fields and observed radar reflectivity. It was found that the contributions of hydrometeors to reflectivity change a lot in different reflectivity ranges and heights, indicating that adaptive parameters are necessary for reflectivity partitioning and hydrometeor retrieval. The accuracy of the background-dependent hydrometeor retrieval method and its impact on the subsequent assimilation and forecast were examined through observing system simulation experiments (OSSEs). Results show that by incorporating the background information, the retrieval accuracy was greatly improved, especially in mixed-hydrometeor regions. The assimilation of retrieved hydrometeors helped improve both the hydrometeor analyses and forecasts. With an hourly update cycling configuration, more accurate hydrometeor information was properly transferred to other model variables, such as temperature and humidity fields through the model integration, leading to an improvement of the short-term (0−3 h) precipitation forecasts.