Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for severe short-term weather forecasts

Abstract. Different hydrometeor retrieval schemes are explored based on the Weather Research and Forecasting (WRF) model in the indirect assimilation of radar reflectivity for two real cases occurred during June 2020 and August 2018. When retrieving hydrometeors from radar reflectivity, there are two commonly used hydrometeor classification methods: “temperature-based” and “background hydrometer-dependent” schemes. The hydrometeor proportions are usually empirically assigned in the “temperature-based” method within different background temperature intervals, whereas in the “background hydrometer-dependent” scheme, each type of hydrometeor is derived based on the portions estimated from the background field for different radar reflectivity ranges. In this study, a blending scheme is designed to combine “temperature-based” and “background hydrometer-dependent” methods adaptively to avoid errors caused by fixed relationships and reduce uncertainties introduced by the background field itself. Three experiments, EXP_temp, EXP_bg, and EXP_temp-bg are conducted using the “temperature-based” method, “background hydrometer-dependent” scheme, and blending scheme, respectively. It is found that adding the “background hydrometer-dependent” scheme facilitates the generation of accurate hydrometeor species which will enhance the effectiveness of radar data assimilation. In addition, due to the adaptive combination of “temperature-based” and “background hydrometer-dependent” schemes, the EXP_temp-bg experiment yields improved thermodynamic and dynamic structures, which contributes to predicting radar reflectivity and precipitation intensity more accurately.