Impact of Improving Radar Reflectivity Assimilation Schemes in High-Resolution Models and Their Combined Application with Convective Environment Parameters on Severe Convective Weather Forecast

Taking short-duration heavy rainfall and convective wind gusts as examples, the present study examined the characteristics of radar reflectivity and several convective parameters. We analyzed nowcasting techniques by integrating a high-resolution numerical weather prediction model with these convective parameters. Based on the CMA-GD 1-km model and its assimilation system, we conducted repeated tests on radar reflectivity data assimilation and analyzed their impact on nowcasting accuracy. Based on these analyses, we proposed a method to improve model forecasts using the useful indicative information provided by high-frequency radar reflectivity data and convective parameters. The improved method was applied to the CMA-GD 1-km model for nowcasting tests. Evaluations from batch tests and case analysis show that the proposed method significantly reduced the model's false alarm rates and improved its nowcasting performance.