Application of Dual-Polarization Radar Data Assimilation Via a Deep UNet Network Model

The assimilation of dual-polarization (dual-pol) radar data plays a crucial role in enhancing the simulation of hydrometeors and improving the short-term precipitation forecasts of numerical weather prediction (NWP) models. However, existing dual-pol radar data assimilation (DA) methods exhibit limitations in terms of computational efficiency and data utilization. In this study, a new dual-pol radar DA approach is developed that utilizes a UNet-based model to retrieve mixing ratio information for four hydrometeor species from dual-pol radar data. The validation results for the UNet-based model indicate that the distributions of the retrieved hydrometeor mixing ratios provided by the model align well with the labeled data, yielding a reasonable range of root mean square errors (RMSEs). On this basis, the hydrometeor analysis increments retrieved by the UNet-based model are incorporated into the model integration process through the incremental analysis update (IAU) scheme, establishing a complete dual-pol radar DA framework for the CMA-MESO model. To evaluate the efficacy of this DA scheme, comparative simulation experiments were conducted for Typhoon Lekima (2019). Verification results indicate that using the hydrometeor DA scheme generally improves the threat scores (TSs) for 3-hour accumulated precipitation during medium- and heavy-rainfall events. Additionally, the 24-hour accumulated rainfall TSs for the medium-, heavy-, and extreme-precipitation categories in the DA experiment are all superior to those in the control experiment. The DA method also yields superior predictions of the spatial distribution of extremerainfall events. These results demonstrate that the proposed dual-pol radar DA approach effectively enhances the precipitation forecasting capabilities of numerical weather models.