Improved Model Prediction with Dual-polarimetric Radar Operator in Ensemble Data Assimilation

 Dual-polarimetric (dual-pol) radar variables, such as differential reflectivity (Z_DR) and specific differential phase (K_DP), provide valuable information about hydrometeor types, sizes, and water content. A dual-pol radar operator that applies scattering calculations using the T-matrix method for rain and the Rayleigh scattering approximation for snow and graupel can more accurately translate model variables into observed variables. Assimilating dual-pol radar variables in numerical weather prediction models enhances the forecast accuracy for evolving mesoscale precipitation events. Therefore, developing advanced radar observation operators capable of calculating dual-pol radar variables using microphysical variables is crucial.

In this study, an improved observation operator (K-DROP; KNU dual-pol radar observation operator) is developed. The K-DROP restricts the distribution of mixed-phase hydrometeors in regions with strong vertical motions, thereby reducing overestimation of radar variables near the melting layer. Additionally, by incorporating the observed snow axis ratios for cold rain process, the calculation of  as a constant value in subfreezing regions is corrected. Observed maximum hydrometeor radius data are also applied, reducing overestimations of  and in warm regions. Experiments using LETKF are conducted for both convective and stratiform precipitation cases and compared with the previous observation operator without modifications. While the previous operator improved forecast accuracy compared to control experiments without DA, it showed limited improvements near the melting layer due to reduced hydrometeor mixing ratios and increased downdrafts. In contrast, K-DROP produced more realistic radar variables, stronger updrafts, and higher correlations with observations. These improvements are particularly effective for convective precipitation with localized heavy rainfall, demonstrating the importance of assimilating dual-pol radar variables containing water content information.

Key words: Dual-polarization radar operator, Radar data assimilation, Observation operator, Precipitation forecasting.

Acknowledgments: This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1012361), the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740 and the Brain Korea 21 program.