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To enhance the accuracy of heavy rainfall prediction, the assimilation of radar data (DA) is crucial. Single-polarized radar variables, such as reflectivity and Doppler velocity, offer insights into raindrop quantity and speed. Dual-polarization (dual-pol) radar variables, including differential reflectivity (ZDR), specific differential phase (KDP), and co-polar correlation coefficient (hv), provide additional details about hydrometeor phase, size, and liquid water content. Assimilating dual-pol radar variables into a Numerical Weather Prediction (NWP) model can enhance the accuracy of predicting both large-scale and rapidly developing mesoscale precipitation events. Therefore, the development and application of an accurate radar observation operator for DA, considering the microphysical information of an NWP model with dual-pol radar data, is necessary. In this study, we developed a dual-pol radar operator based on microphysical variables such as the mixing ratio and total number concentration of hydrometeors. The enhanced method can accurately replicate the characteristics of dual-pol radar variables in the melting layer, improve the underestimation of hydrometeors mixing ratio for liquid and ice particles. Enhancing the estimation of hydrometeor increments further refines the prediction of mesoscale precipitation effects. This study aims to demonstrate improvements in microphysical processes and enhanced accuracy in rainfall predictions through dual-pol radar DA. This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT)(No. 2021R1A4A1032646, 2022R1A6A3A13073165) and the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740.
Lee et al. (Mon,) studied this question.