Many weather radar networks in the world have now provided polarimetric radar data (PRD) that have the potential to improve our understanding of cloud and precipitation microphysics, and numerical weather prediction (NWP). To realize this potential, an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables. For this purpose, a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain, snow, hail, and graupel. The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution. The calculated polarimetric variables are then fitted to simple functions of water content and volume-weighted mean diameter of the hydrometeor particle size distribution. The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting (WRF) model to have simulated PRD, which are compared with existing operators and real observations to show their validity and applicability. The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations, making it efficient in PRD simulation and assimilation usage.

现在，世界上许多天气雷达网络都提供了极化雷达数据（PRD），它们有可能增进我们对云和降水微物理学以及数值天气预报（NWP）的理解。为了实现这一潜力，需要一套准确有效的极化观测算子，以使用NWP模型模拟和吸收PRD，以准确分析模型状态变量。为此，开发了一组参数化的观测算子，以根据NWP模型预测的水凝物混合比和雨，雪，冰雹和graupel的数量浓度来模拟和吸收极化雷达数据。极化雷达变量的计算基于波散射的T矩阵计算以及由粒度分布加权的散射积分。然后将计算出的极化变量拟合为水含量和水凝颗粒粒径分布的体积加权平均直径的简单函数。将参数化的PRD运算符应用于由天气研究和预报（WRF）模型预测的理想情况和实际案例，以模拟PRD，并将其与现有的运算符和实际观测值进行比较，以显示其有效性和适用性。新的PRD运算符使用不到旧运算符的计算时间的1％来完成相同的模拟，从而使其在PRD模拟和同化使用中非常有效。将参数化的PRD运算符应用于由天气研究和预报（WRF）模型预测的理想情况和实际案例，以模拟PRD，并将其与现有的运算符和实际观测值进行比较，以显示其有效性和适用性。新的PRD运算符使用不到旧运算符的计算时间的1％来完成相同的模拟，从而使其在PRD模拟和同化使用中非常有效。将参数化的PRD运算符应用于由天气研究和预报（WRF）模型预测的理想情况和实际案例，以模拟PRD，并将其与现有的运算符和实际观测值进行比较，以显示其有效性和适用性。新的PRD运算符使用不到旧运算符的计算时间的1％来完成相同的模拟，从而使其在PRD模拟和同化使用中非常有效。