A Model for Retrieval of Dual Linear Polarization Radar Fields from Model Simulation Outputs

• Published in: Advances in atmospheric sciences Vol. 22; no. 5; pp. 711 - 719
• Main Author: 刘黎平 张鹏飞 Qin XU 孔凡铀 刘舜
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.09.2005; The Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, USA%The Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, USA%National Severe Storm Laboratory, National Oceanic and Atmospheric Administration, Norman, Oklahoma, USA%The Center for Analysis Prediction of Storms, University of Oklahoma, Norman, USA; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
• Subjects: Algorithms; Coefficients; Correlation coefficient; Correlation coefficients; Environmental effects; Fields; Hail; Hailstorms; Horizontal polarization; Linear polarization; Mathematical models; Modelling; Numerical models; Polarimetry; Polarization; Radar; Radar data; Radar reflectivity; Reflectance; Simulation; Spatial distribution; Storms; Vertical polarization; 偏振现象; 大气科学; 数值模型; retrieval of polarimetric variables; model output; polarimetric radar
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/bf02918714

An algorithm for retrieving polarimetric variables from numerical model fields is developed. By using this technique, radar reflectivity at horizontal polarization~ differential reflectivity, specific differential phase shift and correlation coefficients between the horizontal and vertical polarization signals at zero lag can be derived from rain, snow and hail contents of numerical model outputs. Effects of environmental temperature and the melting process on polarimetric variables are considered in the algorithm. The algorithm is applied to the Advanced Regional Prediction System （ARPS） model simulation results for a hail storm. The spatial distributions of the derived parameters are reasonable when compared with observational knowledge. This work provides a forward model for assimilation of dual linear polarization radar data into a mesoscale model.