Advanced Search
Article Contents

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


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.
  • [1] Marcus JOHNSON, Youngsun JUNG, Daniel DAWSON, Timothy SUPINIE, Ming XUE, Jongsook PARK, Yong-Hee LEE, 2018: Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 771-784.  doi: 10.1007/s00376-017-7177-0
    [2] Jiang HUANGFU, Zhiqun HU, Jiafeng ZHENG, Lirong WANG, Yongjie ZHU, 2024: Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3039-0
    [3] Hepeng ZHENG, Yun ZHANG, Lifeng ZHANG, Hengchi LEI, Zuhang WU, 2021: Precipitation Microphysical Processes in the Inner Rainband of Tropical Cyclone Kajiki (2019) over the South China Sea Revealed by Polarimetric Radar, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 65-80.  doi: 10.1007/s00376-020-0179-3
    [4] Hao HUANG, Kun ZHAO, Johnny C. L. CHAN, Dongming HU, 2023: Microphysical Characteristics of Extreme-Rainfall Convection over the Pearl River Delta Region, South China from Polarimetric Radar Data during the Pre-summer Rainy Season, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 874-886.  doi: 10.1007/s00376-022-1319-8
    [5] Shuai ZHANG, Jinzhong MIN, Chian ZHANG, Xingyou HUANG, Jun LIU, Kaihua WEI, 2021: Hybrid Method to Identify Second-trip Echoes Using Phase Modulation and Polarimetric Technology, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 480-492.  doi: 10.1007/s00376-020-0223-3
    [6] HU Zhiqun, and LIU Liping, 2014: Applications of Wavelet Analysis in Differential Propagation Phase Shift Data De-noising, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 825-835.  doi: 10.1007/s00376-013-3095-y
    [7] FU Weiwei, ZHOU Guangqing, WANG Huijun, 2004: Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 181-192.  doi: 10.1007/BF02915704
    [8] Chong WU, Liping LIU, Ming WEI, Baozhu XI, Minghui YU, 2018: Statistics-based Optimization of the Polarimetric Radar Hydrometeor Classification Algorithm and Its Application for a Squall Line in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 296-316.  doi: 10.1007/s00376-017-6241-0
    [9] Guifu ZHANG, Jidong GAO, Muyun DU, 2021: Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 737-754.  doi: 10.1007/s00376-021-0289-6
    [10] ZHONG Lingzhi, LIU Liping, FENG Sheng, GE Runsheng, ZHANG Zhe, 2011: A 35-GHz Polarimetric Doppler Radar and Its Application for Observing Clouds Associated with Typhoon Nuri, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 945-956.  doi: 10.1007/s00376-010-0073-5
    [11] HU Zhiqun, LIU Liping, WANG Lirong, 2012: A Quality Assurance Procedure and Evaluation of Rainfall Estimates for C-Band Polarimetric Radar, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 144-156.  doi: 10.1007/s00376-011-0172-y
    [12] Haochen LI, Chen YU, Jiangjiang XIA, Yingchun WANG, Jiang ZHU, Pingwen ZHANG, 2019: A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1156-1170.  doi: 10.1007/s00376-019-9023-z
    [13] Chaoqun MA, Tijian WANG, Zengliang ZANG, Zhijin LI, 2018: Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 813-825.  doi: 10.1007/s00376-017-7179-y
    [14] ZHAO Kun, LIU Guoqing, GE Wenzhong, DANG Renqing, Takao TAKEDA, 2003: Retrieval of Single-Doppler Radar Wind Field by Nonlinear Approximation, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 195-204.  doi: 10.1007/s00376-003-0004-9
    [15] Susannah M. BURROWS, Aritra DASGUPTA, Sarah REEHL, Lisa BRAMER, Po-Lun MA, Philip J. RASCH, Yun QIAN, 2018: Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1101-1113.  doi: 10.1007/s00376-018-7300-x
    [16] LIU Hongya, XUE Jishan, GU Jianfeng, XU Haiming, 2012: Radar Data Assimilation of the GRAPES Model and Experimental Results in a Typhoon Case, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 344-358.  doi: 10.1007/s00376-011-1063-y
    [17] Wei Ming, Dang Renqing, Ge Wenzhong, Takao Takeda, 1998: Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 553-568.  doi: 10.1007/s00376-998-0032-6
    [18] LIANG Qiaoqian, FENG Yerong, DENG Wenjian, HU Sheng, HUANG Yanyan, ZENG Qin, CHEN Zitong, 2010: A Composite Approach of Radar Echo Extrapolation Based on TREC Vectors in Combination with Model-Predicted Winds, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1119-1130.  doi: 10.1007/s00376-009-9093-4
    [19] Kong Fanyou, Mao jietai, 1994: A Model Study of Three Dimensional Wind Field Analysis from Dual-Doppler Radar Data, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 162-174.  doi: 10.1007/BF02666543
    [20] Bo LIU, Juan HUO, Daren LYU, Xin WANG, 2021: Assessment of FY-4A and Himawari-8 Cloud Top Height Retrieval through Comparison with Ground-Based Millimeter Radar at Sites in Tibet and Beijing, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1334-1350.  doi: 10.1007/s00376-021-0337-2

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 September 2005
Manuscript revised: 10 September 2005
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

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

  • 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;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,The Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, USA

Abstract: 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.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return