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An Algorithm on Convective Weather Potential in the Early Rainy Season over the Pearl River Delta in China


doi: 10.1007/s00376-007-0101-2

  • This paper describes the procedure and methodology to formulate the convective weather potential (CWP) algorithm. The data used in the development of the algorithm are the radar echoes at 0.5$^\circ$ elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations (AWS) and outputs of numeric weather prediction (NWP) models. The procedure to develop the CWP algorithm consists of two steps: (1) identification of thunderstorm cells in accordance with specified statistical criteria; and (2) development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50~dB($Z$) and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta (PRD) region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.
  • [1] LI Gang, HE Guangxin, Xiaolei ZOU*, and Peter Sawin RAY, 2014: A Velocity Dealiasing Scheme for C-band Weather Radar Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 17-26.  doi: 10.1007/s00376-013-2251-8
    [2] WANG Gaili, WONG Waikin, LIU Liping, WANG Hongyan, 2013: Application of Multi-Scale Tracking Radar Echoes Scheme in Quantitative Precipitation Nowcasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 448-460.  doi: 10.1007/s00376-012-2026-7
    [3] Jing YANG, Gaopeng LU, Ningyu LIU, Haihua CUI, Yu WANG, Morris COHEN, 2017: Analysis of a Mesoscale Convective System that Produced a Single Sprite, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 258-271.  doi: 10.1007/s00376-016-6092-0
    [4] SUN Jianhua, ZHAO Sixiong, XU Guangkuo, MENG Qingtao, 2010: Study on a Mesoscale Convective Vortex Causing Heavy Rainfall during the Mei-yu Season in 2003, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1193-1209.  doi: 10.1007/s00376-009-9156-6
    [5] Min CHEN, Benedikt BICA, Lukas TÜCHLER, Alexander KANN, Yong WANG, 2017: Statistically Extrapolated Nowcasting of Summertime Precipitation over the Eastern Alps, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 925-938.  doi: 10.1007/s00376-017-6185-4
    [6] PENG Xindong, ZHANG Renhe, WANG Hongyan, 2013: Kinematic Features of a Bow Echo in Southern China Observed with Doppler Radar, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1535-1548.  doi: 10.1007/s00376-012-2108-6
    [7] Jidong GAO, Keith BREWSTER, Ming XUE, 2006: A Comparison of the Radar Ray Path Equations and Approximations for Use in Radar Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 190-198.  doi: 10.1007/s00376-006-0190-3
    [8] YANG Jing, YANG Meirong, LIU Chao, FENG Guili, 2013: Case Studies of Sprite-producing and Non-sprite-producing Summer Thunderstorms, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1786-1808.  doi: 10.1007/s00376-013-2120-5
    [9] SUN Jianhua, ZHANG Xiaoling, QI Linlin, ZHAO Sixiong, 2005: An Analysis of a Meso-β System in a Mei-yu Front Using the Intensive Observation Data During CHeRES 2002, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 278-289.  doi: 10.1007/BF02918517
    [10] Yang LI, Yubao LIU, Rongfu SUN, Fengxia GUO, Xiaofeng XU, Haixiang XU, 2023: Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 887-899.  doi: 10.1007/s00376-022-2082-6
    [11] Shibo GAO, Haiqiu YU, Chuanyou REN, Limin LIU, Jinzhong MIN, 2021: Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 132-146.  doi: 10.1007/s00376-020-0081-z
    [12] 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
    [13] LIU Liping, ZHUANG Wei, ZHANG Pengfei, MU Rong, 2010: Convective Scale Structure and Evolution of a Squall Line Observed by C-Band Dual Doppler Radar in an Arid Region of Northwestern China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1099-1109.  doi: 10.1007/s00376-009-8217-1
    [14] Jo-Han LEE, Dong-Kyou LEE, Hyun-Ha LEE, Yonghan CHOI, Hyung-Woo KIM, 2010: Radar Data Assimilation for the Simulation of Mesoscale Convective Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1025-1042.  doi: 10.1007/s00376-010-9162-8
    [15] Lu ZHANG, Xiangjun TIAN, Hongqin ZHANG, Feng CHEN, 2020: Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012), ADVANCES IN ATMOSPHERIC SCIENCES, 37, 873-892.  doi: 10.1007/s00376-020-9274-8
    [16] Wenbo Xue, Hui Yu, Shengming TANG, Wei Huang, 2024: Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3087-5
    [17] Na LI, Lingkun RAN, Linna ZHANG, Shouting GAO, 2017: Potential Deformation and Its Application to the Diagnosis of Heavy Precipitation in Mesoscale Convective Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 894-908.  doi: 10.1007/s00376-017-6282-4
    [18] Dang Renqing, Tang Xinzhang, Zhang Jiacheng, 1992: Experiments in Forecasting Mesoscale Convective Weather over Changjiang Delta, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 223-230.  doi: 10.1007/BF02657512
    [19] Guifu ZHANG, Vivek N. MAHALE, Bryan J. PUTNAM, Youcun QI, Qing CAO, Andrew D. BYRD, Petar BUKOVCIC, Dusan S. ZRNIC, Jidong GAO, Ming XUE, Youngsun JUNG, Heather D. REEVES, Pamela L. HEINSELMAN, Alexander RYZHKOV, Robert D. PALMER, Pengfei ZHANG, Mark WEBER, Greg M. MCFARQUHAR, Berrien MOORE III, Yan ZHANG, Jian ZHANG, J. VIVEKANANDAN, Yasser AL-RASHID, Richard L. ICE, Daniel S. BERKOWITZ, Chong-chi TONG, Caleb FULTON, Richard J. DOVIAK, 2019: Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 571-588.  doi: 10.1007/s00376-019-8172-4
    [20] Zhao Qiang, Liu Shikuo, 1999: Simplification of Potential Vorcticity and Mesoscale Quasi-balanced Dynamics Model, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 304-313.  doi: 10.1007/BF02973090

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Manuscript History

Manuscript received: 10 January 2007
Manuscript revised: 10 January 2007
通讯作者: 陈斌, bchen63@163.com
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An Algorithm on Convective Weather Potential in the Early Rainy Season over the Pearl River Delta in China

  • 1. Department of Atmospheric Sciences, Zhongshan University, Guangzhou 510270; Guangdong Meteorological Observatory, Guangzhou 510080,Department of Atmospheric Sciences, Zhongshan University, Guangzhou 510270,Department of Atmospheric Sciences, Zhongshan University, Guangzhou 510270,Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510080

Abstract: This paper describes the procedure and methodology to formulate the convective weather potential (CWP) algorithm. The data used in the development of the algorithm are the radar echoes at 0.5$^\circ$ elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations (AWS) and outputs of numeric weather prediction (NWP) models. The procedure to develop the CWP algorithm consists of two steps: (1) identification of thunderstorm cells in accordance with specified statistical criteria; and (2) development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50~dB($Z$) and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta (PRD) region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.

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