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

• Published in: Advances in atmospheric sciences Vol. 24; no. 1; pp. 101 - 110
• Main Author: 冯业荣 汪瑛 彭涛涌 闫敬华
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01-01-2007; Department of Atmospheric Sciences, Zhongshan University, Guangzhou 510270; Guangdong Meteorological Observatory, Guangzhou 510080%Department of Atmospheric Sciences, Zhongshan University, Guangzhou 510270%Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510080
• Subjects: Algorithms; Radar; Rain; Rainy season; Rivers; Severe weather; Studies; Weather forecasting; 中尺度数值模型; 对流天气; 数值天气预报; 珠江三角洲; 雨季早期; China, People's Rep., Guangdong Prov., Guangzhou; China, People's Rep., Zhu R; China, People's Rep; mesoscale numerical model; Doppler radar; nowcasting; convective weather potential
• ISSN: 0256-1530; 1861-9533
• 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° 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.