Identification of Stratiform and Convective Cloud Using 3D Radar Reflectivity Data
XIAO Yan-Jiao1,2,3 and LIU Li-Ping11 State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 1000812 Nanjing University of Information Science & Technology,Nanjing 2100443 Institute of Heavy Rain,China Meteorological Administration,Wuhan 430074
An automatic algorithm for the partitioning of radar reflectivity into convective and stratiform rain classifications has been developed and tested using volume scan radar reflectivity data from the Guangzhou and Fuzhou Doppler weather surveillance Radar.Based on the differences of radar reflectivity distribution morphology between convective and stratiform rain,six preparative reflectivity-morphological parameters are presented,which are composite reflectivity and its horizontal gradient,echo top height associated with 35 dBZ reflectivity and its horizontal gradient,vertically integrated liquid water content and its density.To arrive at a set of skillful separation parameters,the probability densities of the six separation attributes from "true" stratiform and convective rain are obtained.Ideally,the statistics should come from a large sample in a site-and seasonality-specific manner,but large sample estimation of parameter statistics is not operationally viable.So the approach taken here is to estimate the parameter statistics from a small but very informative sample.Such a sample should contain,at least,a precipitation event with widespread,well-developed and clearly distinguishable areas of convective and stratiform precipitation.This paper uses the representative squall line on 22 March 2005 and mixed precipitation with bright band enhancement on 23 June 2005 at Guangzhou.Areas of 'true' stratiform and convective precipitation are identified by analyzing images of six preparative parameters and selective vertical cross section of reflectivity in man-machine interaction manner.Results show that the densities of the horizontal gradient of composite reflectivity,the horizontal gradient of echo top height associated with 35 dBZ reflectivity and the density of vertically integrated liquid water content which are identified as the ultimate stratiform and convective cloud separation parameters are more concentrative than those of composite reflectivity,echo top height associated with 35 dBZ reflectivity and vertically integrated liquid water content,and the cross parts exist in the probability density function graphs of stratiform and convective cloud separation parameters.It is not very desirable to select a single set of thresholds for stratiform and convective cloud separation which could not possibly work well consistently and reliably for all sites,all seasons,and under varying conditions of radar calibration accuracy.Therefore,the fuzzy logic method is used for stratiform and convective cloud separation.The membership function of the fuzzy logic method is constructed according to the probability density features of the ultimate stratiform and convective cloud separation parameters,and the asymmetric trapezoidal membership function is chosen as the form of the membership functions.Three cases from Ghuangzhou and Fuzhou Doppler weather surveillance Radar are studied using the fuzzy logic method and the advanced maxima method.Results show that both the methods can separate most stratiform and convective rain.Because of using only the two-dimensional reflectivity morphology feature,the advanced maxima method has two main sources of misclassification: convective classification being assigned to heavy stratiform rain,and stratiform classification being assigned to the periphery of convective cores.By applying information based on the three-dimensional hydrometeor field inferred from radar reflectivity,the fuzzy logic method improves the performance of echo classification by correcting two main error sources of the advanced maxima method.Heavy stratiform rain and the periphery of convective cores are both classified correctly by the fuzzy logic method.
【Fund】： 灾害天气国家重点实验室基金资助2006LASW012;; 国家重点基础研究发展规划项目2004CB418305
【CateGory Index】： P412.25
【CateGory Index】： P412.25