By using the data of synoptic charts and Datong Doppler radar data, two heavy snow processes in Datong during November 9 - 10, 2009 and on March 14, 2010 were analyzed. The results show that surface current, occluded ...By using the data of synoptic charts and Datong Doppler radar data, two heavy snow processes in Datong during November 9 - 10, 2009 and on March 14, 2010 were analyzed. The results show that surface current, occluded fronts, high-altitude and low-altitude jet stream were main reasons for the heavy snow processes. Zero velocity curves were like ＂S＂ at elevations of 0.5, 1.5 and 2.4~, and there were a pair of＂ bull＇s-eye＂ structure, showing that heavy snow would occur. As for the two heavy snow processes, the qualitative judgment results based on the area of posi- tive and negative velocity zones were consistent with the quantitative analysis results based on the average divergence well, so we can use radar images to judge features of velocity fields rapidly in practice.展开更多
Convective wind is one of the common types of severe convective weather.Identification and Forecasting of convective wind are essential.In this paper,five kinds of features are firstly constructed from characteristics...Convective wind is one of the common types of severe convective weather.Identification and Forecasting of convective wind are essential.In this paper,five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data.The features include storm motion,high-value reflectivity,high-value velocity,velocity shear,and velocity texture.A severe convectiye wind(SCW)identification model is then built by applying the above features to the random forest model.With convective wind samples collected over 13 cities of China in June-August 2016,it is found that the probability of detection(POD)of SCW is 78.9%,the false alarm ratio(FAR)is 26.4%,and the critical success index(CSI)is 61.5%.For the convective wind samples that cary typical echo features,the POD,FAR,and CSI range from 89.4%to 99.3%,4.2%to 16.0%,and 76.4%to95.1%,respectively.Meanwhile,the POD and negative-case POD of samples without typical echo features are 66.8%and 85.4%,respectively.The experimental results demonstrate that the SCW identification model can classify nonSCW effectively,and performs better with SCW samples carrying typical echo features than without.展开更多
基金Supported by the Development Project of Air Water Resources For Improvement of Ecological Environment in Datong City([2002]552)
文摘By using the data of synoptic charts and Datong Doppler radar data, two heavy snow processes in Datong during November 9 - 10, 2009 and on March 14, 2010 were analyzed. The results show that surface current, occluded fronts, high-altitude and low-altitude jet stream were main reasons for the heavy snow processes. Zero velocity curves were like ＂S＂ at elevations of 0.5, 1.5 and 2.4~, and there were a pair of＂ bull＇s-eye＂ structure, showing that heavy snow would occur. As for the two heavy snow processes, the qualitative judgment results based on the area of posi- tive and negative velocity zones were consistent with the quantitative analysis results based on the average divergence well, so we can use radar images to judge features of velocity fields rapidly in practice.
基金Supported by the Applied Foundation and Frontier Technology Research Program(Youth Project)of Tianjin,China(16JQNJC07500)。
文摘Convective wind is one of the common types of severe convective weather.Identification and Forecasting of convective wind are essential.In this paper,five kinds of features are firstly constructed from characteristics of typical convective wind-related echo phenomena based on Doppler radar data.The features include storm motion,high-value reflectivity,high-value velocity,velocity shear,and velocity texture.A severe convectiye wind(SCW)identification model is then built by applying the above features to the random forest model.With convective wind samples collected over 13 cities of China in June-August 2016,it is found that the probability of detection(POD)of SCW is 78.9%,the false alarm ratio(FAR)is 26.4%,and the critical success index(CSI)is 61.5%.For the convective wind samples that cary typical echo features,the POD,FAR,and CSI range from 89.4%to 99.3%,4.2%to 16.0%,and 76.4%to95.1%,respectively.Meanwhile,the POD and negative-case POD of samples without typical echo features are 66.8%and 85.4%,respectively.The experimental results demonstrate that the SCW identification model can classify nonSCW effectively,and performs better with SCW samples carrying typical echo features than without.
