ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 45 Issue 8 > 2019,45(8):1037-1051. DOI:10.7519/j.issn.1000-0526.2019.08.001 Prev Next
Discrimination Analysis of Snow and Rain Occurring Under Critical Temperature Conditions in Central and Eastern China
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Based on surface observation and sounding data from 2001 to 2013, the spatial and temporal distribution of snowfall occurring at surface air temperature of 0-2℃ (hereinafter referred to as critical surface temperature condition) in China is analyzed, and comparative analysis of vertical thermal characteristics is made between snow and rain under the above condition, then a decision tree discriminant method is introduced to discriminate snow from rain. The results show that the frequency of snow under the critical surface air temperature conditions is generally higher than that of rain and sleet, and it mainly distributes in the central and eastern regions between southern North China and northern Jiangnan Area, with an average annual number of 7.69-15.38 stations. Combined soundings of snow and rain show maximum temperature difference around 650 hPa, more obvious when T2 m (surface air temperature) is lower. Maximum humidity difference lies around the surface, more obvious when T2 m is higher. A melting layer above the surface exists more frequently in rain than in snow. The melting layer of rain is mainly located in the middle layer, while that of snow is mainly near 1000 hPa. Discriminative accuracies of different decision tree discriminant models constructed from different factors vary differently. T2 m is especially important, and wetbulb temperature can improve the discriminative accuracy in the process of discrimination. The accuracy of the decision tree discriminant model constructed from cloud top temperature, middlelevel melting parameter and lowlevel wetbulb temperature is the highest among all models, with a discriminant accuracy rate of 91.86%.
Keywords:
Project Supported:
Clc Number:
Mobile website
