STUDY OF THE OBJECTIVE PROBABILITY FORECAST METHOD FOR SHORT-TERM HEAVY RAIN BASED ON ECMWF FINE-MESH MODEL
LI Ming
Abstract:Using percentile method,the standards of the short-term heavy rain are determined in the south part of Shaanxi province on the base of precipitation data of 643 automatic meteorological station after data quality control from 2010 to 2014.Based on 11 824 cases of short-term heavy rain and 0.25 °× 0.25 ° reanalysis data at 6 h intervals of the European Centre for Medium-Range Weather Forecasts (ECMWF)during 2010 to 2014,according to the rule of being within nearby space and near recent time:the characteristic values of 36 kinds of convective parameters probability distribution are obtained in the south part of Shaanxi province from May to Sept during flood season.The evaluate scheme is formulated with the significance and appropriation indexes of 36 kinds of convective parameters,by the methods of the relative deviation fuzzy matrix and standard deviation coefficient,the 15 kinds of convective parameters from May to Sept are selected out,which can well indicate the environmental field for short-term heavy rain,and their weights are given.Based on high-resolution basic forecast products of the ECMWF fine-mesh model,the objective probability forecasting method for short-term heavy rain is established through comprehensive consideration of probability distribution and weights of the selected convective parameters.The value of more than 0.2 corresponding to 80% of the ascending order of probability forecast is taken as critical probability,the TS is 0.59,the rate missing prediction 0.18,and the rate of false predication 0.31 during 2015 flood season.
Keywords:short-term heavy rainrelative deviation fuzzy matrixobjective probability forecastECMWF fine-mesh model
Publication Date:2017-01-01
Online Publishing Date:2025-08-15(First online date of this platform, not the publication date of the document)
Pages:10( 812-821 )
Journal of Tropical Meteorology

Journal of Tropical Meteorology

PKUISTIC
ISSN:1004-4965
Year, Vol.(Issue):2017,33(6)