2016-2020年广东省定量降水预报检验评估

对2016-2020年全球模式ECMWF和区域模式GZ_GRAPES、基于模式的解释应用和广东省气象局发布的定量降水预报(QPF)进行检验和评估.结果表明:ECMWF和GZ_GRAPES模式对一般性降水预报技巧在逐年提升,对大雨或以上的降水预报技巧的提升缓慢.GZ_GRAPES对大雨以上降水的预报技巧和定量降水预报的精...

VERIFICATION AND EVALUATION OF QUANTITATIVE PRECIPITATION FORECAST FOR GUANGDONG PROVINCE DURING 2016-2020

The present study examines and evaluates the 2016-2020 global model ECMWF and regional model GZ_GRAPES, model-based interpretation and application, and quantitative precipitation forecasts (QPF) issued by forecasters from the Guangdong Meteorological Service. The results show that the forecast skill of QPF (< 25mm) by the ECMWF and GZ_GRAPES increases year by year and the skill of QPF (≥25mm) varies from year to year. The GZ_GRAPES forecast skill for precipitation stronger than heavy rain and the fine temporal and spatial distribution of QPF are better than those of the ECMWF, and it is easier for the regional model to forecast small and medium-scale precipitation. Classified rainstorm evaluation shows that the models have the best forecast for typhoon rain, followed by frontal rainstorm and monsoon rain. The models'rainstorm forecast underestimates the rainfall area, and the underestimation is obvious. Forecasters have significantly improved the rainstorm forecast score through empirical corrections. Among them, the amount of correction for monsoon rain is the largest, but there are problems of a large forecast range and high false alarms. The interpretation and application based on the ECMWF ensemble forecast is equivalent to the QPF ability of forecasters. The stronger the precipitation, the more obvious the advantages of model interpretation and application, but there is also serious underestimation or underreporting of the monsoon rain. At present, the precise temporal and spatial distribution of precipitation, monsoon rains and extreme rainstorms still rely mainly on the empirical correction of forecasters. With the improvement of the capabilities of ensemble forecasting models and regional high-resolution models, forecasts will become more objective, which, combined with numerical forecasting techniques, can help improve QPF.