基于雨量分级回归分析的站点日降水量预报订正

利用ECMWF 24h累计降水量预报资料,以全国范围内2403个国家地面气象观测站24h累计降水量作为观测资料,对站点预报结果进行雨量分级回归订正,并与直接双线性插值的预报结果进行对比,利用频率匹配法进一步对不同量级的降水预报结果进行二次订正。结果表明,雨量分级回归相比双线性插值,可以减小预报误差,提高预报结果与观测值之间的相关系数以及降水预报的ETS评分,使站点预报值更接近实况。频率匹配法能改善各降水量级的预报效果,降水面积偏差减小,小雨空报率和大雨漏报率减小。对于不同起报时间、不同降水量级和不同预报时效,雨量分级回归和频率匹配法的改进程度各不相同。雨量分级回归对于20:00起报的降水预报改进幅度大于08:00,对0.1mm和50mm量级的降水预报改进较为有限,对5~15mm量级的降水预报改进明显,且随预报时效的延长,对降水预报的改进幅度呈增大趋势。此外,频率匹配法对于起报时次效果较差的降水预报改进幅度较大。

Station Forecast Calibration of Daily Precipitation Using Categorized Rainfall Regression

Based on the precipitation forecast dataset from the European Centre for Medium-Range Weather Forecasts(ECMWF)and the daily accumulated precipitation of 2403 national surface meteorological observation stations across China,the calibration on daily precipitation by means of the categorized rainfall regression and the further calibration by means of the frequency-matching method are conducted.The results show that compared with the bilinear method,the categorized rainfall regression is more effective in decreasing the forecast biases,and improves the correlation coefficient with the observed data and the equitable threat score.The forecasts of different thresholds become more accurate after applying the frequency-matching method,with the smaller area deviation.The false alarm rate of light rain and the missing rate of heavy rain are also both reduced.Improvements of the forecasts by the categorized rainfall regression and the frequency-matching method differ in initialized times,rainfall thresholds and lead times.After the calibration of the categorized rainfall regression,the forecast initialized at 20:00 is improved with a larger magnitude than that at 08:00.The improvement of the forecast is relatively limited for rainfall thresholds of 0.1 mm and 50 mm,but significant for thresholds of 5 mm,10 mm and 15 mm.Additionally,the amplitude of the improvement increases slightly over the lead time.The improvement induced by the frequency-matching method is greater for precipitation forecasts initialized at specific times that show worse performances.