多模式降水集合预报的统计降尺度研究

Statistical downscaling of the precipitation multimodel ensemble forecast

基于全球交互式大集合(TIGGE)预报资料以及TRMM/3B42RT合成降雨量资料,分别对欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)和英国气象局(UKMO)的集合平均预报及其多模式集成预报进行降尺度试验。结果表明,统计降尺度方法可有效改进各中心模式直接插值预报效果,单中心集合平均预报场降尺度后,预报误差明显减小,与"观测场"的距平相关系数也明显提高;多模式集成的降尺度预报效果明显优于单中心集合平均预报场的降尺度预报效果,试验期间在所选区域内多模式集成的降尺度预报与"实况"最接近,对降水极大值的捕捉能力在绝大多数时间多模式集成的降尺度预报效果最好。

Based on the TIGGE datasets and the TRMM/3B42RT rainfall product during the period from June 1 to August 27, 2007, several statistical downscaling schemes ai＇e applied to the ensemble mean rainfall forecasts made by the European Centre for Medium-Range Weath- er Forecasts （ECMWF）, the Japan Meteorological Agency （JMA）, the National Centers for Environmental Prediction （NCEP） and the UK Met Office （UKMO） and to their muhimodel ensemble products. The results show that the statistical downscaling may improve the forecast effect of each single model. After the downscaling, the root-mean-square errors （RMSE） of the forecasts are significantly reduced, and the Anomaly Correlation Coefficients （ACC） between the forecasts and the TRMM data become larger. The downscaling of the muhimodel ensemble fore- casts is superior to that of the single model in terms of forecast capability of the precipitation maximum.