基于TIGGE多模式降水量预报的统计降尺度研究

利用TIGGE资料中欧洲中期天气预报中心、美国国家环境预报中心、英国气象局以及日本气象厅4个中心,1~7 d预报时效的降水量预报资料,以TRMM/3B42RT降水量作为"观测值",对东亚地区降水量进行统计降尺度处理。首先利用逻辑回归方法将天气分为有雨和无雨,再对有雨的情况,利用线性回归方法对插值后的预报结果进行降尺度订正,最后将4个中心的预报值进行消除偏差集合平均,得到多模式集成的降水量预报场。结果表明:逻辑回归能够有效地改善预报中小雨的空报情况,统计降尺度订正后的预报结果比直接插值更加准确,多模式集成的预报效果优于单模式结果,其改进效果随预报时效的延长逐渐减小。

Statistical downscaling research of precipitation forecast based on TIGGE multimodel ensemble

By taking the TRMM/3B42RT rainfall amount as "observed value", the 1-7 day's precipitation forecasting data from the European Centre for Medium-Range Weather Forecasts, the Japan Meteorological Agency, the National Centers for Environmental Prediction and the UK Met Office in the TIGGE datasets were used to statistically downscale the precipitation over East Asia(90-140 °E,15-45 °N). Firstly, the weather phenomenon was divided into rain and no-rain by logistic regression. Then, the linear regression was subsequently used to statistically downscale the interpolated precipitation forecast for rainy weather. Finally, the bias-removed ensemble mean was applied to gain the multimodel ensemble precipitation forecast. Results show that the logistic regression can effectively eliminate the false alarms for light and moderate rain; the forecasts by statistical downscaling are more accurate than that by interpolating. The multimodel ensemble forecast is superior to that by individual model in terms of the root-mean-square errors and the anomaly correlation coefficients of the precipitation forecasts, whose improvement effects decrease as forecast leading time increases.