基于CFS模式的中国站点夏季降水统计降尺度预测

本研究针对中国夏季站点降水,研制建立了基于Climate Forecast System(CFS)实时预测数值产品及观测资料的统计降尺度预测系统。此预测系统选取了CFS模式中当年夏季500 hPa高度场和观测资料中前一年秋、冬季海表面温度场作为预测因子,两因子的关键区分别为泛东亚地区和热带太平洋地区。统计降尺度模型对1982~2011年中国夏季降水的回报效果较CFS模式原始结果显著提高,空间距平相关系数由0.03提高到0.31,时间相关系数在中国大部分地区显著提高,最大可达0.6。均方根误差较CFS模式原始结果明显降低,同时,此降尺度模型较好的回报出2011年汛期降水的距平百分率的空间分布型。

A Statistical Downscaling Model for Summer Rainfall over China Stations Based on the Climate Forecast System

A statistical downscaling system for forecasting summer precipitation at stations in China has been established in this study on the basis of real-time prediction of numerical products from the Climate Forecast System (CFS) and observational data. The summer 500-hPa geopotential height in the current year from CFS and the previous autumn-winter sea surface temperature from observations were selected as the two predictors, with corresponding key regions of Pan-East-Asia and the tropical Pacific, respectively. The statistical downscaling hindcast on the 1982-2001 summer precipitation over China improved the performance of the prediction compared with that of the original CFS. The spatial anomaly correlation coefficients increased from 0.03 to 0.31, and the temporal correlation coefficients over most parts of China also increased significantly by the downscaling scheme with a maximum of 0.6. The root mean square error decreased in comparison with the output of the original CFS. Furthermore, we successfully created a hindcast on the 2011 summer precipitation anomaly pattern in China by using this statistical downscaling scheme.