多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用

利用动力季节模式输出的匹配域投影技术和多模式集合预报技术对多个国家和城市的站点月平均降水进行预报。预报变量是北京1个站、韩国60个站和曼谷地区8个站点的月平均降水,预报因子是从多个业务动力季节预报模式输出的多个大尺度变量。模式回报数据和站点观测降水数据时段是1983—2003年。降尺度预报降水的技巧是在交叉验证的框架下进行的。匹配域投影方法是设定一个可以活动的窗口在全球范围内大尺度场上进行扫描,寻求与目标站点降水最优化的因子和最相关的区域,目标站点的降水变率就是由该匹配域上大尺度环流场信息决定的。最终预报是用多个降尺度模式预报结果的集合预报(DMME)。多个降尺度模式预报结果的集合预报能显著地提高站点降水的预报技巧。北京站,多个降尺度模式预报结果的集合预报的预报和观测降水的相关系数可以提高到0.71;韩国地区,多个降尺度模式预报结果的集合预报平均技巧提高到0.75;泰国,多个降尺度模式预报结果的集合预报技巧是0.61。

Statistical downscaling of pattern projection using multi-model output variables as predictors

A pattern projection downscaling method is employed to predict monthly station precipitation.The predictand is the monthly precipitation at 1 station in China,60 stations in Korea,and 8 stations in Thailand.The predictors are multiple variables from the output of operational dynamical models.The hindcast datasets span a period of 21 year from 1983 to 2003.A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework.The pattern projection method uses a moving window,which scans globally,in order to seek the most optimal predictor for each station.The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME.It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models.The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71;the skill is improved to 0.75 for Korea and 0.61 for Thailand.