集合方法在月动力预报信息提取中的应用

本工作将集合方法应用于提取月动力预报有用信息。利用中国气象局国家气候中心Ｔ６３Ｌ１６全球谱模式的５００百帕高度场月集合预报产品（集合成员数为８个，初始场的选取采用滞后方法（ＬＡＦ），即相邻两天的００００，０６００，１２００和１８００ＧＭＴ的初始化资料），就１９９７年１月至５月共１５次预报，分析了集合预报成员间的离散度与预报评分（距平相关系数和均方根误差）的关系，研究了用集合各成员预报离散度作为各个成员逐日预报的权重对月预报效果的影响。结果表明集合预报成员的离散度与预报评分有显著的相关，是有效预报长度Ｎ的一个很好估计；用离散度作为权重平均的月预报高度距平相关系数明显高于算术平均和线性权重，此外个例分析表明月平均环流及其异常的预报得到明显的提高。

An Approach to Extract Effective Information of Monthly Dynamical Prediction-The Use of Ensemble Method

The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800 GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997 are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root-mean-square deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root-mean-square distance between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained from the ensemble spread weighted mean.