提出了一种新的考虑权重的集合预报成员平均方法。使用气候等概率区间来对集合成员进行分组, 并根据气候等概率区间的大小及其中的成员数, 对集合成员的权重进行调整, 得到了一种改进的集合平均预报结果。检验表明, 它可以进一步提高集合平均预报的效果。相对于提高模式分辨率或发展庞大的集合预报系统, 这种方法的效果是显著的。
The ensemble mean forecasting is one of the major forecasting products in an ensemble prediction system. Compared with the results from a single deterministic forecast, the ensemble average forecast has higher forecasting skills. For the forecasts starting at the same initial time, the ordinary ensemble mean forecasting is the arithmetic mean of all ensemble members. In the ensemble, each of ensemble members has the same weight. A new ensemble average method considering the different weight of ensemble members (known as "the weighted ensemble average method") is presented. In this method, the ensemble average is calculated from all the ensemble members, which has the same initial time and lead time, with the different weight. Through this method, it is expected to get the better forecasting performance than the ordinary ensemble mean forecast (known as "the common ensemble average method").Some research conclusions indicate that the results of the ensemble forecasting are more inclined to the value of major ensemble members; at the same lead time, the higher success rate of ensemble forecasting always correspond to the more consistent members of the forecasting results (the prerequisite of this result is that the ensemble prediction system has better reliability and the initial ensemble perturbations have enough spread so that the forecasting results can more comprehensively cover the possibilities of forecasting errors).The starting point of the weighted ensemble average method is to adjust the weight of ensemble members in order that the members with consistent forecast results have larger weights. By this process, the consistence among the ensemble members is emphasized. Here, the weighted ensemble average method uses the climatologically likely intervals to group the ensemble members according to the proximity extent each other; then according to the size of the climatologically likely interval and the ensemble members in it, the corresponding weight is adjusted; eventually, based on the adjusted weight, the calibrated ensemble mean forecasts are gotten.Based on the NCEP EPS forecasting dataset, the verification methods of ACC and RMSE are used to prove the performance of the weighted ensemble average method. The test results at the various regions show that the method can upgrade the ensemble mean performance in a way. Even in the worst situation, the effects of the weighted ensemble average method do not appear below the ordinary ensemble average method. Compared to the resolution improvement of the numerical weather prediction or development of enormous ensemble prediction system further, the enhancement of the weighted ensemble average method is significant.In addition to the medium-range ensemble average forecasting, this method can also be used in the ensemble mean of the continuous variables during the other types of forecasts, predictions to improve the forecast performance.