新型局地增长模培育法对两次飑线个例的对流尺度集合预报试验

Convection-allowing ensemble forecasts of two squall line cases using a novel local breeding growth mode method

摘要:

利用局地增长模培育法对两次典型飑线过程进行了对流尺度集合预报试验，通过与传统增长模培育法对比，检验了局地增长模培育法的实际预报效果。通过概率匹配平均处理后，将降水预报结果与实况资料进行对比分析，并用分数技巧评分来代替传统公平技巧评分实现对降水结果的合理检验，得出结论：1）在飑线降水预报上，局地增长模培育法优于增长模培育法。2）分数技巧评分比公平技巧评分更好地反映对流尺度集合预报能力，特别是在大暴雨量级降水评估上。3）降水评分结果显示，集合平均对于小雨、中雨和大雨级别降水的预报技巧高于概率匹配平均，概率匹配平均对于暴雨和大暴雨级别降水更有优势。

Abstract:

In the present study, a new local breeding growth mode (LBGM) method is used to conduct convection-allowing ensemble prediction experiments regarding two typical squall line cases.Next, by comparing the results with the traditional breeding growth mode (BGM) method, the actual forecast effect of the LBGM is tested.The simulations of precipitation are performed using the probability-matched mean (PMM) method, then compared with the observational data.Finally, the fraction skill score (FSS) is used to replace the traditional equitable threat score (ETS), so as to reasonably evaluate the precipitation prediction.We obtained the following results:(1) The LBGM is observed to be superior to the BGM in the forecast of the precipitation in the squall line.(2) The FSS better represents the convection-allowing ensemble prediction capability better than ETS, particularly for extremely severe rain assessments.(3) The precipitation scoring results show that the EM is superior to the PMM method for light, moderate and heavy rainfall, whereas the PMM method is better for severe and extremely severe rainfall.