Development of a method with high-reliability to forecast the occurrence of location and time of local heavy rainfall within 24 hours.

利用PSU/NCAR MM5中尺度气象模式中多个参数化获得的32个单元的模式集合方法，建立了局地强降水预报方法。将多参数化集合预报与利用JMA GSM-GPV和雷达数据同化系统初始化的20km分辨率全球分析模型相结合。32个成员的平均预报在指示强烈的本地风暴活动区域方面非常熟练，但每个成员与整体平均值相差很大。通过对日本气象厅（Japan Meteorological Agency， JMA）降水观测值与预报值之间的形变误差进行评价，提出了一种考虑降雨区域复杂变化的基于图像处理方法的降水临近预报新方法，从而在评价的基础上实时降低JMA降水临近预报的误差。强降水事件验证实验结果表明，该方法考虑了降雨区域复杂的变形过程，可显著提高降水临近预报的精度。此外，新方法预报的1小时预报精度比JMA原有降水临近预报的精度至少提高10%。

Forecasting method of local heavy rainfall was developed by using a model ensemble approach combining 32 member ensembles obtained from multiple parameterizations in the mesoscale meteorological model PSU/NCAR MM5. The multi-parameterization ensemble forecasts were combined with the model initialized using the 20km resolution global analyses JMA GSM-GPV with the Radar-data assimilation system. The forecasts averaged by 32 members are found to be very skillful in indicating the area of severe local storm activity, while each member deviates largely from the ensemble average.A new rainfall nowcasting approach based on the image processing method taking in complicated changes of rainfall area was developed, by evaluating the deformational errors between observed and predicted rainfall obtained by Japan Meteorological Agency(JMA) so as to minimize errors of the JMA rainfall nowcasting in real time based on its evaluation. Results of the verification experiments for intense rainfall events verified that the new approach can remarkably improve the accuracy of rainfall nowcasting by considering complicated deformation processes of rainfall area. Further, it made clear that the predictive value for one hour ahead forecasted by the new approach is at least 10% more accurate than that by the original JMA rainfall nowcasting.