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王智, 范旭亮, 于甜甜. 2018: 一次长三角地区暴雨过程的集合预报应用与分析. 暴雨灾害, 37(1): 8-13. DOI: 10.3969/j.issn.1004-9045.2018.01.002
引用本文: 王智, 范旭亮, 于甜甜. 2018: 一次长三角地区暴雨过程的集合预报应用与分析. 暴雨灾害, 37(1): 8-13. DOI: 10.3969/j.issn.1004-9045.2018.01.002
WANG Zhi, FAN Xuliang, YU Tiantian. 2018: Ensemble forecast analysis of a heavy rainfall event in the Yangtze River delta. Torrential Rain and Disasters, 37(1): 8-13. DOI: 10.3969/j.issn.1004-9045.2018.01.002
Citation: WANG Zhi, FAN Xuliang, YU Tiantian. 2018: Ensemble forecast analysis of a heavy rainfall event in the Yangtze River delta. Torrential Rain and Disasters, 37(1): 8-13. DOI: 10.3969/j.issn.1004-9045.2018.01.002

一次长三角地区暴雨过程的集合预报应用与分析

Ensemble forecast analysis of a heavy rainfall event in the Yangtze River delta

  • 摘要: 利用ECMWF集合预报对2016年6月11—12日发生在长三角地区的一次暴雨过程进行了分析,并对集合“好” “坏”两类成员的预报结果进行了对比。分析表明:集合预报对本次暴雨过程具有比较好的预报能力,集合平均预报效果要优于确定性预报,其雨量预报的增大趋势对暴雨的预报具有一定的指示意义;高分位数集合成员对于暴雨预报有比较好的参考价值,尤其是在预报时效还较长的时候,如果连续多起报时次高分位数集合成员都预报出暴雨,以及低分位数集合成员的雨量预报呈现逐渐增大趋势,预示着暴雨的可能性在增大,有助于暴雨预报的决策;对天气系统和气象要素的预报差异是造成“好”“坏”两类集合成员对本次暴雨过程模拟效果差异的主要原因,对500 hPa高空槽、850 hPa低涡及其切变线、西南气流和偏东气流的模拟是决定“好”“坏”两类集合成员模拟效果的关键因素。

     

    Abstract: Using ECMWF ensemble forecasts, a heavy rainfall event in the Yangtze River delta was analyzed in this paper. The"good"and "bad"clusters of ECMWF ensemble members were also compared. The results show that the ensemble products, such as ensemble mean and quantile products have better forecast skills for the heavy rainfall event compared to the deterministic forecasts. The precipitation forecast of high quantile products is a good indication to heavy rainfall, especially for a long forecast length. The continuous forecasts of heavy rainfall by high quantile products, and the increasing trend of precipitation forecasts by ensemble mean and lower quantile products indicate that heavy rainfall is very likely to happen. The forecast differences of weather systems and meteorological elements, including the trough in 500 hPa, vortex and wind shear in 850 hPa, southwesterly and easterly winds, between the"good"and"bad"clusters of ensemble members have key impacts on the forecast skills.

     

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