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Application Research of Big Data in Heavy Rainfall Forecast Model in Meiyu Season

机译：大雨预测模型大数据的应用研究在梅玉季

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摘要

In this paper, 33 classic Meiyu precipitation processes in recent 30 years are selected by using historical observation big data, and the inter-annual variation characteristics of heavy rainfall location are analyzed by descriptive big data analysis method. Besides, it is verified that there is a close connection between rainfall location and 500 hPa 5840 geopotential meter isoline. However, serious errors appeared in the forecast model during the medium-range precipitation forecast (4-10 days) from June 30th to July 4th, 2016. Therefore, in this paper, the causes of errors are analyzed by diagnostic big data analysis method using European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast data. The results show that the premise for an accurate forecast by the classic forecast model is that, the heavy precipitation process must be accompanied by a southward-moving cold air. As the precipitation was a warm area rainfall in the monsoon region, errors were caused by the lack of high-level cold air participation. On one hand, this study proves the important impact of southward-moving cold air on the accuracy of rain belt location forecast. On the other, it will undoubtedly serve as an important reference for the subjective correction of the rain belt location in the forecast operation.
机译：本文通过使用历史观察大数据选择近30年的33个经典美云降水过程，通过描述性大数据分析方法分析了大雨位置的年间变化特性。此外，还核实降雨地区与500 HPA 5840地理电视率ISOLINE之间存在密切联系。然而，预测模型在2016年6月30日至7月4日的中距离降水预测（4-10天）期间出现了严重错误。因此，在本文中，通过诊断大数据分析方法使用误差的原因欧洲中等地区天气预报中心（ECMWF）集合预测数据。结果表明，经典预测模型准确预测的前提是，沉重的降水过程必须伴随着南方移动的冷空气。随着季风地区的降水量是温暖的区域降雨，误差是由于缺乏高水平的冷空气参与引起的。一方面，该研究证明了向南移动冷空气对雨带定位预测的准确性的重要影响。另一方面，它无疑将作为预测操作中雨带位置的主观校正的重要参考。

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