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吴伟杰, 郑伟鹏, 郑秀云, 杨奇志, 彭婕, 郭林. 1980~2017年厦门地区小时尺度极端降水特征分析[J]. 气候与环境研究, 2019, 24(3): 359-368. DOI: 10.3878/j.issn.1006-9585.2019.18139
引用本文: 吴伟杰, 郑伟鹏, 郑秀云, 杨奇志, 彭婕, 郭林. 1980~2017年厦门地区小时尺度极端降水特征分析[J]. 气候与环境研究, 2019, 24(3): 359-368. DOI: 10.3878/j.issn.1006-9585.2019.18139
WU Weijie, ZHENG Weipeng, ZHENG Xiuyun, YANG Qizhi, PENG Jie, GUO Lin. Characteristics of Extreme Hourly Precipitation inXiamen during 1980-2017[J]. Climatic and Environmental Research, 2019, 24(3): 359-368. DOI: 10.3878/j.issn.1006-9585.2019.18139
Citation: WU Weijie, ZHENG Weipeng, ZHENG Xiuyun, YANG Qizhi, PENG Jie, GUO Lin. Characteristics of Extreme Hourly Precipitation inXiamen during 1980-2017[J]. Climatic and Environmental Research, 2019, 24(3): 359-368. DOI: 10.3878/j.issn.1006-9585.2019.18139

1980~2017年厦门地区小时尺度极端降水特征分析

Characteristics of Extreme Hourly Precipitation inXiamen during 1980-2017

  • 摘要: 利用1980~2017年厦门逐小时降水资料和NCEP再分析资料,分析厦门地区极端降水事件的气候特征,并初步讨论其成因。研究结果表明:1)极端降水事件的年发生频率呈现减少的趋势,厦门岛的减少趋势要比内陆更为显著。2)小时尺度的极端降水事件在较小尺度空间内无论是发生频率还是强度都存在明显的区域性差异,内陆地区在发生频率和强度上均高于厦门岛,但强度的平均值一致。3)造成极端降水事件的天气系统有4类,分别是热带气旋型、冷式切变型、西南风气流型和低槽冷锋型。随着城市抗灾能力的提升,对极端降水预报的要求也不断提高,基于小时值的结论可以为未来厦门地区极端降水事件的预报提供参考基础,进而提升预报的有效性和针对性。

     

    Abstract: Based on the hourly precipitation data and NCEP reanalysis data from 1980 to 2017, extreme precipitation events in Xiamen are analyzed to explore their climatic characteristics and related weather patterns. The results show the following: 1) Interannual variabilities show decreasing trends; the island exhibits a significant decreasing trend than the inland. 2) The extreme precipitation events have significant regional differences, the intensity and frequency of inland events are stronger than those of island events. However, the average intensities in both inland and island are the same. 3) The induced weather systems are categorized into four types, thase are the type of tropical cyclones, type of cold shear, type of south-west wind, and type of trough combined with cold front. With continuous development of the city, a more accurate extreme precipitation forecast is required. The results of this study are based on the hourly data, and it will possibly make the operational forecast more efficient.

     

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