1961~2010年华南极端降水日数的时空变化特征 |
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引用本文: | 何慧,陆虹,陈思蓉.1961~2010年华南极端降水日数的时空变化特征[J].安徽农业科学,2012(12):7256-7259,7276. |
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作者姓名: | 何慧 陆虹 陈思蓉 |
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作者单位: | 广西壮族自治区气候中心,广西南宁,530022 |
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摘 要: | 目的]分析1961~2010年华南极端降水日数的时空变化特征。方法]根据华南地区气象站逐日降水资料,以百分位值定义极端降水阈值,采用模糊聚类、趋势系数、小波分析、交叉谱分析等方法,对华南地区极端降水日数的时空变化特征进行研究。结果]华南地区极端降水日数可分为4个气候区,分别是南岭区、桂西区、沿海区和海南区,不同区域出现极端降水的季节有明显差异。各区域极端降水日数都有增加的趋势,其中,南岭区和沿海区增加趋势显著。各区域极端降水日数周期变化特征显著,且在2~5年周期上大多具有显著的同位相演变趋势,但落后时间长度不一致。结论]该研究为华南地区极端强降水的预测和影响评估提供了背景材料。
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关 键 词: | 华南 极端降水日数 时空变化特征 交叉谱分析 |
Temporal-spatial Variation Characteristics of the Extreme Precipitation Days over South China from 1961 to 2010 |
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Institution: | HE Hui et al(Climate Center of Guangxi,Nanning,Guangxi 530022) |
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Abstract: | Objective] The research aimed to analyze temporal-spatial variation characteristics of the extreme precipitation days over South China from 1961 to 2010.Method] Based on the daily precipitation data in meteorological stations over South China,extreme precipitation thresholds were determined according to the percentiles distribution for different stations.Temporal-spatial variation characteristics of the extreme precipitation days over South China were studied by the methods of fuzzy clustering,trend coefficient,wavelet analysis and cross spectrum analysis,etc.Result] Four sub-regions were identified over South China.They were respectively Nanling area,Guixi area,Coastal area and Hainan area.Occurrence seasons of the extreme precipitations in each sub-region were significantly different.Extreme precipitation days in four sub-regions all had increase trends,and those of Nanling area and Coastal area were significant.From wavelet analysis and cross spectrum analysis,there were significant periodic variation characteristics.Each sub-region all had significant same-phase evolution trends at the period of 2-5 years,but lagging time was different.Conclusion] The research provided background materials for forecast and influence assessment of the extremely heavy precipitation over South China. |
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Keywords: | South China Extreme precipitation days Temporal-spatial variation characteristics Cross spectrum analysis |
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