利用广义帕雷托分布拟合中国东部日极端降水的试验

Extreme Precipitation Experimentation over Eastern China Based on Generalized Pareto Distribution

引进广义帕雷托分布拟合我国东部地区78个测站夏季（5-9月）逐日极端降水量。结果表明,不同门限值条件下的逐日降水量所拟合的降水极值概率分布均符合广义帕雷托分布,与其它极值分布如广义极值（下称GEV）分布模式相比,以GPD模式为最优。根据现代气候条件,分别计算了50年一遇和100年一遇的极端降水量分位数并分析其空间分布特征,两者基本一致,总体上都呈现出由东南向西北方减小的趋势,且南北差异较大,南方的极端降水量值可能达到北方地区的两倍以上。此外,资料年份越长,拟合效果越好。

The Generalized Pareto Distribution （GPD） is introduced to simulate the daily extreme precipitation at 78 stations over Eastern China from May to September. The results indicate that the probability distribution of the daily extreme precipitation is subjected to GPD under the different threshold, and GPD is superior to other extreme value distributions such as Generalized Extreme Value Distribution （GEV）. According to the observed climatic conditions from 1951--2000, the 50-year and 100-year return values of annual extreme precipitation are calculated, respectively and the space distribution features are analyzed. They are in substantial agreement which in general present downtrend from southeast to northwest, while there are obvious differences between south and north and the return values in the former area may reach more than twice compared to the latter one. In addition, the larger size of observed time series is, the better of the fitting is.