The probability distribution of intense daily precipitation

被引:79
作者
Cavanaugh, Nicholas R. [1 ,2 ]
Gershunov, Alexander [2 ]
Panorska, Anna K. [3 ]
Kozubowski, Tomasz J. [3 ]
机构
[1] Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Earth Sci, Berkeley, CA 94720 USA
[2] Univ Calif San Diego, Scripps Inst Oceanog, Climate Atmospher Sci & Phys Oceanog Div, La Jolla, CA 92093 USA
[3] Univ Nevada, Dept Math & Stat, Reno, NV 89557 USA
关键词
extreme; Pareto; probability; precipitation; weather station; EXTREME ORDER-STATISTICS; DAILY RAINFALL; CLIMATE-CHANGE; UNITED-STATES; MODELS; TRENDS; TAILS; TEMPERATURE; VARIABILITY; RECORD;
D O I
10.1002/2015GL063238
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical and practical implications for the modeling of high-frequency climate variability worldwide.
引用
收藏
页码:1560 / 1567
页数:8
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