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空间点过程理论在极端气候事件中的应用研究
Cluster Extreme Events Based on Point Process Theory
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摘要: 该文定义了12种极端温度事件和6种极端降水事件，将基于空间点过程理论的k阶最近邻距离丛集点提取算法应用于极端温度和极端降水事件的研究，给出了极端温度和极端降水事件区域群发性站点的检测流程；采用多年平均的疏密差异比R和有效率η两个指标，对所定义的各种极端气候事件的区域群发性进行了有效性检验，结果表明：k阶最近邻距离丛集点提取算法适用于极端气候事件的区域群发性研究，并进一步阐明了极端气候事件区域群发性的物理含义。Abstract: Extreme weather and climate events have attracted more attention in the last few years due to the often large loss of human life and exponentially increasing costs coping with them. The assessment of extreme climate events has found that extreme events are showing the characteristic of clustering. Many natural phenomena manifest themselves as spatial point processes which produce numerous events in space, such as extreme events. For clarification, the occurrence of a phenomenon located at a single point is defined as an event in contrast to a simple geometric point. Some events assemble in a restricted region while other events are dispersed. Regarding extreme events as spatial point processes and the cluster events as the clustered points, a method is developed to delineate the cluster events from a region which is called k-th order nearest distance spatial point method. By combining spatial point theory with cluster events, the algorithm is validated with a new method which consists of four major steps. The first step is to define different kinds of extreme events including 12 kinds of extreme temperature events and 6 kinds of extreme precipitation events. The second step is to calculate the statistically weights of every station based on the frequency and strength. The third step is to give the spatial distributions of all the stations that extreme events happens and the last step is to pick out the clustered events combining the spatial theory. Based on this method, extreme events research in eastern and middle areas of China have been accomplished, including extreme temperature events and extreme precipitation events. Results of k-th order nearest distance, the material definitions of weights and the distribution charts are given, which can detect cluster extreme events effectively. Based on the given charts and two efficient indexes (one is called average of ratio which is represented by R, the other is called efficiency represented by η), all kinds of extreme climate events are tested to validate the method, finding out that the algorithm is fit for cluster extreme climate events, especially for heavier extreme events. The method performs better on temperature extreme events than precipitation extreme events. So it's concluded that cluster extreme events can be delineated not only by qualitative research, but also by quantitative research, which offers a new idea in extreme events researches.
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Key words:
- extreme events;
- cluster;
- k-th order nearest distance;
- cluster point
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图 2 2000年夏季高温事件的空间分布
(a) 群发提取结果图 (“+”表示发生极端高温事件的站点, “●”表示提取出的群发性站点), (b) 权重分布图 (数字“1” “5”代表所在位置上站点的权重)
Fig. 2 Stations over China about temperature extremein 2000
(a) cluster distribution ("+"represent the station soccurring temperature extreme, "●"represent the cluster stations occurring temperature extreme), (b) power distribution (number "1"—"5" represent station spower)
图 3 12种极端温度事件有效性检测结果图
(a)12种极端温度事件的R, (b)12种极端温度事件的η
Fig. 3 Tempretureex treme's average of ratio R
(a) and efficient η (b)
图 4 6种极端降水事件有效性检测结果图
(a) 极端降水事件的R, (b) 极端降水事件的η
Fig. 4 Precipitation extreme's average of ratio R
(a) an defficient η (b)
表 1 极端温度事件的定义
Table 1 Different defintions of temparture extreme
表 2 极端降水事件的定义
Table 2 Different defintions of precipitation extreme
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