| 空间点过程理论在极端气候事件中的应用研究 |
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| 引用本文： | 杨萍,封国林,刘伟东,侯威,孙树鹏.空间点过程理论在极端气候事件中的应用研究[J].应用气象学报,2010,21(3):352-359. |
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| 作者姓名： | 杨萍 封国林 刘伟东 侯威 孙树鹏 |
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| 作者单位： | 1.中国气象局北京城市气象研究所, 北京 100089 |
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| 基金项目： | 国家自然科学基金项目，科技部支撑项目"极端气候事件的检测和可预测性关键技术研发"，"气候变化的检测和预估技术研究"，公益性行业(气象)科研专项 |
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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。
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| 关 键 词： | 极端气候事件 区域群发性 k阶最近邻距离 丛集点 |
| 收稿时间： | 2009-09-24 |
Cluster Extreme Events Based on Point Process Theory |
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| Yang Ping,Feng Guolin,Liu Weidong,Hou Wei and Sun Shupeng.Cluster Extreme Events Based on Point Process Theory[J].Quarterly Journal of Applied Meteorology,2010,21(3):352-359. |
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| Authors: | Yang Ping Feng Guolin Liu Weidong Hou Wei and Sun Shupeng |
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| Affiliation: | 1.Institute of Urban Meteorology, CMA, Beijing 1000892.National Climate Center, Beijing 100081 |
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| 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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| Keywords: | extreme events cluster k th order nearest distance cluster point |
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