Weather Pattern Classification to Represent the Urban Heat Island in Present and Future Climate
Weather Pattern Classification to Represent the Urban Heat Island in Present and Future Climate
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DOI：
10.1175/jamc-d-12-065.1
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发表时间：
2013-12
影响因子：
3
通讯作者：
P. Hoffmann;K. Schlünzen
中科院分区：
文献类型：
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作者：
P. Hoffmann;K. Schlünzen
摘要：天气模式（WP）的分类是为了最好地代表与城市热岛（UHI）相关的情况而衍生的。进行了三种不同类型的基于k均值的聚类方法。解释的聚类方差被用作质量的度量。利用40年ECMWF重新分析（ERA-40）的700 hpa油田的几个变量进行了分类测试。变量和聚类域的选择尽可能地解释了城市热岛指数的可变性。结果表明，位势高度、相对湿度、涡度和1000 ~ 700 hpa厚度的组合最适合。为了确定最优簇数k，应用了几种统计度量。除秋季（k = 12）外，最优聚类数为k = 7。利用两种区域气候模式（RCM）的气候预估分析了WP频率的变化。区域模式（REMO）和气候有限区域模式（CLM）这两种rcm都是由…
AbstractA classification of weather patterns (WP) is derived that is tailored to best represent situations relevant for the urban heat island (UHI). Three different types of k-means-based cluster methods are conducted. The explained cluster variance is used as a measure for the quality. Several variables of the 700-hPa fields from the 40-yr ECMWF Re-Analysis (ERA-40) were tested for the classification. The variables as well as the domain for the clustering are chosen in a way to explain the variability of the UHI as best as possible. It turned out that the combination of geopotential height, relative humidity, vorticity, and the 1000–700-hPa thickness is best suited. To determine the optimal cluster number k several statistical measures are applied. Except for autumn (k = 12) an optimal cluster number of k = 7 is found. The WP frequency changes are analyzed using climate projections of two regional climate models (RCM). Both RCMs, the Regional Model (REMO) and Climate Limited-Area Model (CLM), are driven ...