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contributor authorYokoi, Satoru
contributor authorTakayabu, Yukari N.
contributor authorNishii, Kazuaki
contributor authorNakamura, Hisashi
contributor authorEndo, Hirokazu
contributor authorIchikawa, Hiroki
contributor authorInoue, Tomoshige
contributor authorKimoto, Masahide
contributor authorKosaka, Yu
contributor authorMiyasaka, Takafumi
contributor authorOshima, Kazuhiro
contributor authorSato, Naoki
contributor authorTsushima, Yoko
contributor authorWatanabe, Masahiro
date accessioned2017-06-09T16:39:18Z
date available2017-06-09T16:39:18Z
date copyright2011/08/01
date issued2011
identifier issn1558-8424
identifier otherams-71650.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4213565
description abstracthe overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model?s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.
publisherAmerican Meteorological Society
titleApplication of Cluster Analysis to Climate Model Performance Metrics
typeJournal Paper
journal volume50
journal issue8
journal titleJournal of Applied Meteorology and Climatology
identifier doi10.1175/2011JAMC2643.1
journal fristpage1666
journal lastpage1675
treeJournal of Applied Meteorology and Climatology:;2011:;volume( 050 ):;issue: 008
contenttypeFulltext


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