Application of Cluster Analysis to Climate Model Performance Metrics
The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individualmodels thatmeasure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate...
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| Veröffentlicht in: | Journal of applied meteorology and climatology 2011-08, Vol.50 (8), p.1666-1675 |
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| creator | Yokoi, Satoru Takayabu, Yukari N. Nishii, Kazuaki Nakamura, Hisashi Endo, Hirokazu Ichikawa, Hiroki Inoue, Tomoshige Kimoto, Masahide Kosaka, Yu Miyasaka, Takafumi Oshima, Kazuhiro Sato, Naoki Tsushima, Yoko Watanabe, Masahiro |
| description | The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individualmodels thatmeasure 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 performancemetrics. Two clusteringmethods, theK-means and the Wardmethods, 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. |
| doi_str_mv | 10.1175/2011jamc2643.1 |
| format | Article |
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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 performancemetrics. Two clusteringmethods, theK-means and the Wardmethods, 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.</description><identifier>ISSN: 1558-8424</identifier><identifier>EISSN: 1558-8432</identifier><identifier>DOI: 10.1175/2011jamc2643.1</identifier><identifier>CODEN: JOAMEZ</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Analysis ; Atmospheric models ; Business metrics ; Climate ; Climate change ; Climate models ; Climatic analysis ; Cluster analysis ; Clustering ; Constraint modelling ; Correlation ; Correlations ; Datasets ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; General circulation models ; Global climate models ; Meteorology ; Methods ; Modeling ; Modelling ; Performance evaluation ; Performance measurement ; Performance metrics ; Radiation ; Reproducibility ; Simulations ; Studies ; Wind</subject><ispartof>Journal of applied meteorology and climatology, 2011-08, Vol.50 (8), p.1666-1675</ispartof><rights>2011 American Meteorological Society</rights><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society 2011</rights><rights>Copyright American Meteorological Society Aug 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c484t-df52367e184d4993afc04caf669d0213957516fa587f721e38615dd0d1814f0d3</citedby><cites>FETCH-LOGICAL-c484t-df52367e184d4993afc04caf669d0213957516fa587f721e38615dd0d1814f0d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26174121$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26174121$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,3668,27901,27902,57992,58225</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24426492$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yokoi, Satoru</creatorcontrib><creatorcontrib>Takayabu, Yukari N.</creatorcontrib><creatorcontrib>Nishii, Kazuaki</creatorcontrib><creatorcontrib>Nakamura, Hisashi</creatorcontrib><creatorcontrib>Endo, Hirokazu</creatorcontrib><creatorcontrib>Ichikawa, Hiroki</creatorcontrib><creatorcontrib>Inoue, Tomoshige</creatorcontrib><creatorcontrib>Kimoto, Masahide</creatorcontrib><creatorcontrib>Kosaka, Yu</creatorcontrib><creatorcontrib>Miyasaka, Takafumi</creatorcontrib><creatorcontrib>Oshima, Kazuhiro</creatorcontrib><creatorcontrib>Sato, Naoki</creatorcontrib><creatorcontrib>Tsushima, Yoko</creatorcontrib><creatorcontrib>Watanabe, Masahiro</creatorcontrib><title>Application of Cluster Analysis to Climate Model Performance Metrics</title><title>Journal of applied meteorology and climatology</title><description>The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individualmodels thatmeasure 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 performancemetrics. Two clusteringmethods, theK-means and the Wardmethods, 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.</description><subject>Analysis</subject><subject>Atmospheric models</subject><subject>Business metrics</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatic analysis</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Constraint modelling</subject><subject>Correlation</subject><subject>Correlations</subject><subject>Datasets</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>General circulation models</subject><subject>Global climate models</subject><subject>Meteorology</subject><subject>Methods</subject><subject>Modeling</subject><subject>Modelling</subject><subject>Performance evaluation</subject><subject>Performance measurement</subject><subject>Performance 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Analysis to Climate Model Performance Metrics</atitle><jtitle>Journal of applied meteorology and climatology</jtitle><date>2011-08-01</date><risdate>2011</risdate><volume>50</volume><issue>8</issue><spage>1666</spage><epage>1675</epage><pages>1666-1675</pages><issn>1558-8424</issn><eissn>1558-8432</eissn><coden>JOAMEZ</coden><abstract>The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individualmodels thatmeasure 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 performancemetrics. Two clusteringmethods, theK-means and the Wardmethods, 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. 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| source | Jstor Complete Legacy; American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Analysis Atmospheric models Business metrics Climate Climate change Climate models Climatic analysis Cluster analysis Clustering Constraint modelling Correlation Correlations Datasets Earth, ocean, space Exact sciences and technology External geophysics General circulation models Global climate models Meteorology Methods Modeling Modelling Performance evaluation Performance measurement Performance metrics Radiation Reproducibility Simulations Studies Wind |
| title | Application of Cluster Analysis to Climate Model Performance Metrics |
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