Data-Driven versus Köppen–Geiger Systems of Climate Classification
Climate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the cl...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/3581299 |
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| author | Vajira Lasantha Taikan Oki Daisuke Tokuda |
| author_facet | Vajira Lasantha Taikan Oki Daisuke Tokuda |
| author_sort | Vajira Lasantha |
| collection | DOAJ |
| description | Climate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification. |
| format | Article |
| id | doaj-art-34f4be85178f4b8fad6335a4330ef0bd |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-34f4be85178f4b8fad6335a4330ef0bd2025-02-03T01:22:53ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/3581299Data-Driven versus Köppen–Geiger Systems of Climate ClassificationVajira Lasantha0Taikan Oki1Daisuke Tokuda2Graduate School of EngineeringGraduate School of EngineeringGraduate School of EngineeringClimate zone classification promotes our understanding of the climate and provides a framework for analyzing a range of environmental and socioeconomic data and phenomena. The Köppen–Geiger classification system is the most widely used climate classification scheme. In this study, we compared the climate zones objectively defined using data-driven methods with Köppen–Geiger rule-based classification. Cluster analysis was used to objectively delineate the world’s climatic regions. We applied three clustering algorithms—k-means, ISODATA, and unsupervised random forest classification—to a dataset comprising 10 climatic variables and elevation; we then compared the obtained results with those from the Köppen–Geiger classification system. Results from both the systems were similar for some climatic regions, especially extreme temperature ones such as the tropics, deserts, and polar regions. Data-driven classification identified novel climatic regions that the Köppen–Geiger classification could not. Refinements to the Köppen–Geiger classification, such as precipitation-based subdivisions to existing Köppen–Geiger climate classes like tropical rainforest (Af) and warm summer continental (Dfb), have been suggested based on clustering results. Climatic regions objectively defined by data-driven methods can further the current understanding of climate divisions. On the other hand, rule-based systems, such as the Köppen–Geiger classification, have an advantage in characterizing individual climates. In conclusion, these two approaches can complement each other to form a more objective climate classification system, wherein finer details can be provided by data-driven classification and supported by the intuitive structure of rule-based classification.http://dx.doi.org/10.1155/2022/3581299 |
| spellingShingle | Vajira Lasantha Taikan Oki Daisuke Tokuda Data-Driven versus Köppen–Geiger Systems of Climate Classification Advances in Meteorology |
| title | Data-Driven versus Köppen–Geiger Systems of Climate Classification |
| title_full | Data-Driven versus Köppen–Geiger Systems of Climate Classification |
| title_fullStr | Data-Driven versus Köppen–Geiger Systems of Climate Classification |
| title_full_unstemmed | Data-Driven versus Köppen–Geiger Systems of Climate Classification |
| title_short | Data-Driven versus Köppen–Geiger Systems of Climate Classification |
| title_sort | data driven versus koppen geiger systems of climate classification |
| url | http://dx.doi.org/10.1155/2022/3581299 |
| work_keys_str_mv | AT vajiralasantha datadrivenversuskoppengeigersystemsofclimateclassification AT taikanoki datadrivenversuskoppengeigersystemsofclimateclassification AT daisuketokuda datadrivenversuskoppengeigersystemsofclimateclassification |