The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Classifications of Atmospheric Circulation Patterns: Recent Advances and Applications
Open accessAbstract
We review recent advances in classifications of circulation patterns as a specific research area within synoptic climatology. The review starts with a general description of goals of classification and the historical development in the field. We put circulation classifications into a broader context within climatology and systematize the varied methodologies and approaches. We characterize three basic groups of classifications: subjective (also called manual), mixed (hybrid), and objective (computer‐assisted, automated). The roles of cluster analysis and principal component analysis in the classification process are clarified. Several recent methodological developments in circulation classifications are identified and briefly described: the introduction of nonlinear methods, objectivization of subjective catalogs, efforts to optimize classifications, the need for intercomparisons of classifications, and the progress toward an optimum, if possible unified, classification method. Among the recent tendencies in the applications of circulation classifications, we mention a more extensive use in climate studies, both of past, present, and future climates, innovative applications in the ensemble forecasting, increasing variety of synoptic–climatological investigations, and steps above from the troposphere. After introducing the international activity within the field of circulation classifications, the COST733 Action, we briefly describe outputs of the inventory of classifications in Europe, which was carried out within the Action. Approaches to the evaluation of classifications and their mutual comparisons are also reviewed. A considerable part of the review is devoted to three examples of applications of circulation classifications: in historical climatology, in analyses of recent climate variations, and in analyses of outputs from global climate models.
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Cites
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Cited by
Han, H., Liu, J., Shu, L., Wang, T., & Yuan, H. (2020) · Atmospheric Chemistry and Physics · doi:10.5194/acp-20-203-2020 · Cited by 331
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
Bochenek, B., & Ustrnul, Z. (2022) · Atmosphere · doi:10.3390/atmos13020180 · Cited by 325
Self-citationSanuy, M., Rigo, T., Jiménez, J. A., & Llasat, M. C. (2021) · Hydrology and Earth System Sciences · doi:10.5194/hess-25-3759-2021 · Cited by 34
Metadata
- Authors
- Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., & Tveito, O. E.
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2008-12
- Volume
- 1146
- Issue
- 1
- Pages
- 105-152
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- OpenAlex
- W2052872719
- Cited by
- 619
- References
- 2
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., & Tveito, O. E. (2008). Classifications of Atmospheric Circulation Patterns: Recent Advances and Applications. Annals of the New York Academy of Sciences. https://doi.org/10.1196/annals.1446.019
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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