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  Climate classifications: the value of unsupervised clustering

Zscheischler, J., Mahecha, M., & Harmeling, S. (2012). Climate classifications: the value of unsupervised clustering. In H. Ali, Y. Shi, D. Khazanchi, M. Lees, G. van Albada, P. Sloot, et al. (Eds.), Procedia Computer Science (pp. 897-906). Amsterdam, Netherlands: Elsevier.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B728-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1A0F-3
Genre: Conference Paper

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 Creators:
Zscheischler, J1, Author              
Mahecha, MD, Author
Harmeling, S1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: Classifying the land surface according to dierent climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classifications such as the prominent K¨oppen–Geiger (KG) approach rely on heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of “climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring dierent combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K¨oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.

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Language(s):
 Dates: 2012-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.procs.2012.04.096
BibTex Citekey: ZscheischlerMH2012
 Degree: -

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Title: International Conference on Computational Science (ICCS 2012)
Place of Event: Omaha, NE, USA
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Title: Procedia Computer Science
Source Genre: Proceedings
 Creator(s):
Ali, H, Editor
Shi, Y, Editor
Khazanchi, D, Editor
Lees, M, Editor
van Albada, GD, Editor
Sloot, PMA, Editor
Dongarra, J, Editor
Affiliations:
-
Publ. Info: Amsterdam, Netherlands : Elsevier
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 897 - 906 Identifier: -