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Meteorological data analysis using self-organizing maps
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DOI：10.1002/int.20294.png)
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A data analysis task is described, which is focused on the clustering of high-dimensional meteorological data collected long term (more than 43 years) at 128 weather stations in Greece. The proposed hybrid method combines (a) the assignment of the stations to two-dimensional grids of nodes via self-organizing maps (SOMs) of various sizes and (b) statistical clustering of the SOM nodes. The areas resulting from clustering have well-defined meteorological profiles; they are also described by distinct combinations of morphological and geographical characteristics, indicating that morphology and geographical location largely affect the meteorological measurements. The most salient data parameters per area as well as over the entire map are determined, whereby the parameters and parameter ranges that shape the various meteorological profiles are exposed. The classification of stations with missing and noise-contaminated meteorological measurements into their expected areas demonstrates the prediction capability and robustness of the proposed hybrid method. (c) 2008 Wiley Periodicals, Inc.
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