Papers
Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database
2011 Volume 10 Pages 1-12
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2011 Volume 10 Pages 1-12
Data mining is a valuable tool in meteorological applications. Properly selected data mining techniques enable researchers to process and analyze massive amounts of data collected by satellites and other instruments. Large spatial-temporal datasets can be analyzed using different linear and nonlinear methods. The Self-Organizing Map (SOM) is a promising tool for clustering and visualizing high dimensional data and mapping spatial-temporal datasets describing nonlinear phenomena. We present results of the application of the SOM technique in regions of interest within the European re-analysis data set. The possibility of detecting climate change signals through the visualization capability of SOM tools is examined.
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