Multi-dimensional reduction using Self-Organizing Map

Self-Organising Map (SOM) is found to be a useful tool for climatological synoptic, analysis in extreme and rainfall pattern, cloud classification and climate change analysis. In data preprocessing for use in statistical downscaling, Principal Component Analysis (PCA) or empirical orthogonal function (EOF) analysis is used to select the mode criterion for the predictor and predictand fields for building a model. However, EOF contributes less total variance for most cases of which 70% to 90% of total population variance is accounted in the analysis. Therefore, SOM is proposed to obtain a nonlinear mapping for the preprocessing process. This study examines the dimension reduction of NCEP variable using SOM during the periods of November-December-January-February (NDJF). The NCEP data used is the 20 grids point atmospheric data for variable Sea Level Pressure (SLP). The result showed that SOM had extracted the high dimensional data onto a low dimensional representation.