Performance evaluation of the self-organizing map for feature extraction

Despite its wide applications as a tool for feature extraction, the Self-Organizing Map (SOM) remains a black box to most meteorologists and oceanographers. This paper evaluates the feature extraction performance of the SOM by using artificial data representative of known patterns. The SOM is shown to extract the patterns of a linear progressive sine wave. Sensitivity studies are performed to ascertain the effects of the SOM tunable parameters. By adding random noise to the linear progressive wave data, it is demonstrated that the SOM extracts essential patterns from noisy data. Moreover, the SOM technique successfully chooses among multiple sets of patterns in contrast with an Empirical Orthogonal Function method that fails to do this. A practical way to apply the SOM is proposed and demonstrated using several examples, including long time series of coastal ocean currents from the West Florida Shelf. With improved SOM parameter choices, strong current patterns associated with severe weather forcing are extracted separate from previously identified asymmetric upwelling/downwelling and transitional patterns associated with more typical weather forcing.