Interpreting Self-Organizing Map errors in the classification of ocean patterns

The paper aims to introduce quality measures that can evaluate how well the Self-organizing Maps method performs in transitional stages. The errors have been computed with respect to the spatial and temporal properties of the data and in relation to the data gap significance. Temperature and salinity data collected in the central Adriatic Sea at six stations during 196 field cruises carried out between 1963 and 2011 have been used for the mapping of ocean patterns and computation of the respective errors. The errors resemble both the stability of ocean regimes and variability of patterns that are documented in the investigated region. As the data collection methodology and approach have changed over time, the errors may be a good indication for the presence of bad data in a series, which may then be controlled by other quality-check techniques.