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Using self-organizing maps as unsupervised learning models for meteorological data mining

One of the most challenging topics within the meteorological domain is weather forecasting, with a particular focus on predicting severe weather. Weather forecasting for short-time periods (0 to 6 hours) is called weather nowcasting and is of major interest both for researchers and for operational meteorologists. For operational meteorologists, the task of issuing nowcasting warnings is not an easy one as it is highly dependent on their experience in analysing and correlating various meteorological observations. A study on using self-organizing maps for analyzing radar data is conducted in this paper, with the aim of detecting meaningful patterns which are relevant for short-term weather prediction. The Romanian National Meteorological Administration has provided actual radar data which is used in our experiments and highlight that self-organizing maps have the potential to uncover meteorological relevant relationships.