Analyzing Meteorological Data Using Unsupervised Learning Techniques

Weather nowcasting which is the analysis and shortterm weather forecast is a topic of major interest both for the meteorological and machine learning researchers. The problem is a complex one due to the large volume of data (such as radar, satellite or other ground meteorological observations) which has to be analyzed by meteorologists for issuing nowcasting warnings. In addition, climate changes are chaotic and the climate models are complex. The main goal of the paper is to better understand the relationships between the meteorological products extracted from radar observations both in severe and normal weather conditions. Self organizing maps are proposed as unsupervised learning models for detecting hidden patterns in radar data which are well correlated with weather changes. Experiments performed on real radar data provided by the Romanian National Meteorological Administration highlight the potential of unsupervised learning to uncover in radar data hidden rules which are relevant from a meteorological perspective. The results of our study suggest promising results in applying predictive supervised learning models for weather nowcasting based on radar data.