A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events

Extreme weather events, like extreme rainfalls, are severe weather hazards and also the triggers for other natural disasters like floods and tornadoes. Accurate forecasting of such events relies on the understanding of the spatiotemporal evolution processes in climate system. Learning from climate science data has been a challenging task, because the variations among spatial, temporal and multivariate spaces have created a huge amount of features and complex regularities within the data. In this study we developed a framework for learning patterns from the spatiotemporal system and forecasting extreme weather events. In this framework, we learned patterns in a hierarchical manner: in each level, new features were learned from data and used as the input for the next level. Firstly, we summarized the temporal evolution process of individual variables by learning the location-based patterns. Secondly, we developed an optimization algorithm for summarizing the spatial regularities, SCOT, by growing spatial clusters from the location-based patterns. Finally, we developed an instance-based algorithm, SPC, to forecast the extreme events through classification. We applied this framework to forecasting extreme rainfall events in the eastern Central Andes area. Our experiments show that this method was able to find climatic process patterns similar to those found in domain studies, and our forecasting results outperformed the state-of-art model.