Adapting K-means clustering to identify spatial patterns in storms

This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular hour and overall storms have both spatial and temporal characteristics of the storm. We aim to find meaningful patterns and predict trajectories in the spatio-temporal data (i.e. overall storms which are sets of geographically overlapping, consecutive hourly storms). In this paper, we adapt K-Means clustering to find different types of hourly storms based on their shapes and sizes. Since the rainfall data are typically larger than the memory capacity of a single computer, we have implemented this clustering algorithm in Apache Spark, which is a distributed data processing framework, and have run our experiments on a computer cluster.