Complete storm identification algorithms from big raw rainfall data using MapReduce framework

In our previous work, we described various aspects of our approach in converting big raw rainfall data into meaningful storm concepts. Three concepts were defined: local, hourly, and overall storms. The latter describes overall spatio-temporal characteristics of a storm as it progresses over time. We previously described MapReduce-based algorithms for local and hourly storm identification. Overall storms are the most complex to identify, and are at the core of the storm identification system. Multiple consecutive hourly storms that have spatial overlap are combined to create storm-centric characteristics of the whole storm, which could not be captured in most existing hydrology research. In this paper, we propose a MapReduce-based overall storm identification algorithm, which is based on iteration on MapReduce framework. This greatly improves performance when compared to the depth-first search (DFS) graph traveling approach as introduced in our previous work. In addition, additional essential storm characteristics of hourly and overall storms are introduced in this paper. Examples include storm center concepts for hourly storms and storm track and speed for overall storms.