A MapReduce Technique to Mosaic Continental-Scale Weather Radar Data in Real-Time

It is necessary to create continental-scale mosaics of radar data in real-time for applications ranging from precipitation estimation, hail diagnosis and tornado warnings to public awareness. Because of the high temporal and spatial resolution of data available from the United States´ network of weather radars, creating radar mosaics in real-time has been possible only through compromises on the quality, timeliness or resolution of the mosaics. MapReduce is a programming model that can be employed for processing and generating large data sets by distributing parallel computations and data storage across a distributed cluster of machines. In this paper, a MapReduce approach to computing radar mosaics on a distributed cluster of compute nodes is presented. The approach is massively scalable, and is able to create high-resolution 3D radar mosaics over the Continental United States in real-time.