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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.
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Lakshmanan et al. (2013) studied this question.
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