Parallel Processing of Geospatial Time-Series Data

One way of quantifying how much sunlight the earth receives, at a particular location and time, is to compute a Cloud Index from satellite images. This calculation involves processing large files of data that are collected in a way that makes time-series analysis extremely time consuming on a single computer. There are several ways to render this memory-intensive problem more feasible usingparallel programming. Our implementation uses the Message Passing Interface (MPI) protocol and is an excellent balance between implementation complexity and execution time. We believe that this will also scale well as more complex algorithms for computing the Cloud Index emerge. This work extends to any domain where time-series analysis is needed on large data sets that are collectedas a function of time.