Abstract
AbstractThe creation of a 3D mosaic is often the first step when using the high-spatial- and temporal-resolution data produced by ground-based radars. Efficient yet accurate methods are needed to mosaic data from dozens of radar to better understand the precipitation processes in synoptic-scale systems such as tropical cyclones. Research-grade radar mosaic methods of analyzing historical weather events should utilize data from both sides of a moving temporal window and process them in a flexible data architecture that is not available in most stand-alone software tools or real-time systems. Thus, these historical analyses require a different strategy for optimizing flexibility and scalability by removing time constraints from the design. This paper presents a MapReduce-based playback framework using Apache Spark’s computational engine to interpolate large volumes of radar reflectivity and velocity data onto 3D grids. Designed as being friendly to use on a high-performance computing cluster, these methods may also be executed on a low-end configured machine. A protocol is designed to enable interoperability with GIS and spatial analysis functions in this framework. Open-source software is utilized to enhance radar usability in the nonspecialist community. Case studies during a tropical cyclone landfall shows this framework’s capability of efficiently creating a large-scale high-resolution 3D radar mosaic with the integration of GIS functions for spatial analysis.
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