Performing spatial analysis on tropical cyclone rainband structures after creating a 3D Mosaic of WSR-88D reflectivity data using a map-reduce framework and a Geographic Information System (GIS)

The high spatial and temporal resolution data produced by ground-based radars provide important observations of tropical cyclone structure as these systems move over land. Data must be mosaicked from more than a dozen WSR-88D radars to study the evolution of tropical cyclone rain fields in their entirety, which implies a high volume of data for processing. To facilitate the spatial analysis of radar observations, we develop a Map-reduce-based playback framework using Apache Spark's computational engine to interpolate large volumes of radar reflectivity data onto 3D grids. Because our research focuses on historical cases that do not require processing in real time, we utilize data from two sides of a moving temporal window and process them at adjustable speeds so that our system maintains product quality, timeliness and scalability. We utilize a Geographic Information System (GIS) to interpolate values at grid points where raw data do not exist and to perform spatial analysis functions. Once data are interpolated to the grid, shape metrics are calculated to quantify the spatial distribution of different reflectivity values along horizontal slices at constant altitudes. These shape metrics allow us to compare rainband structures among different storms, and relate observed data from WSR-88D units to modeled storms.