Geo-Spatial Comparison of Rain Gauge and NEXRAD Data for Central and South Florida

Abstract

The South Florida Water Management District (District) is responsible for managing water resources in 16-counties over a 46,439-square kilometer (17,930 square-mile) area. The area extends from Orlando to Key West and from the Gulf Coast to the Atlantic Ocean and contains the country’s second largest lake – Lake Okeechobee and the world famous Everglades wetlands. The District operates approximately 3,000 kilometers (~1,800 miles) of canals, 22 major pump stations and 200 water control structures. Nearreal-time rainfall data are used in operation of these pumps and water control structures. The District uses a network of approximately 287 active rain gauge stations that cover the more populated and environmentally sensitive areas under its management and provide data for this purpose. Four NEXRAD (Next Generation Weather Radar) sites operated by the National Weather Service cover the region. Since 2002 the District began to acquire NEXRAD data coverage. Corporate access of 15-minute, rain gauge-adjusted NEXRAD data, for each of the 2 x 2 km cells in the grid covering the District, was a major objective of the acquisition. The District has been receiving the NEXRAD data two types of datasets - near-real time (NRT) and end-of-month (EOM). District is using ArcIMS based application for NEXRAD data retrieval. The application provides varied spatially and temporally integrated datasets in tabular and image formats. This paper compares results of geo-spatial analysis of clusters of rain gauge and NEXRAD data. The data used in the analysis includes rain gauge data and the NEXRAD rainfall data that was collected during 1995-2005 at 2 x 2 km resolution. A set of clusters of rain gauges and a regular array of analysis blocks that were 20 x 20 km in size for the NEXRAD data were used to account for variability of the rainfall processes and local rainfall patterns. The spatial autocorrelations of the rain gauge and NEXRAD rainfall were identified using a semivariogram approach at daily timescale. The model fitting to the semivariograms were performed on data from 1998-2005. The range parameter of the semivariograms from rain gauge and NEXRAD rainfall data sets were compared.