HR: 14:55h
AN: H23K-06    [Abstracts]
TI: Autocorrelation and Error Structure of Rainfall Derived from NEXRAD in Central and South Florida
AU: * Pathak, C S
EM: cpathak@sfwmd.gov
AF: South Florida Water Management District, 3301 Gun Club Road, West Palm Beach, FL 33406, United States
AU: Vieux, B E
EM: BV@vieuxinc.com
AF: Vieux and Associates, Inc., 350 David L. Boren Blvd., Suite 2500, Norman, OK 73072, United States
AB: Motivation for this study comes from the South Florida Water Management District (District) who is responsible for managing water resources in 16-counties over a 46,439-square kilometer (17,930 square-mile) area. Near-real- time rainfall data are used in operation of approximately 3,000 kilometers (~1,800 miles) of canals, 22 major pump stations and 200 water control structures. The spatial extent of the District extends from Orlando to Key West and from the Gulf Coast to the Atlantic Ocean and contains major water features including Lake Okeechobee and the Everglades wetlands. Rainfall is a key factor in the water management decisions made by the District in real-time and through studies that rely on archival rainfall data derived from radar and rain gauge observations. Rainfall measurements are obtained from a combination of four NEXRAD radars and a rain gauge network that comprises 280 active rain gauge stations located in the more populated areas. Four NEXRAD (Next Generation Weather Radar) sites operated by the National Weather Service cover the region. Rain gauges are used for frequency analysis and for adjustment of the radar rainfall products. An optimization study of the rain gauge network is accomplished by removing gauges in areas of excess coverage, and by adding or moving rain gauges to gain a more even spatial distribution over the District. Rainfall fields measured at daily and hourly timesteps exhibit autocorrelation which can affect the network design subject to optimality constraints. This presentation will describe the autocorrelation and error structure found in rainfall measurements derived from rain gage and NEXRAD data. The data used in the analysis includes rain gage data and the NEXRAD rainfall data that was collected during 1995-2005 at 2 x 2 km resolution. A set of clusters of rain gages 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 gage 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 spatial autocorrelations from rain gage and NEXRAD rainfall data sets were compared and evaluated.
DE: 1853 Precipitation-radar
SC: Hydrology [H]
MN: 2007 Fall Meeting