Correlation between weather radar reflectivity and precipitation data obtained from free Internet resources

Yuri Gorokhovich; Gabriele Villarini

doi:10.1117/12.511184

24 February 2004 Correlation between weather radar reflectivity and precipitation data obtained from free Internet resources

Yuri Gorokhovich, Gabriele Villarini

Author Affiliations +

Proceedings Volume 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V; (2004) https://doi.org/10.1117/12.511184
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

Correlation between weather radar reflectivity and precipitation data collected by rain gauges allows obtaining empirical formula that can be used to create continuous surface of the rainfall. This surface can be used in distributed hydrologic modeling and early warning system in flood management. In this study rain data from multiple weather stations were correlated with reflectivity values from the radar covering the area for each time interval of the selected rain event. Internet sources provided real-time precipitation data and images of weather radars for the continental United States, collected by United States Geological Survey (USGS) and National Oceanographic and Aeronautic Agency (NOAA). Database of 82 radar stations and more than 1500 rain gauges for the Continental part of USA was compiled and used for continuous downloading of radar images and rain data. Image sequences corresponding to rain events were extracted for two randomly selected radar stations in South and North Carolina. Rainfall data from multiple gauges under the radar zone (120 miles) were extracted and combined with corresponding reflectivity values for each time interval of the selected rain event. Results of regression analysis showed significant correlation between rain gauge data and radar reflectivity values and allowed derivation of empirical formula.

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Yuri Gorokhovich and Gabriele Villarini "Correlation between weather radar reflectivity and precipitation data obtained from free Internet resources", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.511184

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