Corrections to Radar-Estimated Precipitation Using Observed Rain Gauge Data

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A new method is presented for calculating daily rainfall amounts from radar. Radar data from two River Forecast Centers (RFC), and daily rain gauge data from stations around the Northeast U.S. are used to create a radar-level resolution grid of rainfall. The purpose for this method is to produce fields of precipitation estimates in the operational area of the Northeast Regional Climate Center (NRCC), to archive the high-resolution precipitation product, and to use the product as input into a crop modeling program. Considering rain gauge observations as the true values, radar errors are calculated at each rain gauge location every day. Using an interpolation method, the errors are estimated at each radar pixel and added back to the radar grid. Thirty cases were selected from different times of year and different weather types. Three interpolation methods, Inverse Distance Weighting, Multiquadric Interpolation, and Ordinary Kriging, are compared to the Multisensor Precipitation Estimation (MPE), used operationally by River Forecast Centers. Parameters associated with each interpolation method are adjusted daily using cross-validation to produce the best results for each case. By using daily rain gauge data, all three interpolation methods perform similarly and better than MPE, which uses hourly rain gauge data. All methods for estimating precipitation perform best at low values of precipitation and worst at high values of precipitation. Because of the similarity in results between interpolation methods, the simplest method computationally, Inverse Distance Weighting, has been chosen to be used operationally for the Northeast Regional Climate Center.

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Chair: Dr. Daniel S. Wilks; Committee: Dr. Arthur T. DeGaetano, Dr. Patrick J. Sullivan


Cornell Initiative on Computational Agriculture, funded by a Special Grant of the USDA-CSREES

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Radar; Precipitation; Rain Gauge


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dissertation or thesis

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