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A geostatistical method for Texas NexRad data calibration
Author(s) -
Li Bo,
Eriksson Marian,
Srinivasan Raghavan,
Sherman Michael
Publication year - 2008
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.848
Subject(s) - rain gauge , radar , environmental science , calibration , meteorology , weather radar , remote sensing , kriging , spatial variability , computer science , statistics , mathematics , geography , telecommunications
Rainfall is one of the most important hydrologic model inputs and is recognized as a random process in time and space. Rain gauges generally provide good quality data, however they are usually too sparse to capture the spatial variability. Radar estimates provide a better spatial representation of rainfall patterns, but they are subject to substantial biases. Our calibration of radar estimates using gauge data takes season, rainfall type, and rainfall amount into account, and is accomplished via a combination of threshold estimation, bias reduction, regression techniques, and geostatistical procedures. We explore the varying‐coefficient model to adapt to the temporal variability of rainfall. The methods are illustrated using Texas rainfall data in 2003, which includes Weather Surveillance Radar‐1988 Doppler (WSR‐88D) radar‐reflectivity data and the corresponding rain gauge measurements. Simulation experiments are carried out to evaluate the accuracy of our methodology. Copyright © 2007 John Wiley & Sons, Ltd.