A geostatistical method for Texas NexRad data calibration

Bo Li, Marian Eriksson, Raghavan Srinivasan, Michael Sherman

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Pages (from-to)1-19
Number of pages19
Issue number1
StatePublished - Jan 1 2008
Externally publishedYes


  • Linear regression
  • NexRad data
  • Threshold
  • Variogram estimation
  • Varying-coefficient

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecological Modeling

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