Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches

Gabriele Villarini, Francesco Serinaldi, Witold F. Krajewski

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


There are large uncertainties associated with radar estimates of rainfall, including systematic errors as well as the random effects from several sources. This study focuses on the modeling of the systematic error component, which can be described mathematically in terms of a conditional expectation function. The authors present two different approaches: non-parametric (kernel-based) and parametric (copula-based). A large sample (more than six years) of rain gauge measurements from a dense network located in south-west England is used as an approximation of the true ground rainfall. These data are complemented with rainfall estimates by a C-band weather radar located at Wardon Hill, which is about 40 km from the catchment. The authors compare the results obtained using the parametric and non-parametric schemes for four temporal scales of hydrologic interest (5 and 15 min, hourly and three-hourly) by means of several different performance indices and discuss the strengths and weaknesses of each approach.

Original languageEnglish (US)
Pages (from-to)1674-1686
Number of pages13
JournalAdvances in Water Resources
Issue number12
StatePublished - Dec 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology


  • Bivariate mixed distribution
  • Copula
  • Non-parametric regression
  • Radar-rainfall
  • Uncertainty


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