Empirically-based generator of synthetic radar-rainfall data

Gabriele Villarini, Witold Krajewski, Grzegorz Ciach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To fully characterize the uncertainties associated with radar-rainfall (RR) estimates, Ciach et al. (2007) developed an empirically based model, in which the relationship between true and radar-rainfall can be described by a deterministic distortion function and a random component. This model has the flexibility to account for different spatio-temporal resolutions, distances from the radar, synoptic conditions, and space-time dependence of the errors. Based on this model, two possible scenarios are presented and described: an ensemble generator and a static estimation of probability maps. In the former, given a time series of hourly radar-rainfall fields, a user can generate an ensemble of synthetic RR data congruent with the error model's characteristics. As far as the second scenario is concerned, given hourly RR maps, it is possible to generate fields with the probability of exceedence of some arbitary thresholds by the true rainfall.

Original languageEnglish (US)
Title of host publicationIAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management
Pages78-85
Number of pages8
Edition313
StatePublished - 2007
Externally publishedYes
EventInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG) - Perugia, Italy
Duration: Jul 2 2007Jul 13 2007

Publication series

NameIAHS-AISH Publication
Number313
ISSN (Print)0144-7815

Other

OtherInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Country/TerritoryItaly
CityPerugia
Period7/2/077/13/07

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

Keywords

  • Ensemble forecasting
  • NEXRAD
  • Radar hydrology
  • Radar-rainfall uncertainties

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