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Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches
Publikationstyp
Journal Article
Date Issued
2008-08-20
Sprache
English
Author(s)
Journal
Volume
31
Issue
12
Start Page
1674
End Page
1686
Citation
Advances in Water Resources 31 (12): 1674-1686 (2008)
Publisher DOI
Scopus ID
Publisher
Elsevier
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.
Subjects
Bivariate mixed distribution
Copula
Non-parametric regression
Radar-rainfall
Uncertainty
DDC Class
551: Geology, Hydrology Meteorology