TY - JOUR
T1 - Modeling radar-rainfall estimation uncertainties using parametric and non-parametric approaches
AU - Villarini, Gabriele
AU - Serinaldi, Francesco
AU - Krajewski, Witold F.
N1 - Funding Information:
The first author was supported by NASA Headquarters under the Earth Science Fellowship Grant NNX06AF23H. The second author was supported by the H 2 CU – Honors Center of Italian Universities, “Sapienza” University of Rome. The third author acknowledges support of the NASA Grant NNX07AD65G and the Rose & Joseph Summers Endowment. The data used were supplied by the British Atmospheric Data Centre from the NERC Hydrological Radar Experiment Dataset ( http://www.badc.rl.ac.uk/data/hyrex/ ).
PY - 2008/12
Y1 - 2008/12
N2 - 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.
AB - 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.
KW - Bivariate mixed distribution
KW - Copula
KW - Non-parametric regression
KW - Radar-rainfall
KW - Uncertainty
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U2 - 10.1016/j.advwatres.2008.08.002
DO - 10.1016/j.advwatres.2008.08.002
M3 - Article
AN - SCOPUS:55649123897
SN - 0309-1708
VL - 31
SP - 1674
EP - 1686
JO - Advances in Water Resources
JF - Advances in Water Resources
IS - 12
ER -