TY - GEN
T1 - Empirically-based generator of synthetic radar-rainfall data
AU - Villarini, Gabriele
AU - Krajewski, Witold
AU - Ciach, Grzegorz
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Ensemble forecasting
KW - NEXRAD
KW - Radar hydrology
KW - Radar-rainfall uncertainties
UR - http://www.scopus.com/inward/record.url?scp=55749105891&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55749105891&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:55749105891
SN - 9781901502091
T3 - IAHS-AISH Publication
SP - 78
EP - 85
BT - IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management
T2 - International Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Y2 - 2 July 2007 through 13 July 2007
ER -