HR: 0800h
AN: H21E-0803    [Abstracts]
TI: Sensitivity Studies of the Radar-Rainfall Error Models
AU: * Villarini, G
EM: gabriele-villarini@uiowa.edu
AF: IIHR-Hydroscience & Engineering, The University of Iowa, 300 South Riverside Drive, Iowa City, IA 52242, United States
AU: Krajewski, W F
EM: witold-krajewski@uiowa.edu
AF: IIHR-Hydroscience & Engineering, The University of Iowa, 300 South Riverside Drive, Iowa City, IA 52242, United States
AU: Ciach, G J
EM: gciach@engineering.uiowa.edu
AF: IIHR-Hydroscience & Engineering, The University of Iowa, 300 South Riverside Drive, Iowa City, IA 52242, United States
AB: It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of WSR-88D radars. These errors are due to the measurement principles, parameter estimation, and not fully understood physical processes. Comprehensive quantitative evaluation of these uncertainties is still at an early stage. The authors proposed an empirically-based model in which the relation between true rainfall (RA) and radar-rainfall (RR) could be described as the product of a deterministic distortion function and a random component. However, how different values of the parameters in the radar-rainfall algorithms used to create these products impact the model results still remains an open question. In this study, the authors investigate the effects of different exponents in the Z-R relation (Marshall- Palmer, NEXRAD, and tropical) and of an anomalous propagation (AP) removal algorithm. Additionally, they generalize the model to describe the radar-rainfall uncertainties in the additive form. This approach is fully empirically based and rain gauge estimates are considered as an approximation of the true rainfall. The proposed results are based on a large sample (six years) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet, and the Agricultural Research Service Micronet.
DE: 1840 Hydrometeorology
DE: 1853 Precipitation-radar
DE: 1854 Precipitation (3354)
DE: 1855 Remote sensing (1640)
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting