HR: 1400h
AN: H33D-02    [Abstracts]
TI: Evaluation of Radar-Rainfall Uncertainties by a Highly Dense Rain Gauge Network
AU: * Villarini, G
EM: gabriele-villarini@uiowa.edu
AF: The University of Iowa, 300 South Riverside Drive, Iowa City, IA 52242, United States
AU: Mandapaka, P V
EM: pmandapa@engineering.uiowa.edu
AF: The University of Iowa, 300 South Riverside Drive, Iowa City, IA 52242, United States
AU: Krajewski, W F
EM: witold-krajewski@uiowa.edu
AF: 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 radar-rainfall estimates. Numerous sources of these errors are due to parameter estimation, the observational system and measurement principle, and not fully understood physical processes. To describe these uncertainties, rain gauge data are usually considered as a good approximation of the true rainfall values. However, in the vast majority of the cases, the available networks are too sparse to accurately describe the true rainfall process, adding uncertainties to the radar-rain gauge comparison. The authors will use a large (seven years) dataset of rainfall measurements by a highly dense rain gauge network deployed during the HYdrological Radar EXperiment (HYREX) in the Brue catchment, south-west part of England. This network presents unprecedented opportunity for the investigation of radar-rainfall uncertainties. In addition to the length of the dataset, one unique characteristic resides in its configuration: on a regular grid with 2×2 km2 resolution, there are 20 pixels with one gauge, seven pixels with two gauges and two super-dense pixels with eight gauges. The radar-rainfall estimates for the same time period are from C-band weather radar located at approximately 40 km from the catchment. Focusing on the two very dense pixels, the authors describe the uncertainties in radar-rainfall estimates for different accumulation times (5-minute to daily), modeling the errors using an additive and a multiplicative model.
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 Joint Assembly