Remotely sensed precipitation products, due to their large areal coverage and high resolution, have been widely used to provide information on the spatiotemporal structure of rainfall. However, it is well known that these precipitation products also suffer from large uncertainties that originate from various sources. In this study, we selected radar-rainfall (RR) data corresponding to 10 warm season events over a 256 x 256 km2 domain with a data resolution of 4 x 4 km2 in space and 1 h in time. We characterized their spatial structure using correlation function, power spectrum, and moment scaling function. We then employed a recently developed RR error model and rainfall generator to obtain an ensemble of probable rainfall fields that are consistent with the RR estimation error structure. We parameterized the spatial correlation functions with a two-parameter power exponential function, the Fourier spectra with a power law function, and the moment scaling functions with the universal multifractal model. The parameters estimated from the ensemble were compared with those obtained from the RR products to quantify the impact of radar-rainfall estimation errors on the spatial characterization of rainfall events. From the spatial correlation and power spectrum analyses, we observed that RR estimation uncertainties introduce spurious correlations with greater impact for the smaller scales. The RR errors also significantly bias the estimation of the moment scaling functions. © 2010 by the American Geophysical Union.


Civil, Architectural and Environmental Engineering

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Publication Date

01 Jan 2010