Abstract
[1] In this study, we investigate the fundamental open question facing the satellite rainfall data community today - If “error” is defined on the basis of independent ground validation (GV) rainfall data, how are these error metrics estimated for a satellite rainfall data product without the need for much extensive GV data? Using a six-year database of high resolution (0.25 degree and 3 hourly) satellite rainfall data over the United States and an optimal spatial interpolation method (ordinary kriging), we demonstrate that most error metrics (such as bias and probability of detection) are amenable for ‘transfer’ from gaged to ungaged locations than others. Our findings also indicate that a continuously-calibrated and regionalized error transfer scheme is technically feasible within the neighborhood of a gaged region if more research is carried out on the role played by different interpolation methods and the temporal structure of error.
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