Abstract
This study contributes to characterization of satellite precipitation error which is fundamental to develop uncertainty models and bias reduction algorithms. Systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. The analyses show that the spatial distribution of systematic error has similar patterns for all precipitation products. However, the systematic (random) error of daily accumulations is significantly less (more) than that of high resolution 3‐hr data. One should note that the systematic biases of satellite precipitation are distinctively different in the summer and winter. The systematic (random) error is remarkably higher (lower) during the winter. Furthermore, the systematic error seems to be proportional to the rain rate magnitude. The findings of this study highlight that bias removal methods should take into account the spatiotemporal characteristics of error as well as the proportionality of error to the magnitude of rain rate.
Highlights
[2] Over the past three decades, development of satellite sensors have resulted in multiple sources of precipitation data sets
[10] The spatial coverage of current in-situ and groundbased precipitation measurement networks is inadequate for monitoring precipitation globally
Remote sensing of precipitation has emerged as a major source of information
Summary
[2] Over the past three decades, development of satellite sensors have resulted in multiple sources of precipitation data sets. The uncertainties of satellite precipitation data arise from different factors including the sensor itself, retrieval error, and spatial and temporal sampling, among others [e.g., Hong et al, 2006]. [3] Numerous studies have addressed validation, verification and uncertainty of satellite precipitation estimates against ground-based measurements [e.g., Turk et al, 2008; Ebert et al, 2007]. This study aims to go beyond the validation and inter-comparison of satellite products by analyzing error characteristics of precipitation algorithms. The Stage IV data include merged operational radar data and rain gauge measurements in hourly accumulations and 4 km grids. The Stage IV observations are accumulated to 3-hourly and aggregated onto 0.25 grids to match with satellite data. The difference between satellite estimates and Stage IV observations is termed as precipitation error
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