Systematic and random error components in satellite precipitation data sets

GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L09406, doi:10.1029/2012GL051592, 2012 Systematic and random error components in satellite precipitation data sets Amir AghaKouchak, 1 Ali Mehran, 1 Hamidreza Norouzi, 2 and Ali Behrangi 3 Received 7 March 2012; revised 13 April 2012; accepted 13 April 2012; published 11 May 2012. [ 1 ] 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. Citation: AghaKouchak, A., A. Mehran, H. Norouzi, and A. Behrangi (2012), Systematic and random error components in satellite precipitation data sets, Geophys. Res. Lett., 39, L09406, doi:10.1029/2012GL051592. 1. Introduction [ 2 ] Over the past three decades, development of satellite sensors have resulted in multiple sources of precipitation data sets. However, the quantification and understanding of uncertainties associated with remotely sensed satellite data remains a challenging research topic Bellerby and Sun [2005]. 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, verifi- cation 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 vali- dation and inter-comparison of satellite products by analyz- ing error characteristics of precipitation algorithms. In this Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, USA. Department of Construction Management and Civil Engineering Technology, City University of New York, New York City College of Technology, Brooklyn, New York, USA. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. paper, systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. Ideally, the systematic error is to be removed or minimized. In mea- surement theory, many algorithms have been developed to reduce systematic error with the aim of reducing the overall uncertainty Taylor [1999]. Evidently, understanding error properties including systematic and random components are fundamental for future improvements in precipitation retrieval algorithms, development of uncertainty models and bias adjustment techniques, and many other research studies and operational applications [Sorooshian et al., 2011]. 2. Data Resources [ 4 ] The following satellite precipitation data sets are used for error analysis: (a) The CPC MORPHing (CMORPH) [Joyce et al., 2004] algorithm; (b) The Precipitation Esti- mation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [Sorooshian et al., 2000]; (c) The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real-time (hereafter, 3b42-RT) [Huffman et al., 2007]. [ 5 ] The Stage IV radar-based gauge-adjusted precipita- tion data, available from the National Center for Environ- mental Prediction (NCEP), are used as the reference data set. 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 study area covers the entire conterminous United States (hereafter, CONUS). Three years of precipi- tation data (01/01/2005–12/31/2007) are used for the anal- ysis. Hereafter, the difference between satellite estimates and Stage IV observations is termed as precipitation error. 3. Methodology and Results [ 6 ] In this study, the Willmott decomposition technique is used for deriving the systematic and random components of error. Willmott [1981] suggested that the error in the numerical weather prediction models can be separated into systematic and random error components as: n A X n A X n ¼ i¼1 i¼1 Corresponding author: A. AghaKouchak, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA. (amir9a@gmail.com) Copyright 2012 by the American Geophysical Union. 0094-8276/12/2012GL051592 P sat A P ref P* sat A P ref n A X i¼1 P sat A P* sat n n where: P sat = satellite estimates P ref = reference measurements (here, Stage IV) L09406 1 of 4