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Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

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Abstract

Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.

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Acknowledgments

The authors would like to thank MWEA and the General Authority of Meteorology and Environmental Protection (GAMEP) for providing the rain gauges data.

Funding

Partial financial support was made available from King Saudi University (KSU), Saudi Arabia Culture Mission (SACM), Ministry of Environment, Water and Agriculture (MEWA) of Saudi Arabia, the National Science Foundation Cyber-Enabled Sustainability Science and Engineering program (CCF-1331915), and the NASA Minority University Research and Education Project (MIRO NNX15AQ06A).

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Authors and Affiliations

  1. Department of Civil Engineering, King Saud University, Riyadh, 12372, Saudi Arabia

    Raied Alharbi

  2. Department of Civil and Environmental Engineering, University of California, Irvine E-4130 Engineering Gateway, Irvine, CA, 92697-2175, USA

    Raied Alharbi, Kuolin Hsu & Soroosh Sorooshian

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  1. Raied Alharbi
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Correspondence to Raied Alharbi.

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Alharbi, R., Hsu, K. & Sorooshian, S. Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia. Arab J Geosci 11, 508 (2018). https://doi.org/10.1007/s12517-018-3860-4

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