Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

Md Abul Ehsan Bhuiyan, Feifei Yang, Nishan Kumar Biswas, Saiful Haque Rahat and Tahneen Jahan Neelam
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Md Abul Ehsan Bhuiyan: Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269-3088, USA
Feifei Yang: Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269-3088, USA
Nishan Kumar Biswas: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
Saiful Haque Rahat: Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH 45220, USA
Tahneen Jahan Neelam: Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA

Forecasting, 2020, vol. 2, issue 3, 1-19

Abstract: The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications.

Keywords: IMERG; SMAP; nonparametric; machine learning; neural network; random forest (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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