Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information. (December 2020)

• Record Type: Journal Article
• Authors: Sadeghi, Mojtaba; Nguyen, Phu; Hsu, Kuolin; Sorooshian, Soroosh
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Infrared information -- Precipitation estimation -- Deep learning -- Convolutional neural networks; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104856
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This study investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products. Graphical abstract: Image 1 Highlights: Recent advances in the field of deep neural network (DNN) offer attractive opportunities to improve the precipitation retrieval algorithms. A CNN-based model—leveraging both geographical and IR information—shows a higher precipitation estimation accuracy than a model that only utilizes IR information. Applying an appropriate U-Net CNN architecture on geographical and IR information provides an opportunity to improve the current satellite-based precipitation products.