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CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles

Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
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Water 2020, 12(12), 3353; https://doi.org/10.3390/w12123353
Received: 22 October 2020 / Revised: 16 November 2020 / Accepted: 25 November 2020 / Published: 30 November 2020
(This article belongs to the Section Hydrology)
Despite numerous studies in statistical downscaling methodologies, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products. View Full-Text
Keywords: statistical downscaling; generative adversarial network; combination of errors; convolutional neural network; multi-scale structural similarity index; Wasserstein GAN statistical downscaling; generative adversarial network; combination of errors; convolutional neural network; multi-scale structural similarity index; Wasserstein GAN
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MDPI and ACS Style

Chaudhuri, C.; Robertson, C. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. Water 2020, 12, 3353. https://doi.org/10.3390/w12123353

AMA Style

Chaudhuri C, Robertson C. CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles. Water. 2020; 12(12):3353. https://doi.org/10.3390/w12123353

Chicago/Turabian Style

Chaudhuri, Chiranjib, and Colin Robertson. 2020. "CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles" Water 12, no. 12: 3353. https://doi.org/10.3390/w12123353

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