Version 1
: Received: 14 July 2022 / Approved: 25 July 2022 / Online: 25 July 2022 (07:59:59 CEST)
Version 2
: Received: 21 December 2023 / Approved: 21 December 2023 / Online: 22 December 2023 (03:50:22 CET)
How to cite:
Chaudhuri, C.; Robertson, C. MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble. Preprints2022, 2022070356. https://doi.org/10.20944/preprints202207.0356.v2
Chaudhuri, C.; Robertson, C. MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble. Preprints 2022, 2022070356. https://doi.org/10.20944/preprints202207.0356.v2
Chaudhuri, C.; Robertson, C. MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble. Preprints2022, 2022070356. https://doi.org/10.20944/preprints202207.0356.v2
APA Style
Chaudhuri, C., & Robertson, C. (2023). MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble. Preprints. https://doi.org/10.20944/preprints202207.0356.v2
Chicago/Turabian Style
Chaudhuri, C. and Colin Robertson. 2023 "MSCliGAN—A Structure-informed Generative Adversarial Model for Multi-site Statistical Downscaling of Extreme Precipitation Using Multi-model Ensemble" Preprints. https://doi.org/10.20944/preprints202207.0356.v2
Abstract
Although the statistical methods of downscaling climate data have progressed significantly, the development of high-resolution precipitation products continues to be a challenge. This is especially true when interest centres on downscaling value over several study sites. In this paper , we report a new downscaling method termed the multi-site Climate Generative Adversarial Network (MSCliGAN), which can simulate annual maximum precipitation to the regional scale during the 1950-2010 period in different cities in Canada by using different AOGCM's from the Coupled Model Inter-Comparison Project 6 (CMIP6) as input. Auxiliary information provided to the downscaling model included topography and land-cover. The downscaling framework uses a convolution encoder-decoder U-net network to create a generative network and a convolution encoder network to create a critic network. An adversarial training strategy is used to train the model. The critic/discriminator used Wasserstein distance as a loss measure and on the other hand the generator is optimized using a summation of content loss on Nash-Shutcliff Model Efficiency (NS), structural loss on structural similarity index (SSIM), and adversarial loss Wasserstein distance. Downscaling results show that downscaling AOGCMs by incorporating topography and land-use/land-cover can produce spatially coherent fields close to observation over multiple-sites. We believe the model has sufficient downscaling potential in data sparse regions where climate change information is often urgently needed.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
