Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ...
Regional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of usin...
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Online Access: | https://dx.doi.org/10.3929/ethz-b-000617206 http://hdl.handle.net/20.500.11850/617206 |
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ftdatacite:10.3929/ethz-b-000617206 2024-04-28T07:57:26+00:00 Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... van der Meer, Marijn de Roda Husman, Sophie Lhermitte, Stef 2023 application/pdf https://dx.doi.org/10.3929/ethz-b-000617206 http://hdl.handle.net/20.500.11850/617206 en eng ETH Zurich machine learning RCM-emulator GCM downscaling Antarctica article-journal Text ScholarlyArticle Journal Article 2023 ftdatacite https://doi.org/10.3929/ethz-b-000617206 2024-04-02T12:32:08Z Regional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of using a cost-efficient machine learning alternative to dynamical downscaling by using the example case study of emulating surface mass balance (SMB) over the Antarctic Peninsula. More specifically, we determine the impact of the training framework by comparing two training scenarios: (a) a perfect and (b) an imperfect model framework. In the perfect model framework, the RCM-emulator learns only the downscaling function; therefore, it was trained with upscaled RCM (UPRCM) features at GCM resolution. This emulator accurately reproduced SMB when evaluated on UPRCM, but its predictions on GCM data conserved RCM-GCM inconsistencies and led to underestimation. In the imperfect model framework, the RCM-emulator was trained ... : Journal of Advances in Modeling Earth Systems, 15 (6) ... Article in Journal/Newspaper Antarc* Antarctic Antarctic Peninsula Antarctica DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
machine learning RCM-emulator GCM downscaling Antarctica |
spellingShingle |
machine learning RCM-emulator GCM downscaling Antarctica van der Meer, Marijn de Roda Husman, Sophie Lhermitte, Stef Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
topic_facet |
machine learning RCM-emulator GCM downscaling Antarctica |
description |
Regional climate models (RCMs) have a high computational cost due to their higher spatial resolution compared to global climate models (GCMs). Therefore, various downscaling approaches have been developed as a surrogate for the dynamical downscaling of GCMs. This study assesses the potential of using a cost-efficient machine learning alternative to dynamical downscaling by using the example case study of emulating surface mass balance (SMB) over the Antarctic Peninsula. More specifically, we determine the impact of the training framework by comparing two training scenarios: (a) a perfect and (b) an imperfect model framework. In the perfect model framework, the RCM-emulator learns only the downscaling function; therefore, it was trained with upscaled RCM (UPRCM) features at GCM resolution. This emulator accurately reproduced SMB when evaluated on UPRCM, but its predictions on GCM data conserved RCM-GCM inconsistencies and led to underestimation. In the imperfect model framework, the RCM-emulator was trained ... : Journal of Advances in Modeling Earth Systems, 15 (6) ... |
format |
Article in Journal/Newspaper |
author |
van der Meer, Marijn de Roda Husman, Sophie Lhermitte, Stef |
author_facet |
van der Meer, Marijn de Roda Husman, Sophie Lhermitte, Stef |
author_sort |
van der Meer, Marijn |
title |
Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
title_short |
Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
title_full |
Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
title_fullStr |
Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
title_full_unstemmed |
Deep Learning Regional Climate Model Emulators: A Comparison of Two Downscaling Training Frameworks ... |
title_sort |
deep learning regional climate model emulators: a comparison of two downscaling training frameworks ... |
publisher |
ETH Zurich |
publishDate |
2023 |
url |
https://dx.doi.org/10.3929/ethz-b-000617206 http://hdl.handle.net/20.500.11850/617206 |
genre |
Antarc* Antarctic Antarctic Peninsula Antarctica |
genre_facet |
Antarc* Antarctic Antarctic Peninsula Antarctica |
op_doi |
https://doi.org/10.3929/ethz-b-000617206 |
_version_ |
1797588616029929472 |