Evaluating the transferability potential of deep learning models for climate downscaling
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913434437681152 |
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| author | Prasad, Ayush Harder, Paula Yang, Qidong Sattegeri, Prasanna Szwarcman, Daniela Watson, Campbell Rolnick, David |
| author_facet | Prasad, Ayush Harder, Paula Yang, Qidong Sattegeri, Prasanna Szwarcman, Daniela Watson, Campbell Rolnick, David |
| contents | Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating the transferability potential of deep learning models for climate downscaling Prasad, Ayush Harder, Paula Yang, Qidong Sattegeri, Prasanna Szwarcman, Daniela Watson, Campbell Rolnick, David Machine Learning Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types. |
| title | Evaluating the transferability potential of deep learning models for climate downscaling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.12517 |