Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929540272488448 |
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| author | Liu, Qian Gong, Bing Zhuang, Xiaoran Zhong, Xiaohui Kang, Zhiming Li, Hao |
| author_facet | Liu, Qian Gong, Bing Zhuang, Xiaoran Zhong, Xiaohui Kang, Zhiming Li, Hao |
| contents | The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particularly regarding the substantial costs associated with processing extensive data and the limitations of computational resources. Inspired by the Neural Image Compression (NIC) task in computer vision, this study seeks to compress weather data to address these challenges and enhance the efficiency of downstream applications. Specifically, we propose a variational autoencoder (VAE) framework tailored for compressing high-resolution datasets, specifically the High Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) with a spatial resolution of 1 km. Our framework successfully reduced the storage size of 3 years of HRCLDAS data from 8.61 TB to just 204 GB, while preserving essential information. In addition, we demonstrated the utility of the compressed data through a downscaling task, where the model trained on the compressed dataset achieved accuracy comparable to that of the model trained on the original data. These results highlight the effectiveness and potential of the compressed data for future weather research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09109 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model Liu, Qian Gong, Bing Zhuang, Xiaoran Zhong, Xiaohui Kang, Zhiming Li, Hao Machine Learning Artificial Intelligence Image and Video Processing Atmospheric and Oceanic Physics The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particularly regarding the substantial costs associated with processing extensive data and the limitations of computational resources. Inspired by the Neural Image Compression (NIC) task in computer vision, this study seeks to compress weather data to address these challenges and enhance the efficiency of downstream applications. Specifically, we propose a variational autoencoder (VAE) framework tailored for compressing high-resolution datasets, specifically the High Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) with a spatial resolution of 1 km. Our framework successfully reduced the storage size of 3 years of HRCLDAS data from 8.61 TB to just 204 GB, while preserving essential information. In addition, we demonstrated the utility of the compressed data through a downscaling task, where the model trained on the compressed dataset achieved accuracy comparable to that of the model trained on the original data. These results highlight the effectiveness and potential of the compressed data for future weather research. |
| title | Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model |
| topic | Machine Learning Artificial Intelligence Image and Video Processing Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2410.09109 |