SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910657095401472 |
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| author | Xu, Jingyi Luo, Yeqi Yang, Weidong Liu, Keyi Wang, Shengnan Fei, Ben Bai, Lei |
| author_facet | Xu, Jingyi Luo, Yeqi Yang, Weidong Liu, Keyi Wang, Shengnan Fei, Ben Bai, Lei |
| contents | Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have emerged and showcased superior performance over physics-based dynamical models. However, previous methods forecast SIC at a fixed temporal granularity, e.g. sub-seasonal or seasonal, thus only leveraging inter-granularity information and overlooking the plentiful inter-granularity correlations. SIC at various temporal granularities exhibits cumulative effects and are naturally consistent, with short-term fluctuations potentially impacting long-term trends and long-term trends provides effective hints for facilitating short-term forecasts in Arctic sea ice. Therefore, in this study, we propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data and provide a unified perspective for modeling SIC via our Sea Ice Foundation Model. SIFM is delicately designed to leverage both intra-granularity and inter-granularity information for capturing granularity-consistent representations that promote forecasting skills. Our extensive experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting Xu, Jingyi Luo, Yeqi Yang, Weidong Liu, Keyi Wang, Shengnan Fei, Ben Bai, Lei Machine Learning Atmospheric and Oceanic Physics Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have emerged and showcased superior performance over physics-based dynamical models. However, previous methods forecast SIC at a fixed temporal granularity, e.g. sub-seasonal or seasonal, thus only leveraging inter-granularity information and overlooking the plentiful inter-granularity correlations. SIC at various temporal granularities exhibits cumulative effects and are naturally consistent, with short-term fluctuations potentially impacting long-term trends and long-term trends provides effective hints for facilitating short-term forecasts in Arctic sea ice. Therefore, in this study, we propose to cultivate temporal multi-granularity that naturally derived from Arctic sea ice reanalysis data and provide a unified perspective for modeling SIC via our Sea Ice Foundation Model. SIFM is delicately designed to leverage both intra-granularity and inter-granularity information for capturing granularity-consistent representations that promote forecasting skills. Our extensive experiments show that SIFM outperforms off-the-shelf deep learning models for their specific temporal granularity. |
| title | SIFM: A Foundation Model for Multi-granularity Arctic Sea Ice Forecasting |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2410.14732 |