Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910779564883968 |
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| author | Ning, Ding Vetrova, Varvara Bryan, Karin R. Koh, Yun Sing Voskou, Andreas Kouagou, N'Dah Jean Sharma, Arnab |
| author_facet | Ning, Ding Vetrova, Varvara Bryan, Karin R. Koh, Yun Sing Voskou, Andreas Kouagou, N'Dah Jean Sharma, Arnab |
| contents | This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05731 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Ning, Ding Vetrova, Varvara Bryan, Karin R. Koh, Yun Sing Voskou, Andreas Kouagou, N'Dah Jean Sharma, Arnab Machine Learning Atmospheric and Oceanic Physics Applications This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling. |
| title | Diving Deep: Forecasting Sea Surface Temperatures and Anomalies |
| topic | Machine Learning Atmospheric and Oceanic Physics Applications |
| url | https://arxiv.org/abs/2501.05731 |