预测全球海表温度异常，助力气候与渔业管理
Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
- 基于ERA5数据，用机器学习预测3个月后海表温度异常
- 挑战赛中实现对北大西洋和地中海区域较高预测精度
- 适合关注气候建模与海洋生态管理的研究者
本文概述了在2024年欧洲机器学习与知识发现实践大会（ECML PKDD）上举办的「深潜：海表温度与异常值预测挑战赛」的成果。该挑战聚焦于全球海表温度（SST）的数据驱动可预测性，这是气候预测、生态系统管理、渔业管理及气候变化监测的关键因素。任务要求利用历史数据提前3个月预测海表温度异常（SSTA），并增设一项针对波罗的海提前9个月预测的特殊任务。参赛者采用多种机器学习方法，基于ERA5数据进行建模。本文讨论了所用方法、取得结果及经验教训，为气候相关预测建模的未来发展提供洞见。
原文摘要 · Abstract (English)
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.
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