Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
This addresses climate forecasting and ecosystem management challenges for researchers and practitioners, but it appears incremental as it summarizes a competition without introducing new methods.
This paper tackled the problem of forecasting sea surface temperature anomalies (SSTAs) three months in advance, with a specific task for nine months ahead in the Baltic Sea, using historical data from ERA5, and participants achieved results through various machine learning approaches, though no concrete numbers are provided in the abstract.
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.