In a development that could reshape the maritime industry, researchers from the Met Office and the University of Exeter have unveiled an artificial intelligence (AI) model capable of forecasting ocean currents in the Gulf of Mexico using nothing more than a laptop. This breakthrough, rooted in the Machine Learning for Low-Cost Offshore Modelling (MaLCOM) framework, has already made waves in the marine science community, earning the American Society of Civil Engineers (ASCE) Offshore Technology Conference (OTC) Best Paper Award 2025. The implications for offshore energy, search and rescue, and defence operations are substantial, promising to make accurate ocean forecasts more accessible than ever before.
The MaLCOM framework initially set its sights on predicting regional ocean waves around the UK. However, its latest iteration has been adapted to forecast ocean currents in the Gulf of Mexico, a region critical for maritime industries and notorious for its extreme weather conditions. The model’s standout features include its low computational requirements, reliance on sparse observational data, adaptability to new regions and ocean variables, and a transparent architecture that offers clear insights into spatial and temporal dynamics.
Dr. Edward Steele, Met Office IT Fellow for Data Science and lead author of the award-winning paper, emphasized the potential of AI-based forecasting. “AI-based forecasting could revolutionise ocean prediction,” he said. “We show the exciting potential of very low-cost, observations-driven, AI-based models, even when constrained by limited data.” This sentiment was echoed by Dr. Ajit Pillai, University of Exeter Senior Lecturer and Royal Academy of Engineering Research Fellow, who highlighted the model’s flexibility. “This is an exciting application of the MaLCOM framework to new parameters and new geographical regions, providing new decision-making capability to help offshore safety and workability.”
Unlike traditional physics-based ocean models, which demand massive computational power and extensive sensor networks, MaLCOM leverages machine learning to glean insights from hyper-sparse data. It delivers impressive accuracy for short-range forecast horizons—up to 12 hours ahead—especially under non-extreme conditions. This evolution in forecasting brings several immediate advantages: affordability, speed, and scalability. The model’s effectiveness was benchmarked against the Met Office’s operational physics-based systems, showing comparable performance in everyday conditions. It’s a significant step toward democratizing access to oceanographic intelligence.
The MaLCOM framework originated as a University of Exeter research project nearly five years ago, with strong participation from the Met Office. The ongoing success of the project reflects the value of cross-sectoral partnerships in emerging technologies. Dr. Steele highlighted the importance of these collaborations: “This is an example of the benefits of academic, government, and industry organisations working together. In rapidly-evolving fields such as machine learning, partnerships are essential to realizing the ambition and vision for AI in weather and climate science.”
Looking ahead, the research team plans to refine and scale the MaLCOM framework further. Future updates may include enhanced capabilities for extreme weather scenarios, regional customization for tropical basins and Arctic waters, integration with satellite-based and drone-collected data, and use in training autonomous marine systems. With growing demand for climate-smart infrastructure, resilient maritime logistics, and sustainable offshore energy, MaLCOM could become a cornerstone technology in the digital marine era.
The ASCE Offshore Technology Conference Best Paper Award 2025 not only affirms the research’s technical merits but also celebrates its practical relevance and forward-thinking design. “It is always exciting for research to deliver real impact,” said Dr. Pillai. “The recognition of the team’s work reflects our progress to date—these are exciting times ahead.”
As the world seeks low-cost, data-smart tools to tackle complex environmental challenges, innovations like MaLCOM point the way forward. This development could spark a wave of similar initiatives, encouraging other researchers and organizations to explore the potential of AI in marine forecasting. The maritime industry, in particular, stands to benefit from more accessible and accurate ocean predictions, which could enhance safety, efficiency, and sustainability across various operations.
Moreover, the success of MaLCOM underscores the importance of interdisciplinary collaboration. By bringing together academia, government, and industry, the research team has demonstrated the power of collective effort in driving technological advancements. This could inspire more partnerships across different sectors, fostering innovation and accelerating progress in the maritime industry and beyond.
In the long run, the MaLCOM framework could pave the way for more sophisticated and accessible AI-driven tools in marine science. As the technology evolves, it may become possible to integrate these models with other data sources, such as satellite imagery and underwater sensors, to provide even more comprehensive and accurate forecasts. This could open up new possibilities for applications in areas like marine conservation, coastal management, and renewable energy.
Furthermore, the low-cost nature of the MaLCOM framework makes it an attractive option for developing regions and smaller agencies that may lack the