arXiv:2605.15470v2 Announce Type: replace Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass. We apply Njord globally at 0.25{\deg} resolution and regionally to the Baltic Sea at 2 km resolution. To scale to these large ocean grids we introduce
The increasing availability of high-resolution ocean data and advancements in graph neural networks are enabling more sophisticated AI models for complex environmental systems.
Accurate, probabilistic ocean forecasting is critical for climate modeling, maritime operations, disaster preparedness, and resource management, impacting global economies and human safety.
The introduction of a probabilistic forecasting model replaces deterministic approaches, providing a more realistic assessment of uncertainties in ocean dynamics, which was previously a significant limitation.
- · Climate scientists
- · Shipping and logistics industry
- · Coastal regions
- · Renewable energy (offshore wind)
- · Traditional deterministic forecasting models
- · Industries relying on static ocean models
Improved accuracy in predicting extreme weather events and ocean currents.
Better operational planning for naval defense, shipping routes, and offshore infrastructure.
Enhanced understanding of climate change impacts on ocean ecosystems, potentially leading to more effective policy interventions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG