Observing the ocean is vital for met-ocean forecasting, supplying essential insights for understanding the ocean’s current state and predicting future patterns. Modern forecasting systems rely on numerical models of ocean dynamics and data-assimilation techniques that incorporate real-time observations. Key parameters like temperature, salinity, currents, and atmospheric conditions significantly enhance model accuracy by connecting model simulations with actual ocean conditions.
A wide network of research institutions, governmental bodies, and private organizations contributes to these observations, using a range of sensors deployed on research vessels, submarines, aircraft, moorings, drifting buoys, gliders, floats, fixed platforms, and satellites. These sensors capture data across physical, chemical, biological, geological, and geophysical domains, forming a comprehensive basis for informed ocean management.
Near Real-Time (NRT) data, updated hourly to weekly, supports immediate forecasting needs, while Delayed Mode (DM) data serves reanalysis, climate monitoring, and seasonal forecasting. To ensure quality, NRT data typically undergoes automated control procedures, while DM data benefits from offline expert review and rigorous quality checks, ensuring the data’s robustness for longer-term scientific studies and climate assessments.
In Europe, two principal programs drive ocean data observation and dissemination: the Copernicus Marine Service and the European Marine Observation and Data Network (EMODnet). Together with the Marine In Situ Collaboration (MIC)—a partnership among EMODnet, CMEMS, and EuroGOOS—these initiatives coordinate ocean data collection, quality control, and accessibility, directly supporting the goals of ocean prediction and action. This collaboration exemplifies vibrant science in action, equipping decision-makers with critical insights to guide sustainable ocean stewardship.