Ocean forecasting is essential for effective and efficient REA operations.
Environmental observations are a necessity for initialization and updating of ocean forecasts.
Numerical ocean forecast capabilities in general consist of observational networks, data assimilation schemes and dynamic forecast models. Since observations are the most expensive part of the forecast and are often difficult to achieve, methods that would reduce the requirements are highly desirable. Knowledge of features, structures and the dynamics which evolves them is necessary for successful forecasting. Adaptive sampling of the observations of greatest impact increases efficiency and can drastically reduce the observational requirements, i.e. by one or two orders of magnitude. This project will develop methodology for ocean forecasting using minimum input.