We plan to collaborate with Applied Ocean Sciences (AOS) to help designing and delivering a compact system to assess local uncertainties and track the evolution of the maritime environment around unmanned platforms at sea. Such a system can run both at control centers and on-board Underwater and Surface Unmanned Vehicles (UUV/SUV) under different network bandwidth constraints. The system uses the Navy ocean forecasts for initial environmental guesses and outlooks up to 2 weeks (or more in future generations) and then implements a Reduced Order Model (ROM) derived from Dynamically Orthogonal (DO) solutions to deliver a local uncertainty picture (for the next 24-48 hours). The ROM-DO solutions will target the variables and parameters of relevance for the UUV/SUV fleet missions planning and execution. These solutions are using a set of dynamic modes from which the reduced order estimates for the parameters and variables of interest are computed. They are then integrated with the local network data collected during the past days-hours, using a non-intrusive filter, and deliver an updated local forecast for the next 12-24 hour. The new fields are then used to compute marginal and conditional probability distributions of pre-loaded dynamical functions/modes that are sent to the forward deployed platforms. These probabilities are then integrated in dedicated payloads with the platform sensor data in real-time to locally reconstruct and update the most likely environments for the next 1-12 hours. This will assure best fits to the in-situ data and sensor performance observations and updates the forecasts around the platforms. These solutions can be used for path optimization and environmental adaptation/adaptive sampling, assuming operators and/or on-board middleware software can then specify a decision point for choosing the path based on mission parameters.