Abstract
In this paper, we use our MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) including Error Subspace Statistical Estimation (ESSE) largeensemble forecasting to provide real-time probabilistic forecasts for the Gulf of Mexico during the collaborative GRand Adaptive Sampling Experiment (GRASE) from April to September 2025. These forecasts are used for optimal planning and adaptive sampling for multiple platforms deployed during the experiment. We highlight real-time forecasts for probabilistic glider reachability and optimal planning. We showcase mutual information forecasts for optimal adaptive sampling with gliders and floats, maximizing information about the Loop Current (LC) and its eddies (LCEs). We showcase reachability and flow map forecasts for floats, characterizing water mass transports and eddy filamentations. We present probabilistic LCE forecasts using clustering techniques. Finally, we guide two gliders to recovery points using reachability and heading forecasts.