In recent years, the Gulf of Mexico Loop Current System has received increased attention. Its dynamics and the warm water it transports from the Caribbean influence the local weather and ecosystems. The high velocities of the Loop Current and the eddies it sheds can disrupt important industries. Accurate forecasting of the Loop Current system is challenging, in part because of the lack of data over long enough periods of time, which leads to considerable uncertainty. In this work, we describe and apply our MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) and Error Subspace Statistical Estimation (ESSE) ensemble forecasting methodology and software to estimate such uncertainty and to inform data collection in a quantitative manner. The ensemble forecasts allow for mitigating risks and optimizing data collection. We demonstrate that our probabilistic system has qualitative skill for over a month. We show that uncertainty grows along and around the Loop Current and its eddies, and transfers to depth from the shelf and slope. Using information theory, we find that our probabilistic hindcasts can have predictive capabilities for one to three months, with a slower loss of predictability in the quieter Loop Current states. Through the use of correlation and mutual information fields, we optimize future sampling by predicting the impacts and information content of observations. We find that the most informative data are those that either directly sample dynamically relevant areas or sample coastal modes that are correlated with these areas. Subsurface data are shown to have more impact on forecasts of one month or longer.

The multi-scale dynamics of oceanic processes and the complex propagation of acoustic waves are fundamental challenges in marine sciences and operations. Recent computing advances enable such multiresolution ocean and acoustic modeling, but a fully integrated system for sustained coupled predictions and Bayesian data assimilation remains needed. In this study, we integrate the MSEAS Primitive Equation (PE) ocean modeling system and the MSEAS acoustic Parabolic Equation (ParEq) solver, enabling real-time coupled ocean and acoustic predictions. Realistic applications in Massachusetts Bay, the Norwegian Sea, the western Mediterranean Sea, and the New York Bight are used to demonstrate capabilities and validate predictions in diverse shallow and deep-water environments. Results provide the foundation for an end-to-end system for coupled ocean-acoustic probabilistic modeling, Bayesian inversion, and learning.

Cost-effective seafloor mapping at high resolution is yet to be attained. A possible solution consists of using a mobile, wide-aperture, sparse array with subarrays distributed across multiple autonomous surface vessels. Such wide-area mapping with multiple dynamic sources and receivers require accurate modeling and processing systems for imaging the seabed. In this paper, we focus on computational schemes and challenges for such high-resolution acoustic imaging or migration. Starting from the imaging condition from the adjoint-state method, we derive a closed-form expression for Gaussian beam migration in stratified media. We employ this technique on simulated data and on real data collected with our novel acoustic array over shipwrecks in the Boston Harbor. We compare Gaussian beam migration with diffraction stack and Kirchhoff migration, and we find that Gaussian beam migration produces the clearest images with the fewest artifacts.

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications.