The first steps towards integrating autonomous monitoring, probabilistic forecasting, reachability analysis, and adaptive sampling for the Gulf of Mexico were demonstrated in real-time during the collaborative Mini-Adaptive Sampling Test Run (MASTR) ocean experiment, which took place from February to April 2024. The emphasis of this contribution is on the use of the MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) including Error Subspace Statistical Estimation (ESSE) large-ensemble forecasting and path planning systems to predict ocean fields and uncertainties, forecast reachable sets and optimal paths for gliders, and guide sampling aircraft and ocean vehicles toward the most informative observations. Deterministic and probabilistic ocean forecasts are exemplified and linked to the variability of the Loop Current (LC) and LC Eddies, demonstrating predictive skill by real-time comparisons to independent data. Risk forecasts in terms of probabilities of currents exceeding 1.5 kt were provided. The most informative sampling patterns for Remote Ocean Current Imaging System (ROCIS) flights were forecast using mutual information between surface currents and density anomaly. Finally, we guided four underwater gliders using probabilistic reachability and path-planning forecasts.

Underwater sound propagation is sensitive to specific environmental features and specific operational configuration parameters. We illustrate the preliminary use of our deterministic and stochastic Dynamically-Orthogonal Wide-Angle Parabolic Equations (DO-WAPEs) to classify and quantify the effects of ocean uncertainties and source depth uncertainties on the acoustic fields. We showcase initial results for the New England Seamounts off the northeastern US coastline, emphasizing the effects of uncertain source depths and subsurface ocean inflows and acoustic ducts. The stochastic DO-WAPEs predict the probability distribution of the acoustic pressure and transmission loss fields. The mean and standard deviation of the TL field are described and linked to the ocean environment and seamount geometry. Mutual information is predicted to identify the TL locations most informative about the source depth.

This report describes the data set from the CALYPSO 2022 Campaign and the processing and quality-control steps that were taken in producing the data set. The CALYPSO Campaign was conducted from both the R/V Pourquoi Pas? from February 17-March 12, 2022 and the R/V Pelagia from February 20-March 16.

This report summarizes activities on the French research vessel Pourquoi Pas? operating as part of the Office of Naval Research CALYPSO project during its main 2022 field program in the northwest Mediterranean. Other components of this field program included the Netherland’s research vessel R.V. Pelagia, a fleet of underwater gliders operated by the Scripps Oceanographic Institute and the Balearic Islands Coastal Observing and Forecasting System (SOCIB).

Abstract: Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging: be it predicting if it will rain, knowing which direction a hurricane will go, understanding the implications of melting Arctic ice, or detecting the impacts of humans on the earth’s surface. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery: allowing scientists to ask “why?” but now with the power of machine learning.

Biography: Dr. Elizabeth (Libby) Barnes is a Professor of Atmospheric Science at Colorado State University. Libby’s research has centered on climate variability, predictability, and change and the data analysis tools used to better understand the Earth system (including machine learning and causal discovery). She teaches graduate courses on statistical analysis methods, machine learning for Earth scientists, and data-driven forecasting from days-to-decades. See Barnes Group.