Real-time Ocean Probabilistic Forecasts, Reachability Analysis, and Adaptive Sampling in the Gulf of Mexico

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.

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Published
2024-09-23
DOI
https://doi.org/10.1109/oceans55160.2024.10754153
Citations
6
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
2.01

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article

Real-time Ocean Probabilistic Forecasts, Reachability Analysis, and Adaptive Sampling in the Gulf of Mexico

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article en
6 citations

Abstract

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.

Rutgers, The State University of New Jersey (US), Florida State University (US), Woodwell Climate Research Center (US), University of Miami (US), University of the Virgin Islands (VI), National Academies of Sciences, Engineering, and Medicine (US), Fugro (Norway) (NO), Rutgers Sexual and Reproductive Health and Rights (NL), Massachusetts Institute of Technology (US), Centro de Investigación Científica y de Educación Superior de Ensenada (MX), Woods Hole Oceanographic Institution (US), Texas A&M University (US)
National Academies of Sciences, Engineering, and Medicine
Life below water
Openalex Percentile: Top 14%
Reservoir Engineering and Simulation Methods
2.01
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