Título del libro:
Título del capítulo: Real-time Ocean Probabilistic Forecasts, Reachability Analysis, and Adaptive Sampling in the Gulf of Mexico
ROSALINDA MONREAL JIMENEZ; DAVID ALBERTO SALAS DE LEON; VICTOR KEVIN CONTRERAS TEREZA;
Autores externos:
Idioma:
Año de publicación:
2024
Palabras clave:
Risk assessment; Risk perception; Underwater imaging; Weather forecasting; Adaptive sampling; Data assimilation; Forecast skill; Mutual informations; Ocean gliders; Ocean model; Predictability; Probabilistic forecasting; Reachability analysis; Real- time; Gliders
Resumen:
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. © 2024 IEEE.
Entidades citadas de la UNAM:
ISBN:
9798331540081
Editorial:
Institute of Electrical and Electronics Engineers Inc.
