Applications of Adaptive Sampling Strategies of Autonomous Vehicles, Drifters, Floats, and HF-Radar, to Improve Loop Current System Dynamics Forecasts in the Deepwater Gulf of Mexico

Abstract The Gulf of Mexico holds vital natural, commercial, and societal resources. A diverse array of stakeholders (which includes the offshore energy sector, climate scientists, living resources managers, recreational and commercial fishing industry, tourism, navigation, homeland security, the National Weather Service, oil spill, tropical weather forecasters) rely on accurate and timely prediction of the deepwater dynamics to perform safe operations and to understand the complex interactions of the earth climate and weather system. A strategy to improve predictive skill of numerical ocean circulation models of the deepwater Gulf of Mexico using adaptive sampling of in situ oceanographic observational platforms, which includes autonomous vehicles, buoyancy gliders, floats, drifters, and high-frequency radar is described. Profiling platforms, i.e., gliders and floats, will collect co-located estimates of temperature, salinity, and current velocity, to provide estimates of the total kinematic vertical water-column structure. The observations will be made available to numerical circulation modelers for injection into data assimilation routines and for model skill assessment, validation, and data denial experiments. The activities, to take place in 2023 to 2027, are focused on the Mini Adaptive Sampling Test Run, i.e., MASTR, (summer 2023) and the Grand Adaptive Sampling Experiment, GrASE (2024-2025).