Initial impacts of the Hurricane Sentinel glider fleet deployed during the 2018 hurricane season
Abstract
Regional ocean observing systems are increasingly utilized under hurricanes to sample essential ocean variables, map essential ocean features, and illustrate essential physical ocean processes that feedback on storm intensity. Specifically, autonomous underwater gliders have emerged as robust storm sampling devices capable of transmitting data in real time to support ocean data assimilation and validation, essential to the improvement of coupled ocean-atmosphere forecast skill.
This paper discusses the initial results from an ongoing collaboration between the Naval Oceanographic Office, the National Oceanic and Atmospheric Administration, the National Science Foundation, Shell, numerous academic partners associated with the U.S. Integrated Ocean Observing System, and other research efforts to demonstrate the value of ocean gliders to improve hurricane forecasting. During the 2018 hurricane season, a hurricane picket line of gliders was collaboratively deployed and operated to monitor ocean conditions in the Caribbean, Gulf of Mexico, South Atlantic Bight, and Middle Atlantic Bight. Over 30 gliders, including those deployed for other research efforts reported their near-real-time data through a common cyber-infrastructure, the U.S. IOOS Glider Data Assembly Center (DAC), to the global operational forecast centers. Two of these gliders were deployed within 100 km of Hurricane Florence's landfall location in North Carolina. Seven gliders were deployed in the northeastern Gulf of Mexico in the vicinity of Hurricane Michael, with one glider located within 25 km of the storm's eye. In this study we present comparisons between glider data and the operational ocean models for both Hurricane Michael and Florence. Our focus is on using the glider data, alongside satellite remote sensing and all available in situ observations, to define the essential ocean features and essential physical ocean processes the operational ocean models must resolve to positively impact hurricane forecasts. These observations will be critical to future assimilation data denial observing system experiments, and will also be used evaluate the impact of data assimilation on the Navy's Global Ocean Forecast System, as it provides the initial conditions to the ocean models used for operational hurricane forecasting.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMNH13E..04M
- Keywords:
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- 4313 Extreme events;
- NATURAL HAZARDS