MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Observations of the Upper Ocean from Autonomous Platforms during the Passage of Extratropical Cyclone Epsilon (2020)

Author(s)
Zimmerman, Michael T.
Thumbnail
DownloadThesis PDF (45.94Mb)
Advisor
Jayne, Steven R.
Terms of use
In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/
Metadata
Show full item record
Abstract
Hurricane Epsilon (2020) was a late-season, category-3 tropical cyclone that underwent extratropical transition and became Extratropical Cyclone Epsilon on 26 October. The upper ocean response to the passage of the storm was observed by three types of autonomous platforms: the eXpendable Spar buoy, the Air-Launched Autonomous Micro Observer profiling float, and two Seagliders. Taken together, this array enabled the rare collection of contemporaneous observations of the upper ocean, air-sea interface, and atmospheric boundary layer before, during, and after the passage of the storm. The evidence presented highlights how Extratropical Cyclone Epsilon broke down the residual North Atlantic summer stratification regime and accelerated the shift to the period of prolonged ocean cooling associated with winter. The significance of the synergistic capabilities of the array is two-fold: 1) comparing observations of the same parameters, taken from different platforms, enables a comprehensive approach to better understanding how storm-induced momentum, sensible heat, and moisture fluxes input kinetic and near-inertial energy into the ocean and thereby alter upper ocean structure; and 2) future, targeted deployments of similarly capable observational arrays will reduce the uncertainty of tropical and extratropical cyclone intensity forecasts by facilitating the assimilation of real-time subsurface ocean data into coupled numerical prediction models.
Date issued
2023-09
URI
https://hdl.handle.net/1721.1/153102
Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Publisher
Massachusetts Institute of Technology

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.