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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF WASHINGTON
Doing Business As Name:University of Washington
PD/PI:
  • Eric D'Asaro
  • (206) 685-2982
  • dasaro@apl.washington.edu
Co-PD(s)/co-PI(s):
  • Andrey Shcherbina Dr
Award Date:07/29/2021
Estimated Total Award Amount: $ 88,617
Funds Obligated to Date: $ 88,617
  • FY 2021=$88,617
Start Date:09/01/2021
End Date:08/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.050
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Proposal: Harnessing simulation data to characterize transition layer mixing rates and mechanisms
Federal Award ID Number:2122867
DUNS ID:605799469
Parent DUNS ID:042803536
Program:PHYSICAL OCEANOGRAPHY
Program Officer:
  • Baris Uz
  • (703) 292-4557
  • bmuz@nsf.gov

Awardee Location

Street:4333 Brooklyn Ave NE
City:Seattle
State:WA
ZIP:98195-0001
County:Seattle
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:University of Washington
Street:1013 NE 40th Street
City:Seattle
State:WA
ZIP:98105-6698
County:Seattle
Country:US
Cong. District:07

Abstract at Time of Award

This project will examine entrainment processes in the ocean transition layer (TL), the vertical region between the lower part of the turbulent near-surface mixed layer and the stably stratified ocean interior. The project will use a novel combination of approaches, including further analysis of existing high-resolution profile data through the upper ocean layers, high-resolution modeling of entrainment and internal wave processes in the transition layer, and machine learning techniques. These processes help determine ocean mixed layer depth and temperature and ultimately mediate air-sea exchange effects on the global ocean, yet they remain poorly understood or represented in numerical model parameterizations. Outcomes will be directly relevant to improving physical and biogeochemical ocean models. Additionally, the methods developed will be applicable to interpreting and analyzing data from a variety of geophysical flows, and the analysis scripts will be made publicly available. The work will support an early career investigator, the training of a graduate student, contributions to local outreach and educational programs, and will form the basis for a project for a graduate student in the WHOI summer program in Geophysical Fluid Dynamics. A suite of high-resolution direct numerical simulations will be generated covering a range of expected TL mechanisms, including Kelvin-Helmholtz and Holmboe instabilities and interfacial waves. Using standard fluid dynamical analyses, including characterization of the linear instabilities and a detailed analysis of the flow energetics, this comprehensive library of flow fields will be used to determine how well a given stratified mixing event can be characterized from limited measurements. The simulations will then be used as training data for a neural network-based flow classification method, allowing for input profiles of temperature and velocity to be classified in terms of the underlying waves or instabilities. After the classification method is validated using simulation data, it will be applied to the observations, allowing for identification of the relevant mechanisms driving entrainment in the TL. Knowledge of the mixing associated with each mechanism can thus be used to describe the mixing efficiency and turbulent fluxes in the observational record. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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