Fundamental dynamics of upper-ocean flows and their interaction with sea surface
Abstract
This project aims to answer the following question: From remote sensing of sea surface signatures, how do we deduce the underwater flows in upper oceans? We propose a theoretical and computational study based on rigorous mathematical derivation, theoretical proof, and advanced numerical algorithms. We will study the following three aspects of the upper-ocean inverse modeling problem. (1)Investigate the correlation of sea surface roughness with internal waves. The goal of this research task is to develop an inverse modeling capability to deduce the internal wave properties from the sea surface roughness. We will use both a two-layer high-order spectral method and the large-eddy simulation on a wave-boundary-fitted grid, and combine them with adjoint models.(2)Investigate the correlation of sea surface thermal quantities with underwater flow structures. The goal of this research task is to develop an accurate and robust capability for inverse modeling of upper-ocean flow structures based on sea surface temperature. We will derive a set of Volterra-type integral equations for quantifying surface divergence, a measure of upwelling strength, based on sea surface temperature and heat flux for complex ocean conditions. We will also develop numerical algorithms to solve for the integral equations in the inverse modeling.(3)Investigate the correlation of sea surface small-scale deformation with underwater turbulence. The goal of this research task is to deduce the underwater turbulence characteristics based on sea surface deformation at small scales. We willdevelop theoretical predictive models to deduce the statistics and structures of turbulence based on the observation of sea surfaceelevation variations at small scales. The theoretical model will be validated using high-fidelity simulations of various ocean conditions. The ultimate goal of this project is to pave the way for making the opaque ocean transparent in our Navy#s remote sensing applications.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412710
Entities
People
- Lian Shen
Organizations
- Office of Naval Research
- Regents of the University of Minnesota
- United States Navy