Mapping Internal Tides using a data-assimilative reduced gravity model

Gary Egbert (Oregon State University, United States)

CoAuthors

Lana Erofeeva (Oregon State University, USA)

Event: 2014 Ocean Surface Topography Science Team Meeting

Session: Tides, internal tides and high-frequency processes

Presentation type: Type Poster

We present result from a simplified data assimilation scheme for mapping low-mode phase-locked internal tides from multi-mission altimetry data. The assimilation uses reduced gravity (RG) dynamics, allowing in an approximate way for bathymetry. Our development follows the approach of Griffiths and Grimshaw (2007), with the vertical dependence of pressure and velocities in the linear Boussinesq 3D equations expanded in basis functions derived from local 1D modes for a stratified ocean. This results in a system of coupled 2D PDEs for the coefficients of the modal expansion. Excluding coupling terms between modes (which arise in the presence of variable bottom topography) the resulting equations for each mode are analogous to the usual shallow water equations for the barotropic tide. With modest changes to the OSU tidal inversion software (OTIS) assimilation for this linear model can be easily implemented. The coupling terms can be used to derive the forcing (by the barotropic tide), and also suggest improvements of model error covariances, to account for spatial variability in unmodeled topographic scattering. We have applied the new RG inversion scheme to selected patches in the Pacific and Atlantic oceans, using all available data from the TP and Jason missions (including data from interleaved ground tracks) after correction with mapped SSH from AVISO, to reduce contamination by meso-scale ocean signals. In the deep ocean, data fits are excellent, solutions are well behaved, and results from adjacent patches show excellent agreement at overlaps, suggesting that global mapping of phase-locked low-mode internal tides is practical with this scheme. We will present initial versions of such maps.
 
Gary Egbert
Oregon State University
United States
egbert@coas.oregonstate.edu