Optimizing Numerical Models for Submesoscale Eddy Prediction

Joseph D'Addezio (The University of Southern Mississippi, United States)

CoAuthors

Gregg Jacobs (Naval Research Laboratory, USA)

Event: 2016 Ocean Surface Topography Science Team Meeting

Session: Tides, internal tides and high-frequency processes

Presentation type: Type Poster

With the impending launch of the Surface Water and Ocean Topography (SWOT) satellite mission, unprecedented sea surface height (SSH) data is on the horizon bringing with it the ability to begin monitoring submescale eddies on a global scale. These eddies are known to play an integral role in mixed layer physics and are theorized to be important transporters of mass and heat from the near surface to the interior of the ocean. But, because a similar satellite mission has never been undertaken, ocean forecast systems are not currently prepared for this forthcoming very high-resolution SSH data. An essential component of optimizing model infrastructure for the purposes of submesoscale eddy prediction is to determine the most practical analytical solution for representing them in the model. While the primitive equation can be used quite effectively, forward integration of the 3D solution is computationally taxing. As such, we compare the results of the aforementioned solution with two analytical solutions that have proved effective on the mesoscale: quasi-geostrophy and semi-geostrophy. All three solutions are examined within a regional Gulf of Mexico simulation set to a horizontal resolution of 1 km. An additional set of simulations were run at 250 m resolution over the same domain. Both quasi- and semi-geostrophy are advantageous because while they reduce complexity and computation time, they still account for nonlinear advection by the geostrophic current as well as horizontal divergence associated with the ageostrophic component of the flow; processes which dominate submesoscale eddy dynamics. Nonetheless, a loss of accuracy is a byproduct and the determination of its magnitude will help develop useful statistics for operational forecasters.
 

Poster show times:

RoomStart DateEnd Date
Grande Halle Thu, Nov 03 2016,11:00 Thu, Nov 03 2016,18:00
Joseph D'Addezio
The University of Southern Mississippi
United States
jdaddezio@geol.sc.edu