Campuses:

Talk 1: Assimilation of Sea Surface Height Anomaly Data and Lagrangian Position Data from Floats and Drifters

Wednesday, May 1, 2002 - 3:30pm - 4:00pm
Keller 3-180
Arthur Mariano (University of Miami)
In collaboration with T. Chin, A. Griffa, A. Haza, A. Molcard, T. Ozgokmen, and L. Piterbarg.

A Reduced-Order Information Filter (ROIF), based on a heterogeneous Markov Random Field (MRF) model for the spatial covariances, has been developed for assimilating sea surface height anomaly data and drifting buoy positions into the HYbrid Coordinate Ocean Model (HYCOM). Presently, the MRF is used to encode the large Gaussian covariance matrix in a Kalman filter, and the optimal a-posteriori estimate can be computed efficiently by a convex minimization. (Assimilation of contour data such as oceanic fronts of the Gulf Stream, that makes the problem non-Gaussian, is under consideration, however.) The effectiveness of the ROIF is demonstrated in a number of twin experiments. Four-layer simulations of the classic wind-driven double gyre circulation indicate that simpler algorithms that decouple the estimation of horizontal and vertical covariances perform as well as the computational expensive 4-D covariance ROIF. Forecasts errors for sea surface height and velocities in a coarse-resolution sixteen-layer simulation of the N. Atlantic exhibit an initial rapid and then a steady decrease with assimilation period, even after 6 months of assimilation.

An outstanding data assimilation problem, due to the nonlinear relationship between the Lagrangian velocities and their Eulerian model counterparts, is the optimization of the Lagrangian information in position data from near-surface drifters and subsurface floats. A hierarchy of model assumptions, data density, and initial launch locations are being evaluated in strongly nonlinear numerical simulations of the classic wind-driven double gyre circulation. The numerical results show that, even for simple linearization of the Lagrangian-Eulerian velocity relationship, the assimilation of Lagrangian data, because of their horizontal coverage, leads to better model forecasts then the assimilation of an equivalent amount of Eulerian data.