It is generally accepted that models of the deep ocean must assimilate observations
in order to make realistic forecasts in regions dominated by mesoscale variability
(i.e., “ocean weather”). The present study is an attempt to quantify the information
on ocean weather that is contained in Lagrangian trajectories, and the best way to
extract it. Following a review of ocean data assimilation in a Bayesian framework,
including the Ensemble Kalman Filter and the Particle Filter, a new class of idealized
models of self advecting vortices is introduced. Through a large number of carefully
designed Monte Carlo experiments it is shown when, where and why the Ensemble
Kalman Filter will fail. The study concludes with a discussion of a hybrid scheme that
takes advantage of the lower computational cost of the Ensemble Kalman Filter and
the ability of the Particle Filter to handle highly non-Gaussian probability density
functions. / MSc Thesis
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/13056 |
| Date | 27 August 2010 |
| Creators | Jacobs, Muhammad-Kassiem |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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