TITLE
"Theory and Applications of Variational, Sequential and Bayesian 
Hierarchical Methods in Data Assimilation"

ABSTRACT
Data assimilation methods that combine observations with dynamical 
models of the atmosphere and ocean rely on estimating the conditional 
probability distribution function (PDF) of the dynamical system 
conditioned on an observed state provided by measurements. 
Constructing such a PDF is impractical for most problems with high 
dimensions in the state vector and requires simplifications in the 
estimations of the conditional PDF. We compare the theories underlying 
several data assimilation methods (e.g. variational, ensemble, 
particle, bayesian hierarchical) and show how their estimate of 
the conditional PDF differs. We also present an example of a 
variational data assimilation experiment where the ocean surface 
winds associated with the transit of a hurricane are reconstructed 
using ocean buoy data.


Emanuele Di Lorenzo
School of Earth and Atmospheric Sciences
Georgia Institute of Technology
Atlanta, GA, 30332-0340