[18] Parameter optimization
[181] March 2, 13:2013:50 
Model Parameter Estimation Using Nonlinear Ensemble Algorithms

D. J. Posselt (JPL) and C. H. Bishop (Naval Research Laboratory, Monterey, CA) 
Abstract 
While data assimilation (DA) has most often been used to estimate initial conditions for numerical prediction models, it is increasingly used to interrogate aspects of the model internal physics. In particular, DA techniques can be used to examine how changes in empirical model parameters affect the evolution of the model state. It is now generally acknowledged that introducing variability in parameters in an ensemble data assimilation and prediction context can increase the realism of the prediction system. There are, however, several challenges that must be addressed. Specifically, many parameters are nonlinearly related to the model output, and the transfer functions that map from state to observation space may introduce further nonlinearity. In addition, the prior parameter distributions may be poorly known, and are often bounded at zero. 
Presentation file:  18_1_D.Posselt.pdf 