Incorporating Model Discrepancy into a Data Assimilation Framework

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Incorporating Model Discrepancy into a Data Assimilation Framework

Cari Kaufman
Linda Tran
University of California, Berkley

Tuesday, December 3, 2013
Mesa Lab Chapman Room
12:00 PM

Data assimilation allows us to incorporate observations into a physical model as the observations come online.  Applications include weather forecasting and GPS systems; methods include extended Kalman filters, particle filters, variational assimilation, and many others.  One key assumption of the data assimilation framework is that the physical model is the correct representation of reality.  However, this is often not true -- the physical model is an approximation and can therefore be discrepant with reality.  We examine inter-battery factor analysis, a Bayesian generative model for canonical correlation analysis (CCA), as a method to predict model discrepancy and show some preliminary results using Lorenz 2005 as a toy example.

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Start: 
Monday, December 2, 2013
End: 
Tuesday, December 3, 2013