April 26, 2011 to May 27, 2011
Europe/Stockholm timezone

Data assimilation with uncertain static parameters; or Why it is hard to calibrate dynamical models.

May 5, 2011, 10:30 AM
132:028 (Nordita)




Dr Jonathan ROUGIER


ROUGIER: Data assimilation with uncertain static parameters Abstract: Using the link between variational methods and maximum likelihood, I explore in a non-technical way the conditions under which data assimilation produces consistent estimators, and show that with a perfect model these conditions distinguish clearly between learning about the state vector and learning about the static parameters ('calibration'). Interestingly -- perhaps paradoxically -- the situation for calibration improves when the model is imperfect, and correctly modelled as such. These results are only a sketch in response to the discussions we have been having about imperfect models, and the pros and cons of stochastic models.

Presentation materials

There are no materials yet.