Invitation: AGU Fall Meeting 2013 Session ‘Information and Uncertainty in Data and Models: Towards a Common Framework for Model Building and Prediction’


News / Sunday, December 9th, 2012

Data assimilation algorithms combine available observations of physical systems with the assumed model dynamics in a systematic manner, to produce better estimates of initial conditions for prediction. Broadly they can be categorized in three main approaches: (a) sequential algorithms, (b) sampling methods and (c) variational algorithms…

Detail

General Assembly of the American Geophysical Union, San Francisco.

Data assimilation algorithms combine available observations of physical systems with the assumed model dynamics in a systematic manner, to produce better estimates of initial conditions for prediction. Broadly they can be categorized in three main approaches: (a) sequential algorithms, (b) sampling methods and (c) variational algorithms which transform the density estimation problem to an optimization problem. However, given finite computational resources, only a handful of ensemble Kalman filters and 4DVar algorithms have been applied operationally to very high dimensional geophysical applications, such as weather forecasting. In this paper we present a recent extension to our variational Bayesian algorithm which seeks the ‘optimal’ posterior distribution over the continuous time states, within a family of non-stationary Gaussian processes. Read more…

http://abstractsearch.agu.org/meetings/2013/FM/H33J.html