Bayesian data assimilation

A Bayesian Approach to Data Assimilation

Data assimilation is formulated in a Bayesian context. This leads to a sampling problem in the space of continuous time paths. By writing down a density in path space, and conditioning on observations, it is possible to define a range of Markov Chain Monte Carlo (MCMC) methods which sample from the desired distribution in path space, and thereby solve the data assimilation problem. The basic bu...

Prediction and assimilation of surf-zone processes using a Bayesian network

Bayesian Analysis for Oceanographers

Bayesian statistics provides a general framework for the treatment of problems involving uncertainty. A brief introduction to the principles of Bayesian analysis is presented. Special attention is given to the application of the Bayesian viewpoint in deterministic settings. Short reviews of the roles the Bayesian approach plays in areas of interpolation or \objective analysis" and data assimila...

Bayesian assimilation of snow cover data

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model S. Kolberg, H. Rue, and L. Gottschalk SINTEF Energy Research, Sem Sælands vei 11, 7465 Trondheim, Norway Department of Mathematical Sciences, NTNU, 7491 Trondheim, Norway Department of Geosciences, University of Oslo. P.O. Box 1047 Blindern, 0316 Oslo, Norway Received: 24 June 2005 – Accepted: 6 July 2...

Sampling The Posterior: An Approach to Non-Gaussian Data Assimilation

The viewpoint taken in this paper is that data assimilation is fundamentally a statistical problem and that this problem should be cast in a Bayesian framework. In the absence of model error, the correct solution to the data assimilation problem is to find the posterior distribution implied by this Bayesian setting. Methods for dealing with data assimilation should then be judged by their abili...