Bayesian Reduced-Resolution Data Assimilation

Bayesian data assimilation

Data Assimilation in Reduced Modeling

This paper considers the problem of optimal recovery of an element u of a Hilbert space H from measurements of the form `j(u), j = 1, . . . ,m, where the `j are known linear functionals on H. Problems of this type are well studied [18] and usually are carried out under an assumption that u belongs to a prescribed model class, typically a known compact subset of H. Motivated by reduced modeling ...

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...

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...

A Bayesian approach to Lagrangian data assimilation

Lagrangian data arise from instruments that are carried by the flow in a fluid field. Assimilation of such data into ocean models presents a challenge due to the potential complexity of Lagrangian trajectories in relatively simple flow fields. We adopt a Bayesian perspective on this problem and thereby take account of the fully non-linear features of the