You are here: MIMS > EPrints
MIMS EPrints

2014.69: Variational data assimilation using targetted random walks

2014.69: Simon Cotter, Massoumeh Dashti and Andrew Stuart (2012) Variational data assimilation using targetted random walks. International Journal for Numerical Methods in Fluids, 68 (4). pp. 403-421.

Full text available as:

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
279 Kb

DOI: 10.1002/fld.2510


The variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis. In either of these scenarios, it can be important to assess uncertainties in the assimilated state. Ideally, it is desirable to have complete information concerning the Bayesian posterior distribution for unknown state given data. We show that complete computational probing of this posterior distribution is now within the reach in the offline situation. We introduce a Markov chain–Monte Carlo (MCMC) method which enables us to directly sample from the Bayesian posterior distribution on the unknown functions of interest given observations. Since we are aware that these methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk-type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however, more sophisticated MCMC methods are available which exploit derivative information. For simplicity of exposition, we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number flow in a two-dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces.

Item Type:Article
Subjects:MSC 2000 > 62 Statistics
MSC 2000 > 76 Fluid mechanics
MIMS number:2014.69
Deposited By:Dr Simon L Cotter
Deposited On:18 December 2014

Download Statistics: last 4 weeks
Repository Staff Only: edit this item