Apte, A., Jones, C. K. R. T. (Christopher K. R. T.), Stuart, A. M. and Voss, J. (2008) Data assimilation: Mathematical and statistical perspectives. In: 9th ICFD Conference on Numerical Methods for Fluid Dynamics, Univ Reading, Reading, England, 26 - 29 Mar, 2007. Published in: International Journal for Numerical Methods in Fluids, Vol.56 (No.8). pp. 1033-1046.

Full text not available from this repository.

Abstract

The bulk of this paper contains a concise mathematical overview of the subject of data assimilation, highlighting three primary ideas: (i) the standard optimization approaches of 3DVAR, 4DVAR and weak constraint 4DVAR are described and their interrelations explained; (ii) statistical analogues of these approaches are then introduced, leading to filtering (generalizing 3DVAR) and a form of smoothing (generalizing 4DVAR and weak constraint 4DVAR) and the optimization methods are shown to be maximum a posteriori estimators for the probability distributions implied by these statistical approaches; and (iii) by taking a general dynamical systems perspective on the subject it is shown that the incorporation of Lagrangian data can be handled by a straightforward extension of the preceding concepts. We argue that the smoothing approach to data assimilation, based on statistical analogues of 4DVAR and weak constraint 4DVAR, provides the optimal solution to the assimilation of space-time distributed data into a model. The optimal Solution obtained is a probability distribution on the relevant class of functions (initial conditions or fine-dependent solutions). The approach is a useful one in the first instance because it clarifies the notion of what is the optimal Solution, thereby providing a benchmark against which existing approaches can be evaluated. In the longer term it also provides the potential for new methods to create ensembles of solutions to the model, incorporating the available data in an optimal fashion. Two examples are given illustrating this approach to data assimilation, both in the context of Lagrangian data, one based on statistical 4DVAR and the other on weak constraint statistical 4DVAR. The former is compared with the ensemble Kalman filter, which is thereby shown to be inaccurate in a variety of scenarios. Copyright (C) 2007 John Wiley & Sons, Ltd.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery Q Science > QC Physics
Divisions:	Faculty of Science > Mathematics
Journal or Publication Title:	International Journal for Numerical Methods in Fluids
Publisher:	John Wiley & Sons Ltd.
ISSN:	0271-2091
Date:	20 March 2008
Volume:	Vol.56
Number:	No.8
Number of Pages:	14
Page Range:	pp. 1033-1046
Identification Number:	10.1002/fld.1698
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Title of Event:	9th ICFD Conference on Numerical Methods for Fluid Dynamics
Type of Event:	Conference
Location of Event:	Univ Reading, Reading, England
Date(s) of Event:	26 - 29 Mar, 2007
URI:	http://wrap.warwick.ac.uk/id/eprint/30268

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item