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Title: Sampling, feasibility, and priors in data assimilation

Importance sampling algorithms are discussed in detail, with an emphasis on implicit sampling, and applied to data assimilation via particle filters. Implicit sampling makes it possible to use the data to find high-probability samples at relatively low cost, making the assimilation more efficient. A new analysis of the feasibility of data assimilation is presented, showing in detail why feasibility depends on the Frobenius norm of the covariance matrix of the noise and not on the number of variables. A discussion of the convergence of particular particle filters follows. A major open problem in numerical data assimilation is the determination of appropriate priors, a progress report on recent work on this problem is given. The analysis highlights the need for a careful attention both to the data and to the physics in data assimilation problems.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [4]
  1. Univ. of Kansas, Lawrence, KS (United States)
  2. Univ. of Arizona, Tucson, AZ (United States)
  3. Oregon State Univ., Corvallis, OR (United States)
  4. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Discrete and Continuous Dynamical Systems
Additional Journal Information:
Journal Volume: 36; Journal Issue: 8; Journal ID: ISSN 1078-0947
Publisher:
American Institute of Mathematical Sciences
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1439205
Alternate Identifier(s):
OSTI ID: 1379537