2015/05/29 by Alexandre J. Chorin, Chorin, Alexandre J., Fei Lu +7
Computer Science · Earth and Planetary Sciences · Environmental Science · Mathematics · #Climate variability and models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Meteorological Phenomena and Simulations #stat.CO
paper · pdf · doi:10.48550/arxiv.1506.00043
arxiv created 2015/05/29 · openalex publication_date 2015/05/29 · arxiv updated 2015/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.