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Title: Cluster Sampling Filters for Non-Gaussian Data Assimilation

Journal Article · · Atmosphere (Basel)
DOI:https://doi.org/10.3390/atmos9060213· OSTI ID:1463676
 [1];  [2];  [2]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
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This paper presents a fully non-Gaussian filter for sequential data assimilation. The filter is named the “cluster sampling filter”, and works by directly sampling the posterior distribution following a Markov Chain Monte-Carlo (MCMC) approach, while the prior distribution is approximated using a Gaussian Mixture Model (GMM). Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a GMM to the prior ensemble. Using the data likelihood function, the posterior density is then formulated as a mixture density, and is sampled following an MCMC approach. Four versions of the proposed filter, namely C ℓ MCMC , C ℓ HMC , MC- C ℓ HMC , and MC- C ℓ HMC are presented. C ℓ MCMC uses a Gaussian proposal density to sample the posterior, and C ℓ HMC is an extension to the Hamiltonian Monte-Carlo (HMC) sampling filter. MC- C ℓ MCMC and MC- C ℓ HMC are multi-chain versions of the cluster sampling filters C ℓ MCMC and C ℓ HMC respectively. The multi-chain versions are proposed to guarantee that samples are taken from the vicinities of all probability modes of the formulated posterior. The new methodologies are tested using a simple one-dimensional example, and a quasi-geostrophic (QG) model with double-gyre wind forcing and bi-harmonic friction. Here, numerical results demonstrate the usefulness of using GMMs to relax the Gaussian prior assumption especially in the HMC filtering paradigm.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Air Force Research Laboratory (AFRL), Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF); Virginia Polytechnic Institute, Dept of Computer Science; USDOE
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1463676
Journal Information:
Atmosphere (Basel), Vol. 9, Issue 6; ISSN 2073-4433
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 8 works
Citation information provided by
Web of Science

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Cited By (1)

Tuning Covariance Localization Using Machine Learning
  • Moosavi, Azam; Attia, Ahmed; Sandu, Adrian
  • Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part IV, p. 199-212 https://doi.org/10.1007/978-3-030-22747-0_16
book June 2019

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Related Subjects

97 MATHEMATICS AND COMPUTING
data assimilation
ensemble filters
gaussian mixture models
hamiltonian monte-carlo
markov chain monte-carlo sampling