Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications
The properties and capabilities of the GMM-DO filter are assessed and exemplified by applications to two dynamical systems: (1) the Double Well Diffusion and (2) Sudden Expansion flows; both of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates the use of t...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Meteorological Society
2013
|
| Online Access: | http://hdl.handle.net/1721.1/78927 https://orcid.org/0000-0002-1869-3883 |
| _version_ | 1826203630197276672 |
|---|---|
| author | Sondergaard, Thomas Lermusiaux, Pierre F. J. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Sondergaard, Thomas Lermusiaux, Pierre F. J. |
| author_sort | Sondergaard, Thomas |
| collection | MIT |
| description | The properties and capabilities of the GMM-DO filter are assessed and exemplified by applications to two dynamical systems: (1) the Double Well Diffusion and (2) Sudden Expansion flows; both of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates the use of the EM algorithm and Bayesian Information Criterion with Gaussian Mixture Models in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of non-trivial dimensionality and dynamics with jets and eddies. For each test case, qualitative and quantitative comparisons are made with contemporary filters. The sensitivity to input parameters is illustrated and discussed. Properties of the filter are examined and its estimates are described, including: the equation-based and adaptive prediction of the probability densities; the evolution of the mean field, stochastic subspace modes and stochastic coefficients; the fitting of Gaussian Mixture Models; and, the efficient and analytical Bayesian updates at assimilation times and the corresponding data impacts. The advantages of respecting nonlinear dynamics and preserving non-Gaussian statistics are brought to light. For realistic test cases admitting complex distributions and with sparse or noisy measurements, the GMM-DO filter is shown to fundamentally improve the filtering skill, outperforming simpler schemes invoking the Gaussian parametric distribution. |
| first_indexed | 2024-09-23T12:40:19Z |
| format | Article |
| id | mit-1721.1/78927 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:40:19Z |
| publishDate | 2013 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | mit-1721.1/789272022-10-01T10:24:28Z Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications Sondergaard, Thomas Lermusiaux, Pierre F. J. Massachusetts Institute of Technology. Department of Mechanical Engineering Lermusiaux, Pierre F. J. The properties and capabilities of the GMM-DO filter are assessed and exemplified by applications to two dynamical systems: (1) the Double Well Diffusion and (2) Sudden Expansion flows; both of which admit far-from-Gaussian statistics. The former test case, or twin experiment, validates the use of the EM algorithm and Bayesian Information Criterion with Gaussian Mixture Models in a filtering context; the latter further exemplifies its ability to efficiently handle state vectors of non-trivial dimensionality and dynamics with jets and eddies. For each test case, qualitative and quantitative comparisons are made with contemporary filters. The sensitivity to input parameters is illustrated and discussed. Properties of the filter are examined and its estimates are described, including: the equation-based and adaptive prediction of the probability densities; the evolution of the mean field, stochastic subspace modes and stochastic coefficients; the fitting of Gaussian Mixture Models; and, the efficient and analytical Bayesian updates at assimilation times and the corresponding data impacts. The advantages of respecting nonlinear dynamics and preserving non-Gaussian statistics are brought to light. For realistic test cases admitting complex distributions and with sparse or noisy measurements, the GMM-DO filter is shown to fundamentally improve the filtering skill, outperforming simpler schemes invoking the Gaussian parametric distribution. 2013-05-17T19:48:11Z 2013-05-17T19:48:11Z 2012 Article http://purl.org/eprint/type/JournalArticle 0027-0644 1520-0493 http://hdl.handle.net/1721.1/78927 Sondergaard, Thomas, and Pierre F. J. Lermusiaux. Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part II: Applications. Monthly Weather Review: 121011101334009, 2012. https://orcid.org/0000-0002-1869-3883 en_US http://dx.doi.org/10.1175/MWR-D-11-00296.1 Monthly Weather Review Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Meteorological Society MIT web domain |
| spellingShingle | Sondergaard, Thomas Lermusiaux, Pierre F. J. Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title | Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title_full | Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title_fullStr | Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title_full_unstemmed | Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title_short | Data Assimilation with Gaussian Mixture Models using the Dynamically Orthogonal Field Equations. Part II. Applications |
| title_sort | data assimilation with gaussian mixture models using the dynamically orthogonal field equations part ii applications |
| url | http://hdl.handle.net/1721.1/78927 https://orcid.org/0000-0002-1869-3883 |
| work_keys_str_mv | AT sondergaardthomas dataassimilationwithgaussianmixturemodelsusingthedynamicallyorthogonalfieldequationspartiiapplications AT lermusiauxpierrefj dataassimilationwithgaussianmixturemodelsusingthedynamicallyorthogonalfieldequationspartiiapplications |