An Adjoint-Based Adaptive Ensemble Kalman Filter
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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American Meteorological Society
2014
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| Online Access: | http://hdl.handle.net/1721.1/87992 https://orcid.org/0000-0003-1895-9124 |
| _version_ | 1826188180860174336 |
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| author | Song, Hajoon Hoteit, Ibrahim Cornuelle, Bruce D. Luo, Xiaodong Subramanian, Aneesh C. |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Song, Hajoon Hoteit, Ibrahim Cornuelle, Bruce D. Luo, Xiaodong Subramanian, Aneesh C. |
| author_sort | Song, Hajoon |
| collection | MIT |
| description | A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters. |
| first_indexed | 2024-09-23T07:55:46Z |
| format | Article |
| id | mit-1721.1/87992 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:55:46Z |
| publishDate | 2014 |
| publisher | American Meteorological Society |
| record_format | dspace |
| spelling | mit-1721.1/879922022-09-30T01:04:08Z An Adjoint-Based Adaptive Ensemble Kalman Filter Song, Hajoon Hoteit, Ibrahim Cornuelle, Bruce D. Luo, Xiaodong Subramanian, Aneesh C. Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Song, Hajoon A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters. 2014-06-16T13:41:40Z 2014-06-16T13:41:40Z 2013-10 2013-03 Article http://purl.org/eprint/type/JournalArticle 0027-0644 1520-0493 http://hdl.handle.net/1721.1/87992 Song, Hajoon, Ibrahim Hoteit, Bruce D. Cornuelle, Xiaodong Luo, and Aneesh C. Subramanian. “An Adjoint-Based Adaptive Ensemble Kalman Filter.” Monthly Weather Review 141, no. 10 (October 2013): 3343–3359. © 2013 American Meteorological Society https://orcid.org/0000-0003-1895-9124 en_US http://dx.doi.org/10.1175/MWR-D-12-00244.1 Monthly Weather Review Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Meteorological Society American Meteorological Society |
| spellingShingle | Song, Hajoon Hoteit, Ibrahim Cornuelle, Bruce D. Luo, Xiaodong Subramanian, Aneesh C. An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title | An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title_full | An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title_fullStr | An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title_full_unstemmed | An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title_short | An Adjoint-Based Adaptive Ensemble Kalman Filter |
| title_sort | adjoint based adaptive ensemble kalman filter |
| url | http://hdl.handle.net/1721.1/87992 https://orcid.org/0000-0003-1895-9124 |
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