Joint state and parameter estimation with an iterative ensemble Kalman smoother
Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensembl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2013-10-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/20/803/2013/npg-20-803-2013.pdf |
| _version_ | 1818648039017938944 |
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| author | M. Bocquet P. Sakov |
| author_facet | M. Bocquet P. Sakov |
| author_sort | M. Bocquet |
| collection | DOAJ |
| description | Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and parameter estimation that may inherit the benefits from both the ensemble filtering and variational approaches. <br><br> In this study, an augmented state IEnKS is tested on its estimation of the forcing parameter of the Lorenz-95 model. Since joint state and parameter estimation is especially useful in applications where the forcings are uncertain but nevertheless determining, typically in atmospheric chemistry, the augmented state IEnKS is tested on a new low-order model that takes its meteorological part from the Lorenz-95 model, and its chemical part from the advection diffusion of a tracer. In these experiments, the IEnKS is compared to the ensemble Kalman filter, the ensemble Kalman smoother, and a 4D-Var, which are considered the methods of choice to solve these joint estimation problems. In this low-order model context, the IEnKS is shown to significantly outperform the other methods regardless of the length of the data assimilation window, and for present time analysis as well as retrospective analysis. Besides which, the performance of the IEnKS is even more striking on parameter estimation; getting close to the same performance with 4D-Var is likely to require both a long data assimilation window and a complex modeling of the background statistics. |
| first_indexed | 2024-12-17T01:12:05Z |
| format | Article |
| id | doaj.art-91e290dc046342ada0f7a8f7c654fc90 |
| institution | Directory Open Access Journal |
| issn | 1023-5809 1607-7946 |
| language | English |
| last_indexed | 2024-12-17T01:12:05Z |
| publishDate | 2013-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Nonlinear Processes in Geophysics |
| spelling | doaj.art-91e290dc046342ada0f7a8f7c654fc902022-12-21T22:09:07ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462013-10-0120580381810.5194/npg-20-803-2013Joint state and parameter estimation with an iterative ensemble Kalman smootherM. BocquetP. SakovBoth ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and parameter estimation that may inherit the benefits from both the ensemble filtering and variational approaches. <br><br> In this study, an augmented state IEnKS is tested on its estimation of the forcing parameter of the Lorenz-95 model. Since joint state and parameter estimation is especially useful in applications where the forcings are uncertain but nevertheless determining, typically in atmospheric chemistry, the augmented state IEnKS is tested on a new low-order model that takes its meteorological part from the Lorenz-95 model, and its chemical part from the advection diffusion of a tracer. In these experiments, the IEnKS is compared to the ensemble Kalman filter, the ensemble Kalman smoother, and a 4D-Var, which are considered the methods of choice to solve these joint estimation problems. In this low-order model context, the IEnKS is shown to significantly outperform the other methods regardless of the length of the data assimilation window, and for present time analysis as well as retrospective analysis. Besides which, the performance of the IEnKS is even more striking on parameter estimation; getting close to the same performance with 4D-Var is likely to require both a long data assimilation window and a complex modeling of the background statistics.http://www.nonlin-processes-geophys.net/20/803/2013/npg-20-803-2013.pdf |
| spellingShingle | M. Bocquet P. Sakov Joint state and parameter estimation with an iterative ensemble Kalman smoother Nonlinear Processes in Geophysics |
| title | Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| title_full | Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| title_fullStr | Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| title_full_unstemmed | Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| title_short | Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| title_sort | joint state and parameter estimation with an iterative ensemble kalman smoother |
| url | http://www.nonlin-processes-geophys.net/20/803/2013/npg-20-803-2013.pdf |
| work_keys_str_mv | AT mbocquet jointstateandparameterestimationwithaniterativeensemblekalmansmoother AT psakov jointstateandparameterestimationwithaniterativeensemblekalmansmoother |