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Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter

Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter
Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter
We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the 'curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models.
1463-5003
135-154
Annan, J.D.
dfa1bdc7-bf41-409c-960c-1d96adca782e
Hargreaves, J.C.
a6d5e120-16b7-4473-a8ac-9c0b96f27939
Edwards, N.R.
e41b719b-784e-4748-acc4-6ccbc4643c7d
Marsh, R.
702c2e7e-ac19-4019-abd9-a8614ab27717
Annan, J.D.
dfa1bdc7-bf41-409c-960c-1d96adca782e
Hargreaves, J.C.
a6d5e120-16b7-4473-a8ac-9c0b96f27939
Edwards, N.R.
e41b719b-784e-4748-acc4-6ccbc4643c7d
Marsh, R.
702c2e7e-ac19-4019-abd9-a8614ab27717

Annan, J.D., Hargreaves, J.C., Edwards, N.R. and Marsh, R. (2005) Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter. Ocean Modelling, 8 (1-2), 135-154. (doi:10.1016/j.ocemod.2003.12.004).

Record type: Article

Abstract

We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the 'curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models.

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Published date: 2005

Identifiers

Local EPrints ID: 9726
URI: https://eprints.soton.ac.uk/id/eprint/9726
ISSN: 1463-5003
PURE UUID: a811b476-973b-427d-b074-ed168c54f3ce

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Date deposited: 13 Oct 2004
Last modified: 17 Jul 2017 17:09

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Contributors

Author: J.D. Annan
Author: J.C. Hargreaves
Author: N.R. Edwards
Author: R. Marsh

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