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Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation

Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation
Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation
Methods and results for parameter optimization and uncertainty analysis for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Schartau and Oschlies, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. Our aim is to identify parameters and fit the model output to given observational data. For this model, it has been shown that a satisfactory fit could not be obtained, and that parameters with comparable fits can vary significantly. Since these results were obtained by evolutionary algorithms (EA), we used a wider range of optimization methods: A special type of EA (called quantum-EA) with coordinate line search and a quasi-Newton SQP method, where exact gradients were generated by Automatic/Algorithmic Differentiation. Both methods are parallelized and can be viewed as instances of a hybrid, mixed evolutionary and deterministic optimization algorithm that we present in detail. This algorithm provides a flexible and robust tool for parameter identification and model validation. We show how the obtained parameters depend on data sparsity and given data error. We present an uncertainty analysis of the optimized parameters w.r.t. Gaussian perturbed data. We show that the model is well suited for parameter identification if the data are attainable. On the other hand, the result that it cannot be fitted to the real observational data without extension or modification, is confirmed.
1468-1218
3993-4009
Rückelt, J.
8ac5eee7-febc-4746-b224-98488109c7e3
Sauerland, V.
6680d869-4ceb-432e-b3cc-afcca5a772e2
Slawig, T.
82b92653-9546-477c-b830-ef37a7925610
Srivastav, A.
4e7c2413-59a5-4c64-abca-e9c819effc13
Ward, B.
9063af30-e344-4626-9470-8db7c1543d05
Patvardhan, C.
029f9e81-4462-47ae-b6b3-f311b75224b8
Rückelt, J.
8ac5eee7-febc-4746-b224-98488109c7e3
Sauerland, V.
6680d869-4ceb-432e-b3cc-afcca5a772e2
Slawig, T.
82b92653-9546-477c-b830-ef37a7925610
Srivastav, A.
4e7c2413-59a5-4c64-abca-e9c819effc13
Ward, B.
9063af30-e344-4626-9470-8db7c1543d05
Patvardhan, C.
029f9e81-4462-47ae-b6b3-f311b75224b8

Rückelt, J., Sauerland, V., Slawig, T., Srivastav, A., Ward, B. and Patvardhan, C. (2010) Parameter optimization and uncertainty analysis in a model of oceanic CO2 uptake using a hybrid algorithm and algorithmic differentiation. Nonlinear Analysis: Real World Applications, 11 (5), 3993-4009. (doi:10.1016/j.nonrwa.2010.03.006).

Record type: Article

Abstract

Methods and results for parameter optimization and uncertainty analysis for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Schartau and Oschlies, simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. Our aim is to identify parameters and fit the model output to given observational data. For this model, it has been shown that a satisfactory fit could not be obtained, and that parameters with comparable fits can vary significantly. Since these results were obtained by evolutionary algorithms (EA), we used a wider range of optimization methods: A special type of EA (called quantum-EA) with coordinate line search and a quasi-Newton SQP method, where exact gradients were generated by Automatic/Algorithmic Differentiation. Both methods are parallelized and can be viewed as instances of a hybrid, mixed evolutionary and deterministic optimization algorithm that we present in detail. This algorithm provides a flexible and robust tool for parameter identification and model validation. We show how the obtained parameters depend on data sparsity and given data error. We present an uncertainty analysis of the optimized parameters w.r.t. Gaussian perturbed data. We show that the model is well suited for parameter identification if the data are attainable. On the other hand, the result that it cannot be fitted to the real observational data without extension or modification, is confirmed.

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Published date: 1 October 2010

Identifiers

Local EPrints ID: 417049
URI: http://eprints.soton.ac.uk/id/eprint/417049
ISSN: 1468-1218
PURE UUID: 5f5c6be0-b5da-4557-9962-a1cfa714eb2f

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Date deposited: 18 Jan 2018 17:30
Last modified: 13 Mar 2019 18:59

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