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

Ruckelt, J., Sauerland, V., Slawig, Thomas, Srivastav, Anand, 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). pp. 3993-4009. DOI 10.1016/j.nonrwa.2010.03.006.

Full text not available from this repository.

Supplementary data:

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. (C) 2010 Elsevier Ltd. All rights reserved.

Document Type: Article
Additional Information: Times Cited: 7 Rueckelt, J. Sauerland, V. Slawig, T. Srivastava, A. Ward, B. Patvardhan, C.
Keywords: Parameter optimization, Biogeochemical modeling, Gradient-based optimization, Genetic algorithm, Algorithmic differentiation
Research affiliation: Kiel University > Kiel Marine Science
OceanRep > The Future Ocean - Cluster of Excellence > FO-R11
OceanRep > The Future Ocean - Cluster of Excellence
Kiel University
Refereed: Yes
Open Access Journal?: No
DOI etc.: 10.1016/j.nonrwa.2010.03.006
ISSN: 1468-1218
Projects: Future Ocean
Date Deposited: 30 Jan 2017 10:29
Last Modified: 30 Jul 2019 05:41
URI: http://oceanrep.geomar.de/id/eprint/35685

Actions (login required)

View Item View Item