Optimization of integrated Earth System Model components using Grid-enabled data management and computation: Research Articles

Authors:
A. R. Price;G. Xue;A. Yool;D. J. Lunt;P. J. Valdes;T. M. Lenton;J. L. Wason;G. E. Pound;S. J. Cox
Affiliations:
Southampton Regional e-Science Centre, University of Southampton, Southampton SO17 1BJ, U.K.;Southampton Regional e-Science Centre, University of Southampton, Southampton SO17 1BJ, U.K.;National Oceanography Centre, University of Southampton, Southampton SO14 3ZH, U.K.;School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, U.K.;School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, U.K.;School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, U.K.;Southampton Regional e-Science Centre, University of Southampton, Southampton SO17 1BJ, U.K.;Southampton Regional e-Science Centre, University of Southampton, Southampton SO17 1BJ, U.K.;Southampton Regional e-Science Centre, University of Southampton, Southampton SO17 1BJ, U.K.
Venue:
Concurrency and Computation: Practice & Experience - Selected Papers from the 2004 U.K. e-Science All Hands Meeting (AHM 2004)
Year:
2007

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Abstract

In this paper, we present the Grid enabled data management system that has been deployed for the Grid ENabled Integrated Earth system model (GENIE) project. The database system is an augmented version of the Geodise Database Toolbox and provides a repository for scripts, binaries and output data in the GENIE framework. By exploiting the functionality available in the Geodise toolboxes we demonstrate how the database can be employed to tune parameters of coupled GENIE Earth System Model components to improve their match with observational data. A Matlab client provides a common environment for the project Virtual Organization and allows the scripting of bespoke tuning studies that can exploit multiple heterogeneous computational resources. We present the results of a number of tuning exercises performed on GENIE model components using multi-dimensional optimization methods. In particular, we find that it is possible to successfully tune models with up to 30 free parameters using Kriging and Genetic Algorithm methods. Copyright © 2006 John Wiley & Sons, Ltd.