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Multiobjective Tuning of GENIE fy Earth System Models

Multiobjective Tuning of GENIE fy Earth System Models. GENIE fy Earth System Modelling Workshop 26 th – 28 th June 2006 Andrew Price, Ivan Voutchkov, Graeme Pound, Simon Cox Southampton Regional e-Science Centre. Problem definition. 5000 year spin-up of C-GOLDSTEIN

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Multiobjective Tuning of GENIE fy Earth System Models

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  1. Multiobjective Tuning of GENIEfy Earth System Models GENIEfy Earth System Modelling Workshop 26th – 28th June 2006 Andrew Price, Ivan Voutchkov, Graeme Pound, Simon Cox Southampton Regional e-Science Centre Thursday, 03 April 2014

  2. Problem definition • 5000 year spin-up of C-GOLDSTEIN • Model reaches equilibrium • Find optimal parameter set • Model end state closest to equivalent observational data • Original C-GOLDSTEIN RMS error function • Exploit multiobjective optimisation to obtain pareto optimal solutions for the N constituent fields • Well studied problem within GENIEfy Thursday, 03 April 2014

  3. Response Surface Modelling • Optimisation of 12 parameters in cGOLDSTEIN ocean model • Each objective function calculation (model run) takes ~1 hour • Direct Search methods require too many evaluations to be practical • Employ a Kriging method to construct a Response Surface Model • Search a stochastic process model of the underlying objective function • Iteratively update the metamodel  Converge on an optimal solution R2=0.9052 • Optimal solutions: EnKF = 0.4986 ACCPM=0.4891 Krig=0.5145 Thursday, 03 April 2014

  4. Multi-Objective Optimisation • Single objective function • Weighted sum of (model – observation) RMS differences • Some objectives can be improved at the expense of others • Little improvement in the precipitation and evaporation fields • Multi-objective optimisation • Employ a Pareto Front to optimise multiple objectives • Implementation of the Non-dominated Sorting Genetic Algorithm (NSGA-II, Deb (2002)) • 3 objective functions • Weighted sum of the RMS differences between seasonal averages of model fields and equivalent observational data OBJ1 = (sensible heat + latent heat + net solar + net long) OBJ2 = (precipitation rate + evaporation) OBJ3 = (wind stress_x + wind stress_y) • IGCM problem definition • 32 free parameters (TXBLCNST = TYBLCNST) • 2 constraints on the parameters HUMCLOUDMAX > HUMCLOUDMIN SNOLOOK2 > ALBEDO_ICEHSEET Thursday, 03 April 2014

  5. Pareto Front Progression • 50 generations of the NSGA-II algorithm Thursday, 03 April 2014

  6. 5000 model invocations Southampton University Condor pool Iridis2 Compute Cluster National Grid Service Pareto Front driven towards origin 3 objective functions reduced Targeted improvements Evaporation fields improved without compromising other fields Multi-objective Optimisation Thursday, 03 April 2014

  7. Method Thursday, 03 April 2014

  8. Method • Initial sampling of underlying function (LPt) • Build a Krig metamodel of each objective • Extensive NSGA-II search of surrogate models • GA_NPOP=150, GA_GEN=150 • Evaluate updates • 50 points from Pareto front • 50 random point • 10 points of Greatest Expected Improvement • 10 points of greatest RMS error • Return to 2 Thursday, 03 April 2014

  9. Matlab Web Server SOAP Messages CondorWS Toolbox OMII Condor Web Service Condor Pool CondorNative Toolbox Condor Submit CondorSSH Toolbox SSH / SCP Unix/Linux Shell Globus (GT2) Compute Toolbox GRAM / GridFTP Grid Computation Thursday, 03 April 2014

  10. Results: 2000 year spin-ups Thursday, 03 April 2014

  11. Results: 5000 year spinu-ups Thursday, 03 April 2014

  12. Open Indonesian Throughflow Thursday, 03 April 2014

  13. Discussion • Multi-objective tuning removes the problem of choosing weighting factors to form a single objective • NSGA-II method using surrogate models appears to work well – an improvement on Kriging the single objective • Preparing a paper for eScience2006 • “International Workshop on Biologically-Inspired Optimisation Methods for Parallel and Distributed Architectures: Algorithms, Systems and Applications” • Submission due 10th July 2006 Thursday, 03 April 2014

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