Cite this paper:
YUAN Shijin, ZHANG Huazhen, LI Mi, MU Bin. CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm[J]. HaiyangYuHuZhao, 2019, 37(3): 957-967

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

YUAN Shijin, ZHANG Huazhen, LI Mi, MU Bin
School of Software Engineering, Tongji University, Shanghai 201804, China
Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model, while simulating double-gyre variation in Regional Ocean Modeling System (ROMS). Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) is an effective method of studying the parameters sensitivity, which represents a type of parameter error with maximum nonlinear development at the prediction time. Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation (CNOP). In the paper, we proposed an improved simulated annealing (SA) algorithm to solve CNOP-P to get the optimal parameters error, studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation. Specifically, we firstly found the non-period oscillation of kinetic energy time series of double gyre variation, then extracted two transition periods, which are respectively from high energy to low energy and from low energy to high energy. For every transition period, three parameters, respectively wind amplitude (WD), viscosity coefficient (VC) and linear bottom drag coefficient (RDRG), were studied by CNOP-P solved with SA algorithm. Finally, for sensitive parameters, their effect on model simulation is verified. Experiments results showed that the sensitivity order is WD > VC >> RDRG, the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.
Key words:    parameter sensitivity|double gyre|Regional Ocean Modeling System (ROMS)|Conditional Nonlinear Optimal Perturbation (CNOP-P)|simulated annealing (SA) algorithm   
Received: 2017-09-23   Revised: 2018-01-07
PDF (1079 KB) Free
Print this page
Add to favorites
Email this article to others
Articles by YUAN Shijin
Articles by ZHANG Huazhen
Articles by LI Mi
Articles by MU Bin
Haidvogel D B, Arango H G, Hedstrom K, Beckmann A, Malanotte-Rizzoli P, Shchepetkin A F. 2000. Model evaluation experiments in the North Atlantic Basin:simulations in nonlinear terrain-following coordinates.Dynamics of Atmospheres and Oceans, 32(3-4):239-281.
Hall M C G, Cacuci D G, Schlesinger M E. 1982. Sensitivity analysis of a radiative-convective model by the adjoint method. Journal of Atmospheric Sciences, 39(9):2 038-2 050.
Hamby D M. 1994. A review of techniques for parameter sensitivity analysis of environmental models.Environmental Monitoring and Assessment, 32(2):135-154.
Jiang S, Jin F F, Ghil M. 1995. Multiple equilibria, periodic, and aperiodic solutions in a wind-driven, double-gyre, shallow-water model. Journal of Physical Oceanography, 25(5):764-786.
Kirkpatrick S, Gelatt C D Jr, Vecchi M P. 1983. Optimization by simulated annealing. Science, 220(4598):671-680.
Lu J X, Hsieh W W. 1997. Adjoint data assimilation in coupled atmosphere-ocean models:Determining model parameters in a simple equatorial model. Quarterly Journal of the Royal Meteorological Society, 123(543):2 115-2 139.
Lu J X, Hsieh W W. 1998. On determining initial conditions and parameters in a simple coupled atmosphere-ocean model by adjoint data assimilation. Tellus A:Dynamic Meteorology and Oceanography, 50(4):534-544.
Mahadevan A, Lu J, Meacham S P, Malanotte-Rizzoli P. 2001.The predictability of large-scale wind-driven flows.Nonlinear Processes in Geophysics, 8(6):449-465.
Marchesiello P, McWilliams J C, Shchepetkin A. 2003.Equilibrium structure and dynamics of the California Current System. Journal of Physical Oceanography, 33(4):753-783.
Moore A M, Arango H G, Di Lorenzo E, Cornuelle B D, Miller A J, Neilson D J. 2004. A comprehensive ocean prediction and analysis system based on the tangent linear and adjoint of a regional ocean model. Ocean Modelling, 7(1):227-258.
Moore A M. 1999. Wind-induced variability of ocean gyres.Dynamics of Atmospheres and Oceans, 29(2-4):335-364.
Mu M, Duan W S, Wang B. 2003. Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10(6):493-501.
Mu M, Duan W, Wang Q, Zhang R. 2010.An extension of conditional nonlinear optimal perturbation approach and its applications. Nonlinear Processes in Geophysics, 17(2):211-220.
Nauw J J, Dijkstra H A, Chassignet E P. 2004. Frictionally induced asymmetries in wind-driven flows. Journal of Physical Oceanography, 34(9):2 057-2 072.
Nauw J J, Dijkstra H A. 2001. The origin of low-frequency variability of double-gyre wind-driven flows. Journal of Marine Research, 59(4):567-597.
Navon I M. 1998. Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography. Dynamics of Atmospheres and Oceans, 27(1-4):55-79.
Pierini S. 2010.Coherence resonance in a double-gyre model of the Kuroshio Extension. Journal of Physical Oceanography, 40(1):238-248.
Primeau F, Newman D. 2008. Elongation and contraction of the western boundary current extension in a shallowwater model:A bifurcation analysis. Journal of Physical Oceanography, 38(7):1 469-1 485.
Primeau F. 2002. Multiple equilibria and low-frequency variability of the wind-driven ocean circulation. Journal of Physical Oceanography, 32(8):2 236-2 256.
Ren J H, Yuan S J, Mu B. 2016. Parallel modified artificial bee colony algorithm for solving conditional nonlinear optimal perturbation In:2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), IEEE, Sydney, NSW, Australia. p.333-340.
Sapsis T P, Dijkstra H A. 2013. Interaction of additive noise and nonlinear dynamics in the double-gyre wind-driven ocean circulation. Journal of Physical Oceanography, 43(2):366-381.
Shchepetkin A F, Mcwilliams J C. 2003. A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate.Journal of Geophysical Research:Oceans, 108(C3):3 090.
Shchepetkin A F, Mcwilliams J C. 2005. The regional oceanic modeling system (ROMS):a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4):347-404.
Shen J, Medjo T T, Wang S. 1999. On a wind-driven, doublegyre, quasi-geostrophic ocean model:numerical simulations and structural analysis. Journal of Computational Physics, 155(2):387-409.
Simonnet E, Ghil M, Dijkstra H. 2005. Homoclinic bifurcations in the quasi-geostrophic double-gyre circulation. Journal of Marine Research, 63(5):931-956.
Sun G D, Mu M. 2017. A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the LundPotsdam-Jena (LPJ) model as an example. Theoretical and Applied Climatology, 128(3-4):587-601.
Sura P, Penland C. 2002. Sensitivity of a double-gyre ocean model to details of stochastic forcing. Ocean Modelling, 4(3-4):327-345.
Van Scheltinga A D T, Dijkstra H A. 2008. Conditional nonlinear optimal perturbations of the double-gyre ocean circulation. Nonlinear Processes in Geophysics, 15(5):727-734.
Wen S, Yuan S, Mu B, et al. 2015. PCGD:Principal components-based great deluge method for solving CNOP. IEEE Congress on Evolutionary Computation, (CEC), 1 513-1 520.
White M A, Thornton P E, Running S W et al. 2000.Parameterization and sensitivity analysis of the BIOMEBGC terrestrial ecosystem model:net primary production controls. Earth Interactions, 4(3):1-85.
Wilkin J L, Arango H G, Haidvogel D B et al. 2005. A regional ocean modeling system for the Long-term Ecosystem Observatory. Journal of Geophysical Research:Oceans, 110(C6):C06S91.
Yin X D, Wang B, Liu J J et al. 2014. Evaluation of conditional non-linear optimal perturbation obtained by an ensemblebased approach using the Lorenz-63 model. Tellus Series A:Dynamic Meteorology & Oceanography, 66(2):116-118.
Yuan S J, Li M, Mu B, et al. 2016. PCAFP for solving CNOP in double-gyre variation and its parallelization on clusters.In:2016 IEEE 18th International Conference on High Performance Computing and Communications, Sydney, NSW, Australia. p.284-291.
Yuan S J, Yan J H, Mu B et al. 2015b. Parallel dynamic step size sphere-gap transferring algorithm for solving conditional nonlinear optimal perturbation.:2015 17th, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, New York, NY, USA. p.559-565.
Yuan S, Qian Y, Mu B. 2015a. Paralleled continuous Tabu search algorithm with sine maps and staged strategy for solving CNOP. In:Wang G, Zomaya A, Martinez G et al.eds. Springer, Cham.
Zaehle S, Sitch S, Smith B et al. 2005. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochemical Cycles, 19(3):GB3020.
Zhang K, Mu M, Wang Q. 2015. The impact of initial error on predictability of Double-gyre variability. Marine Science, 39(5):120-128. (in Chinese)
Zhang L L, Yuan S J, Mu B, et al. 2017. CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method. Asia-Pacific Journal of Atmospheric Sciences, 53(1):63-73.