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 Nonl...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.展开更多
In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we si...In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show:(1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time;(2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.展开更多
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘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.
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show:(1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time;(2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.
