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

Abstract

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.