Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent...Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent, and the Kolmogorov en- tropy, are estimated in the reconstructed phase space. The results indicate that the reverberation signals are nonlinear. The Volterra adaptive prediction method is introduced to model the oceanic reverberation signals. The reverberation time series can be predicted in short term with small prediction errors. A preliminary conclusion can be reached that the nonlinear low-dimensional dynamic sys- tem model is more suitable for modeling oceanic reverberation than the classical random AR model.展开更多
Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regio...Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regional Ocean Modeling System(ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy(TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained:(1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable.(2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.展开更多
文摘Acoustic reverberation signals generated by an experimental explosive source are analyzed by nonlinear dynamical methods. Three characteristic parameters, i.e., the correlation dimension, the largest Lyapunov exponent, and the Kolmogorov en- tropy, are estimated in the reconstructed phase space. The results indicate that the reverberation signals are nonlinear. The Volterra adaptive prediction method is introduced to model the oceanic reverberation signals. The reverberation time series can be predicted in short term with small prediction errors. A preliminary conclusion can be reached that the nonlinear low-dimensional dynamic sys- tem model is more suitable for modeling oceanic reverberation than the classical random AR model.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010303)the National Natural Science Foundation of China (Grant Nos. 41230420, 41306023 & 41421005)+1 种基金the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401)the support of K. C. Wong Foundation
文摘Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regional Ocean Modeling System(ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy(TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained:(1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable.(2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.
