In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameter...In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameterizations for the eddy-topography interaction （ETI）. The strong deep mean circulations observed in the EIS are well reproduced when using the ETI parameterizations. The seasonal variability in the EIS deep layer is shown by using ETI parameterization based on the potential vorticity approach, while it is not shown in the statistical dynamical parameterization. The driving mechanism of the strong deep mean currents in the E/S are discussed by investigating the effects of model grids and parameterizations. The deep mean circulation is more closely related to the baroclinic process and potential vorticity than it is to the wind driven circulation.展开更多
Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test ...Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.展开更多
基金The Research Program on Climate Change Adaptation(RECCA)of the Ministry of Education,Culture,Sports,Science and Technology(MEXT)of Japan
文摘In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameterizations for the eddy-topography interaction （ETI）. The strong deep mean circulations observed in the EIS are well reproduced when using the ETI parameterizations. The seasonal variability in the EIS deep layer is shown by using ETI parameterization based on the potential vorticity approach, while it is not shown in the statistical dynamical parameterization. The driving mechanism of the strong deep mean currents in the E/S are discussed by investigating the effects of model grids and parameterizations. The deep mean circulation is more closely related to the baroclinic process and potential vorticity than it is to the wind driven circulation.
基金supported by the National Key R&D Program of China(No.2016YFB1200203)the National Natural Science Foundation of China(Nos.41427806 and 61273233)
文摘Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.
