The K-profile Parameterization augmented by Deep Neural Networks (KPP_DNN) in the General Ocean Turbulence Model (GOTM)
Jianguo Yuan,
Jun-Hong Liang,
Eric P. Chassignet
et al.
Abstract:This study utilizes Deep Neural Networks (DNN) to improve the K-Profile
Parameterization (KPP) for the vertical mixing effects in the ocean’s
surface boundary layer turbulence. The DNNs were trained using 11-year
turbulence-resolving solutions, obtained by running a large eddy
simulation model for Ocean Station Papa, to predict the turbulence
velocity scale coefficient and unresolved shear coefficient in the KPP.
The DNN-augmented KPP schemes (KPP_DNN) have been implemented in the
General Ocean Turbulence Mode… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.