Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme

The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

On the role of wave breaking in ocean dynamics under typhoon Matsa in the Bohai Sea,China

The role of wave breaking(WB) in the ocean dynamics in the Bohai Sea,China under typhoon condition is systematically investigated utilizing a coupled wave-current model.The influences of WB on ocean dynamics and processes(mixing coefficient,temperature,mixed layer depth,and current) during the entire typhoon period(including the pre-typhoon,during-typhoon and after-typhoon stages) are comprehensively detected and discussed.Experimental results show that WB greatly enhances the turbulent mixing at about top 10 m depth under typhoon condition,the increase can be up to 10 times that of the normal weather.At the same time,WB generally strengthens the sea surface cooling by ~1.2°C at the during-typhoon stage,about 3 times that in normal weather.The mixed layer depth,is rapidly increased by ~1.6–3.6 m during typhoon due to WB,particularly,the deepening is stronger in the region from 120.5°E to 121.0°E on account of close to the typhoon eye.In addition,WB renders the current speed more uniformly within the entire depth in the Bohai Sea,the change in speed is ~0.2 m/s,whereas the alternation in current vector is generally opposite to the wind direction except for the typhoon eye region,reflecting that WB has an inhibitory effect on the typhoon-forced current change.The effects of WB on vertical mixing coefficient response to the typhoon rapidly,while the impacts of WB on temperature,and mixed layer depth present hysteretic responses to typhoon.Finally,the mechanisms and distribution characteristics of WB-induced mixing and tidal mixing are compared under typhoon condition.