Physically Constrained Adaptive Deep Learning for Ocean Vertical-Mixing Parameterization  

作　　者：Junjie FANG Xiaojie LI Jin LI Zhanao HUANG Yongqiang YU Xiaomeng HUANG Xi WU 

机构地区：[1]Chengdu University of Information Technology,Chengdu 610225,China [2]Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [3]Tsinghua University,Beijing 100084,China

出　　处：《Advances in Atmospheric Sciences》2025年第1期165-177,共13页大气科学进展(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142);the National Key Research and Development Program of China(Grant No.2020YFA0608000);the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。

摘　　要：Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.

关 键 词：deep learning vertical-mixing parameterization ocean sciences adaptive network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P731.26[自动化与计算机技术—控制科学与工程]