Ocean Climate: Deep Learning, Statistical Methods and Dynamical Modeling

Dear Colleagues,

In this data blooming time, outstanding challenges are to be resolved: How to combine data-driven machine deep learning (MDL) with science-driven dynamical models to advance the science? How can MDL gain benefits as well as advance science and technology? Apparently, science-driven MDL is a heathy track. In fact, MDL did originate from our understanding of the natural world—mathematical modeling for the dynamics and physics and data sampling for the state—Bayes’ Theorem guiding combination of models and data.

MDL refers to the theory and development of a computer system that simulates the laws governing the evolution of the natural system. The essence is a programmed machine that learns from experiences for making decisions and forming new experiences. The core of this process is the statistical analysis of data and event-relevant condition judgments. The amount of data and analysis methods are the basis of successful MDL.

Nowadays, MDL is playing increasingly important roles in almost every field. Particularly, in fields such as marine science and engineering, which have a strong applied nature, MDL is a very powerful tool, and its development and application have become hot topics. On the one hand, the dynamical model-based reanalysis extends data sources for MDL. On the other hand, high-precision dynamical modeling needs MDL to break through the bottlenecks brought by nonlinear feedback in detailed scale interactions.

In this Special Issue, we call for papers that deal with recent advances in machine deep learning, statistical methods and dynamical modeling associated with research and development in marine science and engineering research and applications, including advanced machine learning algorithms, new ideas in statistical methods and dynamical models, data assimilation, MDL-induced physical parameterization, etc. We address the concept that the science-driven MDL development returns to help further our understanding on dynamics and physics, thus further advancing science and technology. Potential topics include, but are not limited to, the following:

• Earth system modeling, data assimilation and parameter estimation;
• Mesoscale and submesoscale ocean processes;
• Bayes’ Theorem-based MDL algorithms;
• Data assimilation-induced MDL algorithms;
• MDL-induced new data assimilation algorithms;
• MDL-induced parameterization and parameter estimation;
• Advanced deep neural network algorithms and statistical methods;
• MDL-induced climate and chemistry modeling;
• Advanced multiscale MDL models;
• MDL-driven cloud and micro-physics expressions;
• Advanced dynamical modeling methods and schemes.

Prof. Dr. Shaoqing Zhang
Prof. Dr. Yuxin Zhao
Prof. Dr. Junyu Dong
Dr. Hao Zuo
Dr. Chang Liu
Guest Editors

Manuscript Submission Information

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• climate modeling
• data assimilation
• deep learning
• artificial intelligence
• ocean information engineering