Earth System Modeling, Data Assimilation, Artificial Intelligence, Deep Learning and Ocean Information Engineering

Dear Colleagues,

Numerous questions are emerging in this time of data bloom: Should the development of Artificial Intelligence and Deep Learning (AIDL) be driven by data or scientific models and information estimation theory? How can AIDL benefit more from as well as advance science and technology? A science-driven AIDL is evidently a heathy track. As a matter of fact, AIDL originated from our understanding of the natural world—mathematical modeling with dynamics and physics, data assimilation—with Bayes’ Theorem guiding combinations of models and data, as well as advanced deep neural network algorithms.

In this Special Issue, we call for papers that deal with recent advances in modeling, data assimilation, and parameter estimation, as well as AIDL-associated research and development in geoscience research and applications, including advanced modeling, data assimilation, and deep neural network algorithms, data mining in the Earth system, as well as advanced topics such as data-based parameter optimization, AIDL-induced parameterization, etc. We address the concept that science-driven AIDL development can help to improve our understanding of dynamics and physics, thus furthering the advances of science and technology. Potential topics include but are not limited to:

• Modeling, data assimilation, and parameter estimation;
• Bayes’ theorem-based AIDL algorithms;
• Data-assimilation-induced AIDL algorithms;
• Model parameter estimation and AIDL;
• Advanced deep neural network algorithms;
• Climate downscaling and evaluation with neural networks;
• AIDL-induced climate and chemistry modeling and parameterization;
• Advanced AIDL algorithms induced from modeling mesoscale to sub-mesoscale physical processes;
• AIDL-driven cloud and microphysics expressions;
• Data-based parameter optimization applied to AIDL algorithms;
• Data mining in the Earth system (e.g., optimal translation of native Earth system observations into user-specific information).

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

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