Machine learning applications for atmosphere and ocean sciences

Introduction:

The main aim of the field of atmospheric and oceanic sciences (AOS) is to describe, understand, represent and simulate the processes of the atmosphere and oceans, and, ultimately, to forecast related phenomena. Traditional physics-based approaches have achieved great success, reaching a stage where weather and climate can be forecast in advance, to some extent. However, considerable uncertainties and model biases in weather and climate predictions still exist. Recent advances in machine learning have led to its emergence as a powerful approach to solve problems in AOS, particularly by providing a data-driven approach to explore atmospheric and oceanic phenomena and processes. Indeed, machine learning techniques have already been widely applied to AOS, and have shown great potential for improving weather forecasts and climate predictions. As such, there is growing interest in integrating new machine learning techniques and traditional physics-based approaches for advancing AOS.

This special issue provides a unique opportunity for scientists to disseminate their recent research in the field of machine learning for AOS. We welcome submissions on machine learning techniques and/or their hybrid combinations with traditional approaches in improving weather forecasts and climate predictions across various spatiotemporal scales, and in understanding and reducing their uncertainties and model biases.  

Topics covered:

These include, but are not limited to:

• Data-driven machine learning algorithms for AOS
• Physics-informed machine learning for parameterisations
• Machine learning–based weather forecasts and climate predictions
• Data-driven and physics-inferred fusion and hybrid approaches
• Machine learning applications for atmospheric and oceanic signal processing

Important deadlines:

• Submission deadline: 30 November 2022

Submission Instructions:

Please read the Guide for Authors before submitting. All submissions should be made online; please select the manuscript type “Special Issue: Machine learning for atmospheric and ocean sciences”. Questions can be emailed to the AOSL editorial office at aosl@mail.iap.ac.cn.

Editors:

Lead Editor:

• Rong-Hua Zhang, Nanjing University of Information Science & Technology, China.Email: rzhang@qdio.ac.cn; 003556@nuist.edu.cn

Guest Editors:

• Prof. Jing-Jia Luo, Nanjing University of Information Science & Technology, China. Email: jjluo@nuist.edu.cn
• Prof. Qingshan Liu, Nanjing University of Information Science & Technology, China. Email: qsliu@nuist.edu.cn