Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences

Dear Colleagues,

In recent years, machine learning (ML) algorithms have been widely used in predictive modeling of different research domains of science and engineering. ML algorithms design an automated, accurate, and robust decision-making system by extracting the meaningful conclusion from the observations. ML algorithms have been implemented successfully in predictive modeling applications of atmospheric sciences in past research, such as circulation pattern classification, risk assessment of atmospheric emissions, atmospheric rive forecast, prediction of geothermal heat flux, diagnosis of cold atmospheric plasma sources, turbulence forecasting, hazard assessment, and forecasting of rainfall changes, etc. Due to the growing demand for ML in most of the aspect of our life, it is sensible to use it in enhancing the prediction efficiency in predictive modeling of atmospheric sciences research and applications. Extracting the meaningful conclusion by using the advanced ML approaches in the analysis of the experimental and simulated observations of atmospheric phenomena is the demanding research at present. With this objective, the present issue invites researchers to submit their novel and unpublished research related to the current advancement of ML in predictive modeling research and applications of atmospheric sciences. The present issue will cover a broad range of topics related to applications of ML approaches in the analysis of atmospheric data with the following subtopics.

• Climate change modeling using machine learning
• Machine learning in meteorology and hydrology applications
• Role of machine learning in renewable energy
• Analysis of data of atmospheric events in following subtopics but not limited
  • Data assimilation
  • Missing value imputation, preprocessing, and denoising
  • Outlier detection and removal
  • Feature extraction and selection
  • Classification and clustering
  • Simulation, modeling, and optimization
  • Reliability analysis
• Big data in atmospheric sciences and its analysis
• Transfer and deep learning in predictive modeling in atmospheric sciences
• Intelligent forecasting in atmospheric sciences
• Reinforcement and ensemble learning uses in atmospheric sciences
• Predictive modeling in atmospheric sciences using evolutionary approaches
• Hybrid ML approaches in efficient modeling of events of atmospheric sciences
• Other advanced ML approaches and tools in atmospheric data modeling and applications.

Dr. Sunil Kumar Jha
Dr. Xiaorui Zhang
Dr. Limao Zhang
Dr. Nilesh Patel
Guest Editors

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