Novel Algorithms and Advanced Computing Methods Application in Atmosphere

Dear Colleagues,

This Special Issue will feature the latest advances and developments in sustainable atmospheric management. The focus is centered on advanced computing methods and new optimization algorithm methods for forecasting atmospheric variables to achieve an optimal sustainable atmosphere. The current computational power available allows us to tackle simulation challenges in atmospheric modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, the design or optimization of hydraulic structures, the calibration of model parameters, uncertainty quantification, and real-time model-based control. New algorithms and advanced computing methods are useful in the prediction requirements of atmospheric data, including atmospheric river prediction, the risk prediction of atmospheric emissions, turbulence and hazard prediction, the class prediction of atmospheric circulation patterns, the prediction of geothermal heat flux, air quality monitoring, rainfall prediction, atmospheric aerosol prediction, global weather prediction systems, the prediction of the influence of atmospheric parameters on human health, etc.

The main themes of this Special Issue include but are not limited to the following: 

• Application of advanced computing methods, including machine learning and deep learning, for precise atmospheric variable forecasting (modeling rainfall, air quality, flood, atmospheric aerosol prediction, solar radiation, wind speed, air temperature, evaporation, evapotranspiration, etc.).
• Utilization of advanced machine learning and deep learning models with ensemble models for solving atmospheric problems.
• Spatial and temporal modeling of atmospheric variables with the aid of advanced computing models.
• Coupling of data preprocessing techniques with machine learning and deep learning methods to capture noise and nonlinear atmospheric variables.
• Usage and development of novel optimization algorithms with machine learning methods to enhance their computing abilities.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Dr. Mo Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• advanced computing methods
• machine learning
• deep learning
• algorithms
• rainfall
• air quality
• flood
• atmospheric aerosol prediction
• solar radiation
• wind speed
• air temperature
• evaporation
• evapotranspiration