Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.3
Journals
Atmosphere
Special Issues
Atmospheric Modeling with Artificial Intelligence Technologies
share announcement

Atmospheric Modeling with Artificial Intelligence Technologies

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1510

Special Issue Editors

Dr. Wei Tang

E-Mail
Guest Editor
Atmospheric Environment Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Interests: air quality modeling; inverse model; satellite data; source apportionment
Special Issues, Collections and Topics in MDPI journals
Dr. Fan Meng

E-Mail Website
Guest Editor
Asia Center for Air Pollution Research, Niigata 950-2144, Japan
Interests: atmospheric modeling and forecasting; emission inventory; acid rain; source apportionment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High economic growth in recent years, along with fast industrialization, urbanization, and motorization, as well as agricultural activities, has enhanced emissions, resulting in the degradation of air quality and negative impacts on human health and climate. Applications of artificial intelligence (AI) techniques recently, such as big data processing and analysis, machine learning, and deep learning, can handle large volumes of atmospheric data from various sources, identify and correct errors or missing values, and therefore improve model performance and accuracy significantly. The aim of this Special Issue is to provide recent research activities in the field of atmospheric modeling that incorporate big data and deep learning technologies, including meteorological and air quality predictions, source apportionment modeling, the parameterization of atmospheric physical and chemical processes, and uncertainty quantification and inverse modeling.

Topics of interest for the Special Issue include, but are not limited to, the following:

  • Air quality modeling;
  • Source apportionment analysis and control policies;
  • The applications of big data in air quality analysis;
  • The applications of big data and deep learning technologies in atmospheric modeling.

Dr. Wei Tang
Dr. Fan Meng
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • air quality modeling and prediction
  • source apportionment
  • inverse modeling
  • big data analysis
  • deep learning technology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 12578 KB  
Article
Real-Time Production of High-Resolution, Gap-Free, 3-Hourly AOD over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, and Air Quality Data
by Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim and Yangwon Lee
Atmosphere 2026, 17(1), 19; https://doi.org/10.3390/atmos17010019 - 24 Dec 2025
Abstract
Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) [...] Read more.
Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved a mean absolute error (MAE) of 0.075 and a correlation coefficient (R) of 0.841 during the 2024 independent validation, significantly outperforming the original CAMS forecast. The system demonstrated robust and consistent performance across varying land cover types, seasons, and AOD conditions, from clean to highly polluted. Empirical orthogonal function (EOF) analysis confirmed that the product successfully captures physically meaningful spatiotemporal patterns, including transboundary pollution transport, regional emission gradients, and topographic effects. Providing real-time, gap-free, 3-hourly daytime AOD, the proposed model overcomes the limitations of cloud-induced gaps in satellite data and the latency and coarseness of reanalysis products. This enables robust operational monitoring and aerosol research across the Korean Peninsula. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
►▼ Show Figures

Graphical abstract

17 pages, 38734 KB  
Article
DSMF-Net: A Spatiotemporal Memory Flow Network for Long-Range Prediction of Stratospheric Sudden Warming Events
by Xiao Ma, Fengmei Zhao, Bin Yue and Xinshuang Liu
Atmosphere 2025, 16(12), 1316; https://doi.org/10.3390/atmos16121316 - 21 Nov 2025
Viewed by 291
Abstract
Sudden Stratospheric Warmings (SSWs) are extreme polar atmospheric disturbances that significantly impact mid-latitude cold surges, but their early prediction remains a challenge for conventional numerical models. In this study, we propose a video prediction framework for SSW forecasting and introduce a Decoupled Spatiotemporal [...] Read more.
Sudden Stratospheric Warmings (SSWs) are extreme polar atmospheric disturbances that significantly impact mid-latitude cold surges, but their early prediction remains a challenge for conventional numerical models. In this study, we propose a video prediction framework for SSW forecasting and introduce a Decoupled Spatiotemporal Memory Flow Network (DSMF-Net) to more effectively capture the dynamic evolution of stratospheric polar vortices. DSMF-Net separates spatial and temporal dependencies using specialized memory flow modules, enabling fine-grained modeling of vortex morphology and dynamic transitions. Experiments on representative SSW events from 2018 to 2021 show that DSMF-Net can reliably predict SSW occurrences up to 20 days in advance while accurately replicating the evolution of polar vortex structures. Compared to baseline models such as the Predictive Recurrent Neural Network (PredRNN) and Motion Recurrent Neural Network (MotionRNN), our method achieves consistent improvements across various metrics, with average gains of 10.5% in Mean Squared Error (MSE) and 6.4% in Mean Absolute Error (MAE) and a 0.7% increase in the Structural Similarity Index Measure (SSIM). These findings underscore the potential of deep video prediction frameworks to improve medium-range stratospheric forecasts and bridge the gap between data-driven models and atmospheric dynamics. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
►▼ Show Figures

Figure 1

32 pages, 7263 KB  
Article
Time Series Prediction and Modeling of Visibility Range with Artificial Neural Network and Hybrid Adaptive Neuro-Fuzzy Inference System
by Okikiade Adewale Layioye, Pius Adewale Owolawi and Joseph Sunday Ojo
Atmosphere 2025, 16(8), 928; https://doi.org/10.3390/atmos16080928 - 31 Jul 2025
Viewed by 807
Abstract
The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) [...] Read more.
The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R2), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R2 for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop