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Atmosphere
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Atmospheric Modeling with Artificial Intelligence Technologies
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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: closed (30 April 2026) | Viewed by 5440

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

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Published Papers (7 papers)

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Research

17 pages, 11454 KB  
Article
Informer-Based Precipitation Forecasting Using Ground Station Data in Guangxi, China
by Ting Zhang, Donghong Qin, Deyi Wang, Soung-Yue Liew and Huasheng Zhao
Atmosphere 2026, 17(5), 429; https://doi.org/10.3390/atmos17050429 - 22 Apr 2026
Viewed by 316
Abstract
Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this [...] Read more.
Precipitation forecasting is essential for disaster prevention, water resource management, and socio-economic resilience. The field has evolved from numerical weather prediction (NWP) and optical-flow-based methods toward data-driven deep learning approaches that can exploit larger observational datasets and model complex nonlinear relationships. Against this background, this study evaluates multi-station temporal forecasting models within a single-year, station-based proof-of-concept benchmark under unified data conditions. We adapt the Transformer and Informer architectures to this meteorological setting, rigorously preprocess the AWS dataset to avoid data leakage, and select predictive variables using complementary linear and nonlinear relevance criteria. Model performance is assessed using continuous and categorical precipitation metrics, including the Critical Success Index (CSI). The results show that the Informer outperforms the recurrent neural network (RNN) baselines and achieves the lowest mean MAE and RMSE together with the highest mean CSI among the evaluated models while using substantially fewer parameters than the standard Transformer. However, its sample-wise absolute error distribution remains statistically comparable to that of the standard Transformer. Overall, this study establishes a single-year, station-based proof-of-concept benchmark for comparing architectures in very-short-term (1–5 h ahead) precipitation forecasting. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
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22 pages, 3177 KB  
Article
Machine Learning-Based Prediction of High-Level Clouds: Integrating Meteorological Observations with Independent Lidar Validation
by Maxim Penzin, Konstantin Pustovalov, Olesia Kuchinskaia, Denis Romanov, Ivan Akimov and Ilia Bryukhanov
Atmosphere 2026, 17(4), 348; https://doi.org/10.3390/atmos17040348 - 30 Mar 2026
Viewed by 453
Abstract
This study develops a machine learning-based predictive model for identifying high-level clouds (HLCs). The model uses meteorological parameters as input features and is trained against human-recorded meteorological observations. A statistical analysis of the relationship between two independent methods of registering HLCs—lidar and meteorological [...] Read more.
This study develops a machine learning-based predictive model for identifying high-level clouds (HLCs). The model uses meteorological parameters as input features and is trained against human-recorded meteorological observations. A statistical analysis of the relationship between two independent methods of registering HLCs—lidar and meteorological observations—has been performed. Optimal thresholds for the total amount of cloud cover, at which meteorological data are consistent with lidar data, have been determined. The results demonstrate the promising performance of ML models in identifying the links between weather conditions and the probability of HLC detection, which is confirmed by ROC AUC (Area Under the Curve of the Receiver Operating Characteristic) values in the range of 0.87–0.88 for the presence and 0.77–0.78 for the absence of clouds, as well as balanced metrics Precision, Recall, and F1. The XGBoost (eXtreme Gradient Boosting) model proved to be the most robust, demonstrating the ability to effectively integrate data of various types for reliable prediction in various conditions. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
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24 pages, 3321 KB  
Article
On the Stable Integration of Neural Network Parameterization in Numerical Models
by Yifan Wang, Weizhi Huang, Hao Geng, Yi Ma and Leyi Wang
Atmosphere 2026, 17(3), 306; https://doi.org/10.3390/atmos17030306 - 17 Mar 2026
Viewed by 326
Abstract
Deep learning-based parameterizations of subgrid-scale processes have become a major research focus in recent years, offering the potential to remedy inaccuracies inherent in traditional physics-based schemes. However, their integral stability within numerical models remains insufficiently explored. In this study, we develop deep learning [...] Read more.
Deep learning-based parameterizations of subgrid-scale processes have become a major research focus in recent years, offering the potential to remedy inaccuracies inherent in traditional physics-based schemes. However, their integral stability within numerical models remains insufficiently explored. In this study, we develop deep learning parameterizations for the tropical cyclone boundary layer and implement them in the WRF model. We find that one-dimensional convolutional neural network fails to integrate stably, whereas a fully connected network succeeds. Further analysis shows that the limited receptive field of the convolutional network makes its outputs overly sensitive to certain input perturbations, ultimately causing integral instability. We examine three stabilization strategies—training data augmentation with Gaussian noise, spectral norm regularization, and L2 regularization—and find that all three methods effectively mitigate the network’s output sensitivity to input perturbations, enabling stable integration in WRF and yielding physically reasonable tropical cyclone simulations. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
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26 pages, 44003 KB  
Article
GLKC-Net: Group Large Kernel Convolution for Short-Range Precipitation Forecasting
by Jie Tan, Min Chen, Li Gao, Shaohan Li and Hao Yang
Atmosphere 2026, 17(3), 287; https://doi.org/10.3390/atmos17030287 - 12 Mar 2026
Viewed by 335
Abstract
Accurate short-range precipitation prediction plays a crucial role in daily life and disaster mitigation. However, the existing methods often suffer from inefficient large-scale feature extraction, severe redundant information interference, and insufficient attention to the problem of imbalanced data distributions, leading to unsatisfactory performance. [...] Read more.
Accurate short-range precipitation prediction plays a crucial role in daily life and disaster mitigation. However, the existing methods often suffer from inefficient large-scale feature extraction, severe redundant information interference, and insufficient attention to the problem of imbalanced data distributions, leading to unsatisfactory performance. To address these issues, in this paper, we first propose a novel spatiotemporal module called Group Large Kernel Convolution (GLKC) and develop a short-range precipitation forecasting model based on it, GLKC-Net, using multiple meteorological variables. Specifically, we use decomposed large-kernel convolution to enhance the ability to understand large-scale atmospheric processes. Meanwhile, we introduce the group convolution and channel shuffle operator to control the fusion of channel-wise information, enabling efficient information exchange and reducing redundancy in the channel dimension with multiple variables. Furthermore, we treat the causes of poor model performance for extreme precipitation events with an imbalanced data distribution perspective and design a Multi-threshold Adaptive Loss function (MTA Loss). This function strengthens the model’s focus on high-threshold precipitation events that are inherently difficult to forecast, aiming to improve model performance for extreme events. Finally, forecasting experiments for validation were conducted over southwestern China using ERA5-Land and CMPAS datasets. The results demonstrate that our proposed method outperforms several existing approaches in terms of forecasting accuracy. Full article
(This article belongs to the Special Issue Atmospheric Modeling with Artificial Intelligence Technologies)
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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
Viewed by 1298
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)
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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
Cited by 1 | Viewed by 627
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)
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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
Cited by 1 | Viewed by 1255
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)
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