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Applications of Artificial Intelligence in Atmospheric Sciences
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Applications of Artificial Intelligence in Atmospheric Sciences

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 May 2025 | Viewed by 8077

Special Issue Editors

Dr. Erick G. Sperandio Nascimento

E-Mail Website
Guest Editor
Surrey Institute for People-Centred AI and Global Centre for Clean Air Research (GCARE), Institute for Sustainability, School of Computer Science and Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK
Interests: smart buildings; smart homes; indoor air quality; airborne dispersion; nature-based solutions; low-cost sensors; air monitoring; big data; artificial intelligence; computational modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Taciana Toledo De Almeida Albuquerque

E-Mail Website
Guest Editor
Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Antônio Carlos, 6.627, Belo Horizonte, MG 31270-901, Brazil
Interests: air pollution; air particulate matter; air quality; air quality modeling; air pollution control and modeling applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Prashant Kumar

grade E-Mail Website
Guest Editor
Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
Interests: low-cost sensing; air pollution modelling; pollution mitigation; atmospheric science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current state-of-the-art (SOTA) atmospheric models, such as numerical weather prediction (NWP) models, usually require large computing power since they rely on complex physical equations and parametrisations to simulate and understand spatiotemporal atmospheric phenomena. Currently, artificial intelligence (AI) techniques are used for this purpose with improved forecasting performance, but with a fraction of the computational cost of traditional techniques, leveraging large volumes of historical atmospheric data and advanced AI techniques to build atmospheric models for different spatiotemporal scales. In fact, even world-leading weather agencies, such as the MetOffice in the UK and the European Centre for Medium-Range Weather Forecasts (ECMWF), are developing AI solutions to improve atmospheric modelling performance, presenting competitive performance with SOTA NWP.

Therefore, this Special Issue aims to explore the intersection of AI and atmospheric sciences to tackle pressing challenges in climate change, weather forecasting, clean air, and renewable energy, among others, providing a platform for researchers to showcase cutting-edge research and to foster the development and adoption of AI solutions to address key challenges in atmospheric sciences, with the potential to help achieve the United Nation’s Sustainable Development Goals (UNSDG) 3, 7, 11, and 13. Authors are invited to submit original research articles and reviews that highlight the transformative potential of novel AI techniques in various aspects of atmospheric sciences, including (but not limited to) the following:

  • Weather and extreme weather event forecasting;
  • Air pollution monitoring, management, and forecasting;
  • Renewable energy prediction and optimisation;
  • Regional downscaling;
  • Physics-informed neural networks to simulate atmospheric flow;
  • Foundation models for atmospheric challenges;
  • Climate change and resilience;
  • Indoor and outdoor modelling;
  • The airborne dispersion of contaminants and their impact on indoor and outdoor environments;
  • The inventory estimation of emissions;
  • Land use change assessment;
  • Impacts of air quality on human health;
  • Other related areas.

Dr. Erick G. Sperandio Nascimento
Dr. Taciana Toledo De Almeida Albuquerque
Prof. Dr. Prashant Kumar
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.

Keywords

  • artificial intelligence
  • atmospheric science
  • machine learning
  • deep learning
  • climate change
  • air pollution
  • clean air
  • renewable energy
  • clean energy
  • weather
  • extreme weather
  • physics-informed neural networks

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

Published Papers (6 papers)

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Research

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19 pages, 1296 KiB  
Article
MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting
by Shaohan Li, Min Chen, Lu Yi, Qifeng Lu and Hao Yang
Atmosphere 2025, 16(1), 67; https://doi.org/10.3390/atmos16010067 - 9 Jan 2025
Viewed by 345
Abstract
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as [...] Read more.
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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21 pages, 5946 KiB  
Article
A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0)
by Jianbin Zhang, Meng Yin, Pu Wang and Zhiqiu Gao
Atmosphere 2024, 15(10), 1229; https://doi.org/10.3390/atmos15101229 - 15 Oct 2024
Cited by 1 | Viewed by 1457
Abstract
In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism [...] Read more.
In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism (AM). The CNN is tasked with extracting features from the input data, while the BiLSTM effectively captures temporal dependencies. The AM enhances the results by considering the impact of past feature states on severe weather phenomena. Additionally, we assess the performance of our model in comparison to traditional network architectures, including ConvLSTM, Predrnn++, CNN, FC-LSTM, and LSTM. Our results indicate that the CNN-BiLSTM-AM model exhibits superior accuracy in precipitation forecasting. Especially with the extension of the forecast time, the model performs well across multiple evaluation metrics. Furthermore, an interpretability analysis of the convective weather mechanisms utilizing machine learning highlights the critical role of total precipitable water (PWAT) in short-term heavy precipitation forecasts. It also emphasizes the significant impact of regional variables on convective weather patterns and the role of convective available potential energy (CAPE) in fostering conditions conducive to convection. These findings not only confirm the effectiveness of deep learning in the automatic identification of severe weather features but also validate the suitability of the sample dataset employed. Given its remarkable performance and robustness, we advocate for the adoption of this model to enhance the forecast of severe convective weather across various business applications. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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21 pages, 16146 KiB  
Article
Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model
by Leyi Wang, Yiming Wang, Xiaoyu Hu, Hui Wang and Ruilin Zhou
Atmosphere 2024, 15(10), 1219; https://doi.org/10.3390/atmos15101219 - 12 Oct 2024
Viewed by 798
Abstract
Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochastic nature of moist physics, which can degrade the simulation of large-scale circulations, climate [...] Read more.
Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochastic nature of moist physics, which can degrade the simulation of large-scale circulations, climate means, and variability. To address this issue, a stochastic parameterization scheme called DIFF-MP, based on a probabilistic diffusion model, is developed. Cloud-resolving data are coarse-grained into resolved-scale variables and subgrid contributions, which serve as conditional inputs and outputs for DIFF-MP. The performance of DIFF-MP is compared with that of generative adversarial networks and variational autoencoders. The results demonstrate that DIFF-MP consistently outperforms these models in terms of prediction error, coverage ratio, and spread–skill correlation. Furthermore, the standard deviation, skewness, and kurtosis of the subgrid contributions generated by DIFF-MP more closely match the test data than those produced by the other models. Interpretability experiments confirm that DIFF-MP’s parameterization of moist physics is physically consistent. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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27 pages, 18384 KiB  
Article
Calibration of Typhoon Track Forecasts Based on Deep Learning Methods
by Chengchen Tao, Zhizu Wang, Yilun Tian, Yaoyao Han, Keke Wang, Qiang Li and Juncheng Zuo
Atmosphere 2024, 15(9), 1125; https://doi.org/10.3390/atmos15091125 - 17 Sep 2024
Viewed by 1217
Abstract
An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by [...] Read more.
An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model’s spatio-temporal feature-capturing ability. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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14 pages, 29945 KiB  
Article
Improving Air Quality Prediction via Self-Supervision Masked Air Modeling
by Shuang Chen, Li He, Shinan Shen, Yan Zhang and Weichun Ma
Atmosphere 2024, 15(7), 856; https://doi.org/10.3390/atmos15070856 - 19 Jul 2024
Viewed by 991
Abstract
Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in [...] Read more.
Presently, the harm to human health created by air pollution has greatly drawn public attention, in particular, vehicle emissions including nitrogen oxides as well as particulate matter. How to predict air quality, e.g., pollutant concentration, efficiently and accurately is a core problem in environmental research. Developing a robust air quality predictive model has become an increasingly important task, holding practical significance in the formulation of effective control policies. Recently, deep learning has progressed significantly in air quality prediction. In this paper, we go one step further and present a neat scheme of masked autoencoders, termed as masked air modeling (MAM), for sequence data self-supervised learning, which addresses the challenges posed by missing data. Specifically, the front end of our pipeline integrates a WRF-CAMx numerical model, which can simulate the process of emission, diffusion, transformation, and removal of pollutants based on atmospheric physics and chemical reactions. Then, the predicted results of WRF-CAMx are concatenated into a time series, and fed into an asymmetric Transformer-based encoder–decoder architecture for pre-training via random masking. Finally, we fine-tune an additional regression network, based on the pre-trained encoder, to predict ozone (O 3) concentration. Coupling these two designs enables us to consider the atmospheric physics and chemical reactions of pollutants while inheriting the long-range dependency modeling capabilities of the Transformer. The experimental results indicated that our approach effectively enhances the WRF-CAMx model’s predictive capabilities and outperforms pure supervised network solutions. Overall, using advanced self-supervision approaches, our work provides a novel perspective for further improving air quality forecasting, which allows us to increase the smartness and resilience of the air prediction systems. This is due to the fact that accurate prediction of air pollutant concentrations is essential for detecting pollution events and implementing effective response strategies, thereby promoting environmentally sustainable development. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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Review

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35 pages, 773 KiB  
Review
Machine Learning Methods for Weather Forecasting: A Survey
by Huijun Zhang, Yaxin Liu, Chongyu Zhang and Ningyun Li
Atmosphere 2025, 16(1), 82; https://doi.org/10.3390/atmos16010082 - 14 Jan 2025
Viewed by 1681
Abstract
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning [...] Read more.
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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