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Atmosphere
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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: 31 October 2024 | Viewed by 424

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

Published Papers (1 paper)

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Research

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 217
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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