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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 1176

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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Published Papers (2 papers)

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Research

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 294
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 501
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Fusion of In-Situ and Modelled Marine Data for Enhanced Coastal Dynamics Prediction along the Romanian Black Sea Coast
Authors: Emanuela Mihailov, et al.
Abstract: This research investigates the potential of Artificial Intelligence (AI) and Machine Learning (ML) techniques for enhancing the understanding and prediction of coastal dynamics along the Romanian Black Sea coast. We aim to bridge the gap between in-situ observations from five meteo-oceanographic stations and modelled geospatial marine data from Copernicus Marine Service (https://marine.copernicus.eu). Correlation analysis is employed to evaluate the confidence level between measured thermodynamic parameters and modelled data, informing the development of ML-based correlation systems that link Black Sea dynamic parameters to atmospheric conditions. The main objective is to refine predictions of shallow current dynamics by estimating correction coefficients and considering atmospheric influences. Further, ML models are executed to predict corrections for dynamic vectors based on atmospheric data, focusing on wave-wind correlations in coastal regions. Atmospheric pressure and temperature are treated as latitude-dependent constants, with specific investigations into extreme events like freezing and solar radiation-induced turbulence. Advanced regression techniques, time series analysis, and Explainable AI (XAI) are exploited to ensure accurate predictions and transparent model interpretations. Data attribution strategies address missing data concerns, while ensemble modelling enhances overall prediction robustness. Anticipated outcomes include a deeper understanding of atmosphere-marine interactions, improved accuracy in coastal dynamics predictions (crucial for maritime safety and coastal management), and demonstration of AI/ML's efficacy in bridging observational and modelled data gaps for informed coastal zone management decisions.
Keywords: Artificial intelligence (AI); Machine learning (ML); Coastal dynamics; Black Sea; Shallow current prediction.

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