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Atmosphere
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Novel Algorithms and Advanced Computing Methods Application in Atmosphere
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Novel Algorithms and Advanced Computing Methods Application in Atmosphere

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

Deadline for manuscript submissions: 22 September 2025 | Viewed by 3607

Special Issue Editors

Dr. Rana Muhammad Adnan

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: sustainable development; water resources management; hydrological modeling; artificial intelligence; time series analysis; rainfall–runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydro-meteorological droughts; groundwater; water quality parameters modeling; novel meta-heuristic approaches applications; trend analysis; clustering; watershed planning and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ozgur Kisi

grade E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting; estimating; spatial and temporal analysis of hydro-climatic variables such as precipitation; streamflow; suspended sediment; evaporation; evapotranspiration; groundwater; lake level and water quality parameters; hydro-informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Mo Wang

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Guangzhou University, Guangzhou, China
Interests: green infrastructure; low impact development; landscape planning; stormwater management; nature-based solution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will feature the latest advances and developments in sustainable atmospheric management. The focus is centered on advanced computing methods and new optimization algorithm methods for forecasting atmospheric variables to achieve an optimal sustainable atmosphere. The current computational power available allows us to tackle simulation challenges in atmospheric modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, the design or optimization of hydraulic structures, the calibration of model parameters, uncertainty quantification, and real-time model-based control. New algorithms and advanced computing methods are useful in the prediction requirements of atmospheric data, including atmospheric river prediction, the risk prediction of atmospheric emissions, turbulence and hazard prediction, the class prediction of atmospheric circulation patterns, the prediction of geothermal heat flux, air quality monitoring, rainfall prediction, atmospheric aerosol prediction, global weather prediction systems, the prediction of the influence of atmospheric parameters on human health, etc.

The main themes of this Special Issue include but are not limited to the following: 

  • Application of advanced computing methods, including machine learning and deep learning, for precise atmospheric variable forecasting (modeling rainfall, air quality, flood, atmospheric aerosol prediction, solar radiation, wind speed, air temperature, evaporation, evapotranspiration, etc.).
  • Utilization of advanced machine learning and deep learning models with ensemble models for solving atmospheric problems.
  • Spatial and temporal modeling of atmospheric variables with the aid of advanced computing models.
  • Coupling of data preprocessing techniques with machine learning and deep learning methods to capture noise and nonlinear atmospheric variables.
  • Usage and development of novel optimization algorithms with machine learning methods to enhance their computing abilities.

Dr. Rana Muhammad Adnan
Prof. Dr. Ozgur Kisi
Dr. Mo Wang
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

  • advanced computing methods
  • machine learning
  • deep learning
  • algorithms
  • rainfall
  • air quality
  • flood
  • atmospheric aerosol prediction
  • solar radiation
  • wind speed
  • air temperature
  • evaporation
  • evapotranspiration

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 20169 KiB  
Article
PM2.5 Concentration Prediction in the Beijing–Tianjin–Hebei Region Based on ERA5 Stratified PWV and Atmospheric Pollutants
by Jun Shang, Peixuan Zhang, Yong Wang, Yanping Liu, Hongsheng Wang and Suo Li
Atmosphere 2025, 16(3), 269; https://doi.org/10.3390/atmos16030269 - 25 Feb 2025
Viewed by 254
Abstract
Accurate prediction of PM2.5 (particle pollution from fine particulate) concentration is crucial for environmental protection and public health. Precipitable water vapor (PWV) in the atmosphere is an important meteorological element with stratification properties, which plays a crucial role in energy transfer, weather [...] Read more.
Accurate prediction of PM2.5 (particle pollution from fine particulate) concentration is crucial for environmental protection and public health. Precipitable water vapor (PWV) in the atmosphere is an important meteorological element with stratification properties, which plays a crucial role in energy transfer, weather dynamics, and PM2.5 generation. However, past studies tend to use total PWV as an input parameter, neglecting the impact of PWV variations in different altitude layers on PM2.5 concentration. To overcome this limitation, this study proposes an innovative approach that employs stratified water vapor data (ERA5-PWV) calculated from the ERA5 reanalysis data instead of the total PWV obtained using the traditional method. This approach provides a more accurate representation of the vertical distribution of atmospheric PWV and enhances the prediction of PM2.5 content. In this study, the stratified ERA5 PWV in the Beijing–Tianjin–Hebei region is integrated with other meteorological elements and atmospheric pollutants, and the FFT-ConvLSTM method, characterized by its spatio-temporal properties, is utilized to predict the PM2.5 concentration by incorporating the spatio-temporal correlation. The FFT-ConvLSTM model is modeled by extracting spatio-temporal features through ConvLSTM, following the identification of the optimal common change period of each element using the FFT technique. This process mitigates the problem of spatio-temporal heterogeneity among elements, thus, realizing the high-precision prediction of gridded PM2.5 concentration in the next 24 h. The research results show that among the results of different layers of ERA5-PWV combinations involved in the prediction of PM2.5 concentrations in the research region, divided into three parts of the research region—plains, mountains, and plateaus—the stratified ERA5-PWV from layers 1–4 with pressure levels consistently outperformed the total ERA5-PWV in accuracy, and the RMSEs of the predicted results for the PM2.5 concentrations were each reduced by 0.862 μg/m3, 5.384 μg/m3 and 1.706 μg/m3. Full article
(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)
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21 pages, 4061 KiB  
Article
Development of a Hybrid Attention Transformer for Daily PM2.5 Predictions in Seoul
by Hyun S. Kim, Kyung M. Han, Jinhyeok Yu, Nara Youn and Taehoo Choi
Atmosphere 2025, 16(1), 37; https://doi.org/10.3390/atmos16010037 - 1 Jan 2025
Cited by 2 | Viewed by 824
Abstract
A hybrid attention transformer (HAT) was developed for accurate daily PM2.5 predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport model (3-D CTM). The [...] Read more.
A hybrid attention transformer (HAT) was developed for accurate daily PM2.5 predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport model (3-D CTM). The results demonstrated that the HAT outperformed the 3-D CTM, achieving a 4.60% higher index of agreement (IOA). Additionally, the HAT exhibited 22.09% fewer errors and 82.59% lower bias compared to the 3-D CTM. Diurnal variations in PM2.5 predictions from both models were also analyzed to explore the characteristics of the proposed model further. The HAT predictions closely aligned with observed PM2.5 throughout the day, whereas the 3-D CTM exhibited significant diurnal variability. The importance of the input features was evaluated using the permutation method, which revealed that the previous day’s PM2.5 was the most influential feature. The robustness of the HAT was further validated through a comparison with the long short-term memory (LSTM) model, which showed 18.50% lower errors and 95.91% smaller biases, even during El Niño events. These promising findings highlight the significant potential of the HAT as a cost-effective and highly accurate tool for air quality prediction. Full article
(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)
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30 pages, 21306 KiB  
Article
Enhanced Sequence-to-Sequence Attention-Based PM2.5 Concentration Forecasting Using Spatiotemporal Data
by Baekcheon Kim, Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jinyong Kim and Sungshin Kim
Atmosphere 2024, 15(12), 1469; https://doi.org/10.3390/atmos15121469 - 9 Dec 2024
Viewed by 869
Abstract
Severe air pollution problems continue to increase because of accelerated industrialization and urbanization. Specifically, fine particulate matter (PM2.5) causes respiratory and cardiovascular diseases, and according to the World Health Organization (WHO), millions of premature deaths and significant health burdens annually. [...] Read more.
Severe air pollution problems continue to increase because of accelerated industrialization and urbanization. Specifically, fine particulate matter (PM2.5) causes respiratory and cardiovascular diseases, and according to the World Health Organization (WHO), millions of premature deaths and significant health burdens annually. Therefore, PM2.5 concentration forecasting is essential. This study proposed a method to forecast PM2.5 concentrations one hour after using Sequence-to-Sequence Attention (Seq2Seq-attention). The proposed method selects neighboring stations using minimum redundancy maximum relevance (mRMR) and integrates their data using a convolutional neural network (CNN). The proposed attention score and Seq2Seq are used on the integrated data to forecast PM2.5 concentration after one hour. The performance of the proposed method is validated through two case studies. The first comparison evaluated the performance of the conventional attention score against the proposed attention scores. The second comparison evaluated the forecasting results with and without considering neighboring stations. The first study showed that the proposed attention score improved the performance index (Root Mean Square Error (RMSE): 3.48%p, Mean Absolute Error (MAE): 8.60%p, R2: 0.49%p, relative Root Mean Square Error (rRMSE): 3.64%p, Percent Bias (PBIAS): 59.29%p). The second case study showed that considering neighboring stations’ data can be more effective in forecasting than considering that of a standalone station (RMSE: 5.49%p, MAE: 0.51%p, R2: 0.67%p, rRMSE: 5.44%p, PBIAS: 46.56%p). This confirmed that the proposed method can effectively forecast the PM2.5 concentration after one hour. Full article
(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)
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25 pages, 4564 KiB  
Article
Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds
by Rana Muhammad Adnan, Wang Mo, Ozgur Kisi, Salim Heddam, Ahmed Mohammed Sami Al-Janabi and Mohammad Zounemat-Kermani
Atmosphere 2024, 15(12), 1407; https://doi.org/10.3390/atmos15121407 - 22 Nov 2024
Cited by 4 | Viewed by 1006
Abstract
Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The [...] Read more.
Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R2 (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article
(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)
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