Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Atmosphere
Special Issues
Data Analysis in Atmospheric Research
share announcement

Data Analysis in Atmospheric Research

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 (17 April 2025) | Viewed by 4590

Special Issue Editors

Dr. Zhaoxin Dai

E-Mail Website
Guest Editor
Chinese Academy of Surveying and Mapping, Beijing 100830, China
Interests: geospatial big data and machine learning; air quality simulation; spatial econometrics model; urban studies
Dr. Qi Zhou

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data fusion; analysis of big data; spatial data deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: aerosols; trace gas; satellite remote sensing; inversing modeling of emissions

Special Issue Information

Dear Colleagues,

Recently, with the advancement of technology, multi-source data applications in environment protection are becoming increasingly popular. We are pleased to announce a Special Issue dedicated to exploring the latest advancements in the field of multi-source data fusion and atmospheric research analysis.

The aim of this Special Issue is to showcase cutting-edge research, data science, methodologies, and practical applications related to the monitoring and assessment of atmospheric research. Original papers on statistical machine learning, spatial econometrics, data science, and time series analysis, including case studies on atmospheric data using both theoretical and empirical approaches, are welcome in this Special Issue. We encourage researchers in the fields of data analysis and atmospheric science to contribute their original work to this Special Issue, thereby promoting interdisciplinary collaboration and driving advancements in this domain.

Topics of interest include, but are not limited to, the following:

  • Multi-source data fusion for atmospheric research;
  • Big data analysis in atmospheric science;
  • Deep learning for predictive modeling in atmospheric science;
  • Air quality monitoring using big data;
  • Monitoring air quality techniques;
  • Simulation, modeling, and optimization;
  • Environmental data science;
  • Advanced methods;
  • Spatial data deep learning;
  • The application of satellite products;
  • Spatio-temporal analysis.

We look forward to receiving your contributions.

Dr. Zhaoxin Dai
Dr. Qi Zhou
Prof. Dr. Yi 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

  • aerosols
  • air quality
  • data science
  • geospatial and big data analysis
  • modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 959 KiB  
Article
Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial–Temporal Optimal Interpolation
by Natallia Miatselskaya, Andrey Bril and Anatoly Chaikovsky
Atmosphere 2025, 16(5), 623; https://doi.org/10.3390/atmos16050623 - 20 May 2025
Abstract
A common approach to estimating the spatial–temporal distribution of atmospheric species properties is data assimilation. Data assimilation methods provide the best estimate of the required parameter by combining observations with appropriate prior information (background) that can include the model output, climatology data, or [...] Read more.
A common approach to estimating the spatial–temporal distribution of atmospheric species properties is data assimilation. Data assimilation methods provide the best estimate of the required parameter by combining observations with appropriate prior information (background) that can include the model output, climatology data, or some other first guess. One of the relatively simple and computationally cheap data assimilation methods is optimal interpolation (OI). It estimates a value of interest through a weighted linear combination of observational data and background that is defined only once for the whole time interval of interest. Spatial–temporal OI (STOI) utilizes both spatial and temporal observational error covariance and background error covariance. This allows for filling in not only spatial, but also temporal gaps in observations. We applied STOI to daily mean aerosol optical depth (AOD) observations obtained at the European AERONET (Aerosol Robotic Network) sites with the use of the GEOS-Chem chemical transport model simulations and the AOD climatology data as backgrounds. We found that mean square errors in the estimate when using modeled data are comparable with those when using climatology data. Based on these results, we merged estimates obtained using modeled and climatology data according to their mean square errors. This allows for improving the AOD estimates in areas where observations are limited in space and time. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
►▼ Show Figures

Figure 1

22 pages, 2255 KiB  
Article
Homogenization of the Probability Distribution of Climatic Time Series: A Novel Algorithm
by Peter Domonkos
Atmosphere 2025, 16(5), 616; https://doi.org/10.3390/atmos16050616 - 18 May 2025
Viewed by 198
Abstract
The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual [...] Read more.
The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
►▼ Show Figures

Figure 1

17 pages, 5415 KiB  
Article
Formation and Precipitation Processes of the Southwest Vortex Impacted by the Plateau Vortex
by Aijuan Bai, Jinfeng Bai, Zhao Wang and Chaoyong Tu
Atmosphere 2025, 16(2), 115; https://doi.org/10.3390/atmos16020115 - 22 Jan 2025
Viewed by 657
Abstract
This study investigated the source, trajectory, and precipitation of the Southwest (SW) vortex, which was linked with the Plateau (P) vortex. Based on the statistical study of a number of cases, this study showed the following results. The SW vortex tended to originate [...] Read more.
This study investigated the source, trajectory, and precipitation of the Southwest (SW) vortex, which was linked with the Plateau (P) vortex. Based on the statistical study of a number of cases, this study showed the following results. The SW vortex tended to originate at the northeastern and western peripheries of the Sichuan Basin, normally coinciding with the presence of the P vortices in the eastern region of the Tibetan Plateau. Most of the aforementioned vortices exhibited a longer life span, and resulted in severe storms averaging approximately 50 mm of rainfall per day, especially in the cases of more than 100 mm of rainfall per day in eastern and southern China. Furthermore, new findings were obtained: (1) The SW vortex and the P vortex were attributed from an ‘Ω’ circulation pattern from blocking high in middle to high latitudes region. The SW vortex was notably influenced by the convergence of two air currents. In the lower troposphere, the southwesterly jet of the South Asian monsoon flowed over and around the Yungui Plateau, and cold–dry air from the north flowed into the Basin. (2) Both the SW vortex and the P vortex displayed a shallow synoptic system characterized below 500 hPa, and wet–cold cores formed around the sources at low altitudes. (3) The analysis on atmospheric instability and dynamics suggested that the vortices’ eddies generated significant convective instability at lower levels. The circulation pattern and instability conditions facilitated the heavy precipitation associated with the SW vortex, and the ample water vapor and subsequent latent heat intensified the precipitation. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
►▼ Show Figures

Figure 1

17 pages, 5177 KiB  
Article
A Branched Convolutional Neural Network for Forecasting the Occurrence of Hazes in Paris Using Meteorological Maps with Different Characteristic Spatial Scales
by Chien Wang
Atmosphere 2024, 15(10), 1239; https://doi.org/10.3390/atmos15101239 - 17 Oct 2024
Viewed by 844
Abstract
A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The [...] Read more.
A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The strategy is to make the machine learn from available historical data to recognize various regional weather and hydrological regimes associated with low-visibility events. To better preserve the characteristic spatial information of input features in training, two branched architectures have recently been developed. These architectures process input features firstly through several branched CNNs with different kernel sizes to better preserve patterns with certain characteristic spatial scales. The outputs from the first part of the network are then processed by the second part, a deep non-branched CNN, to further deliver predictions. The CNNs with new architectures have been trained using data from 1975 to 2019 in a two-class (haze versus non-haze) classification mode as well as a regression mode that directly predicts the value of surface visibility. The predictions of regression have also been used to perform the two-class classification forecast using the same definition in the classification mode. This latter procedure is found to deliver a much better performance in making class-based forecasts than the direct classification machine does, primarily by reducing false alarm predictions. The branched architectures have improved the performance of the networks in the validation and also in an evaluation using the data from 2021 to 2023 that have not been used in the training and validation. Specifically, in the latter evaluation, branched machines captured 70% of the observed low-visibility events during the three-year period at Charles de Gaulle Airport. Among those predicted low-visibility events by the machines, 74% of them are true cases based on observation. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
►▼ Show Figures

Figure 1

20 pages, 6706 KiB  
Article
Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation
by Hongyun Zhou, Zhaoxin Dai, Chuangqi Wu, Xin Ma, Lining Zhu and Pengda Wu
Atmosphere 2024, 15(3), 307; https://doi.org/10.3390/atmos15030307 - 29 Feb 2024
Cited by 1 | Viewed by 1868
Abstract
PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and [...] Read more.
PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts. Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop