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Atmosphere
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Statistical Methods in Atmospheric Research
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Statistical Methods 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 (10 April 2023) | Viewed by 10332

Special Issue Editor

Dr. Santtu Mikkonen

E-Mail Website
Guest Editor
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Interests: statistical methodology; atmospheric research; air pollution; health effects; time series analysis; multivariable data analysis; data science

Special Issue Information

Dear Colleagues,

The amount of measured and modeled data in atmospheric research is increasing at a quickening pace. All these data need to be analyzed with appropriate methodology. At the same time, complex experimental setups are bringing only small numbers of data for answering specific questions, but traditionally used methods are not suitable for analyzing these data. Currently, researchers in atmospheric sciences are developing new methods for analysis and bringing methodology from other fields to answers questions from big and small data. Atmosphere recognizes that these methods need to be introduced and this Special Issue is a response to that need.

The purpose of this Special Issue is to introduce advanced statistical methodology for the use of atmospheric scientists wrestling with complex data and the research questions related to them. The main emphasis will be given to methods for multivariable data, capable of finding dependencies from a high number of variables, and advanced time series analysis methods, capable of taking into account the autocorrelative structure of data and dealing with nonstationary or heteroscedastic time series.

We seek methodological studies analyzing measured or modeled atmospheric data, as well as studies introducing new and interesting results gained with statistical methods. We also welcome review papers summarizing existing methodology as well as brief communications introducing solutions to specific problems.

Dr. Santtu Mikkonen
Guest Editor

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

  • statistical methods
  • data analysis
  • aerosols
  • air pollution
  • atmospheric measurements
  • atmospheric models

Published Papers (7 papers)

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Research

24 pages, 4575 KiB  
Article
Impact of Gaseous Pollutants Reduction on Fine Particulate Matter and Its Secondary Inorganic Aerosols in Beijing–Tianjin–Hebei Region
by Zhe Wei and Norhaslinda Mohamed Tahrin
Atmosphere 2023, 14(6), 1027; https://doi.org/10.3390/atmos14061027 - 15 Jun 2023
Cited by 2 | Viewed by 1063
Abstract
A reduction in gaseous pollutants is an important method for mitigating PM2.5 concentration in the atmosphere, and the reduction in SO2/NH3/NOx is beneficial to control secondary inorganic aerosols in PM2.5. In this study, the Weather [...] Read more.
A reduction in gaseous pollutants is an important method for mitigating PM2.5 concentration in the atmosphere, and the reduction in SO2/NH3/NOx is beneficial to control secondary inorganic aerosols in PM2.5. In this study, the Weather Research and Forecasting model with Chemistry model (WRF-Chem) was applied to study the impact on the PM2.5 and its secondary inorganic aerosols using the scenario simulation method in the Beijing–Tianjin–Hebei (BTH) region. The results showed that the BTH region is characterized by being NH3-rich and having a higher [NH4+]/[SO42−] ratio in southern BTH, with a ratio of more than 6.0. Source contribution to PM2.5 was highest in the 30%_SO2_40%_NH3_40%_NOx scenario, with a contribution ratio of 6.8%, followed by 3.8% contribution in the 30%_SO2_40%_NH3 scenario, and a 3.4% contribution in the 30%_SO2_60%_NH3_60%_NOx scenario. These results indicate that synergistic reduction measures may be suitable for controlling PM2.5 concentrations. A lower sensitivity factor, β value between PM2.5 and NH3 suggests that solely reducing NH3 emissions is not beneficial for the BTH region. However, this study indicates that the sensitivity of NO3− would improve significantly if NH3 emissions are reduced sharply. A slight reduction in NH3 was found to be beneficial for controlling NO3− in medium and small cities, while a significant decrease in NH3 would be more suitable for mega-cities. The study also observed that SO42− and its constituents continued to decrease with a consistent β value of approximately 0.14 in the 30%_SO2_%_NH3 scenario and between 10.5 and 12.8 in the 30%_SO2_%_NH3_%_NOx scenario. These findings suggest that a synergistic reduction in SO2-NH3-NOx emissions may be more effective in reducing PM2.5 concentrations and its secondary inorganic aerosols (SIAs). However, it is important to ensure that the reduction in NH3 and NOx exceeds 60% in low SO2 concentration conditions. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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14 pages, 2779 KiB  
Article
System Predictability Assessed by Low Wavenumber Fourier Components and Analogue Pair Progression of Geopotential Height
by Marshall Liddle, Hans Moosmüller and John Lewis
Atmosphere 2023, 14(5), 886; https://doi.org/10.3390/atmos14050886 - 18 May 2023
Viewed by 869
Abstract
Following Lorenz’s work using analogue pairs for establishing 10-to-14-day predictability limits for synoptic weather regimes, predictability limits for the Rex block, the long-wave wintertime ridge over the eastern Pacific Ocean and the western United States, have been estimated. This was accomplished by using [...] Read more.
Following Lorenz’s work using analogue pairs for establishing 10-to-14-day predictability limits for synoptic weather regimes, predictability limits for the Rex block, the long-wave wintertime ridge over the eastern Pacific Ocean and the western United States, have been estimated. This was accomplished by using mid-latitude geopotential height reanalysis data over a period of 38 years, 1979–2016, and associated 90-day winters (DJF). The metric used to define analogue pairs is the RMS difference assessed for the hemispheric 850, 500, and 200 hPa geopotential height fields. The resultant set of analogue pairs was used to estimate predictability with respect to both a single latitude circle (40° N) that passes through the Rex Block and for a multi-latitude swath (20–80° N). Our methods showed a range of results, by choice of Fourier component wavenumbers 2 through 8. These results indicate system predictability for low wavenumber components to exceed the 10–14-day limit imposed by Lorenz’ results. The results to 21 days, the maximum predictability limit value allowed by our method, do not preclude the possibility of a greater range of system predictability past 21 days. The unique aspect of this work is determination of predictability limits as a function of geopotential wave structure found through Fourier decomposition. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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15 pages, 21315 KiB  
Article
Dominance Patterns Specified by the Ideal Gas Equation: Example of Examining Simultaneous Multivariate Relation with Scale Analysis Approach
by Juqing Tu, Dian Yuan and Er Lu
Atmosphere 2023, 14(2), 293; https://doi.org/10.3390/atmos14020293 - 01 Feb 2023
Viewed by 1212
Abstract
Climate science involves various functional relations and needs to investigate the dominance or relative importance of the variables in their relation. In our previous studies, we examined several problems in which causal relations are established, showing how the dependent quantity is affected by [...] Read more.
Climate science involves various functional relations and needs to investigate the dominance or relative importance of the variables in their relation. In our previous studies, we examined several problems in which causal relations are established, showing how the dependent quantity is affected by two or more independent variables. With linear fitting, the relative contributions of the variables to the variation of the quantity are compared. In this study, we examine constraint relation, which is a simultaneous multivariate relation, with all variables in the relation being equal in position. The relation can generally be nonlinear. To be convenient for examining the dominance, plane equation fitting can be used to linearize the relation. The equation of state for ideal dry air is investigated as a simple case of the relation. For this special case, a linearized relation can be obtained from both the fitting and the derivation. The scale analysis tool used in dynamic meteorology is applied here for the dominance analysis. Through comparing the scales of the terms, we can simplify the equation. The simplified relations correspond, respectively, to Charle’s law, Boyle’s law, and Gay-Lussac’s law. The geographical preferences of the different dominance patterns are exhibited. In addition, when considering the change of the variable that is smallest in scale, we can identify which factor is the dominator. The ideal gas law is intentionally chosen as the example, since the relation is simple in form, and the results of dominance can be deduced analytically. A comparison demonstrates that the methods used here for the dominance analysis are reliable. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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16 pages, 3797 KiB  
Article
Spatial Patterns in the Extreme Dependence of Ozone Pollution between Cities in China’s BTH Region
by Lu Deng and Siqi Sheng
Atmosphere 2023, 14(1), 141; https://doi.org/10.3390/atmos14010141 - 08 Jan 2023
Cited by 2 | Viewed by 1105
Abstract
Ozone pollution in China has become increasingly severe in recent years. Considering the damage that extreme ozone pollution may cause and the fact that the occurrence of extreme ozone pollution among different locations may be related, this paper uses hourly ozone concentration data [...] Read more.
Ozone pollution in China has become increasingly severe in recent years. Considering the damage that extreme ozone pollution may cause and the fact that the occurrence of extreme ozone pollution among different locations may be related, this paper uses hourly ozone concentration data from national monitoring stations to investigate the co-movement of extreme ozone pollution in the Beijing-Tianjin-Hebei (BTH) Region. The extreme dependence analysis is adopted to assess such extreme co-movements between different cities. The co-occurrences of extreme ozone pollution at the same time or with certain time differences in the region are analyzed. City groups suffering simultaneous extreme pollution and those where the pollution occurs with certain time differences are identified under certain criteria. Furthermore, the order in which cities experience extreme ozone pollution is determined. With the publication of the New Three-year Action Plan for Winning the Blue Sky War, our results may be important for improving the joint early-warning and emergency response mechanism at city levels in the BTH Region. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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17 pages, 2783 KiB  
Article
Association between Air Pollution Exposure and Daily Outpatient Visits for Dry Eye Disease: A Time-Series Study in Urumqi, China
by Kun Liang, Si-Yu Gui, Jian-Chao Qiao, Xin-Chen Wang, Fan Yang, Fang-Biao Tao, Xiang-Long Yi and Zheng-Xuan Jiang
Atmosphere 2023, 14(1), 90; https://doi.org/10.3390/atmos14010090 - 31 Dec 2022
Cited by 1 | Viewed by 1508
Abstract
The potential effects of air pollution on the ocular surface environment have not been fully evaluated, and even fewer studies have been conducted on the lagged effects of air pollution on dry eye disease (DED). The data of 9970 DED outpatients between 1 [...] Read more.
The potential effects of air pollution on the ocular surface environment have not been fully evaluated, and even fewer studies have been conducted on the lagged effects of air pollution on dry eye disease (DED). The data of 9970 DED outpatients between 1 January 2013 and 31 December 2020, and data for six air pollutants, including PM10, PM2.5, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3), were obtained from 11 standard urban background stationary air quality monitors in Urumqi, Xinjiang, China. Time series analysis design and quasi-Poisson generalized linear regression models combined with distributed lagged nonlinear models (DLNM) were used. Single- and multi-pollutant model results suggest that each additional per 10 μg/m3 of PM10, NO2, and SO2 is associated with an increased risk of outpatient DED on lag day 0 and PM2.5, NO2, and SO2 with other cumulative lag days; R software version 4.0.4 (15 February 2021) was used for the analysis. We conducted first time series analysis with a large sample size in northwest China (Xinjiang) and confirmed, for the first time, the impact of air pollution including particulate pollutants (PM10, PM2.5) and acidic gasses (SO2, NO2) on DED risk in the Urumqi region, and suggested the potential lagged effects of PM2.5, SO2, and NO2. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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19 pages, 23208 KiB  
Article
Atmospheric Conditions for Uplift and Dust Transport in the Latitudinal 10° North–20° North Band in Africa
by Abdoulaye Bouya Diop, Malick Wade, Abdoulaye Sy, Abdoul Karim Mbodji, Abdou Karim Farota, El hadji Deme, Babacar Niang, Bouya Diop, Amadou Thierno Gaye and Aboubakary Diakhaby
Atmosphere 2022, 13(7), 1083; https://doi.org/10.3390/atmos13071083 - 08 Jul 2022
Viewed by 1599
Abstract
Desert aerosols suspended in the atmosphere are a very marked fact in West Africa with estimates of 400 to 1000 million tons produced annually and concentrations exceeding 50 µg·m³ in Burkina. In Bamako, the daily dust concentration can go up to reach 504 [...] Read more.
Desert aerosols suspended in the atmosphere are a very marked fact in West Africa with estimates of 400 to 1000 million tons produced annually and concentrations exceeding 50 µg·m³ in Burkina. In Bamako, the daily dust concentration can go up to reach 504 µg/m³. The Sahara and the Sahel are recognized as the primary desert aerosol producing regions. Source areas continue to be discovered as the desert advances. Previous studies have mainly focused on the spatial and temporal variability of aerosols. The current question is: What makes an area a source of dust emission? Our study brings together all the climatic parameters of the 10–20 band, as well as the soil types and their characteristics; it reveals 4 soils characteristic of fine sandy semi-arid soils in Chad. The Ouadaï plateau in Chad was identified as a source area for dust emissions. We noted for JFM (January, February, March) that the strongest wind intensities were located mainly towards Chad for average rmaximum temperatures around 34.7 °C. The statistical study reveals a correlation of 66.8% between direct and indirect links between the climatic factors of the 10–20 band and the source area. The presence of vortexes throughout the year and a vertical wind profile that is among the strongest in the 10–20 band, this gradient is strongly localized in the grid “10° North, 20° North and 20° East, 30° East” next to the Kapka massif. The study shows that the AEJ (African Easterly Jet) profile, which is a strong wind, associated with the harmattan circulation, allows the transport of aerosols from Ouadaï to the West African coast. In Senegal, a significant deposition was observed. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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13 pages, 531 KiB  
Article
Exploring Non-Linear Dependencies in Atmospheric Data with Mutual Information
by Petri Laarne, Emil Amnell, Martha Arbayani Zaidan, Santtu Mikkonen and Tuomo Nieminen
Atmosphere 2022, 13(7), 1046; https://doi.org/10.3390/atmos13071046 - 29 Jun 2022
Cited by 3 | Viewed by 1836
Abstract
Relations between atmospheric variables are often non-linear, which complicates research efforts to explore and understand multivariable datasets. We describe a mutual information approach to screen for the most significant associations in this setting. This method robustly detects linear and non-linear dependencies after minor [...] Read more.
Relations between atmospheric variables are often non-linear, which complicates research efforts to explore and understand multivariable datasets. We describe a mutual information approach to screen for the most significant associations in this setting. This method robustly detects linear and non-linear dependencies after minor data quality checking. Confounding factors and seasonal cycles can be taken into account without predefined models. We present two case studies of this method. The first one illustrates deseasonalization of a simple time series, with results identical to the classical method. The second one explores associations in a larger dataset of many variables, some of them lognormal (trace gas concentrations) or circular (wind direction). The examples use our Python package ‘ennemi’. Full article
(This article belongs to the Special Issue Statistical Methods in Atmospheric Research)
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