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Atmosphere

Atmosphere is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about the Atmosphere.
Companion journals for Atmosphere include: Meteorology and Aerobiology.

Impact Factor: 2.5 (2023); 5-Year Impact Factor: 2.6 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2073-4433

Latest Articles

12 pages, 6255 KiB

Open AccessArticle

Microphysical Characteristics of a Sea Fog Event with Precipitation Along the West Coast of the Yellow Sea in Summer

by Xiaoyu Shi, Li Yi, Suping Zhang, Xiaomeng Shi, Yingchen Liu, Yilin Liu, Xiaoyu Wang and Yuechao Jiang

Atmosphere 2025, 16(3), 308; https://doi.org/10.3390/atmos16030308 - 6 Mar 2025

The microphysics and visibility (Vis) of a sea fog event with precipitation were measured at the Baguan Hill Meteorological Station (BGMS) (36.07° N, 120.33° E; 86 m above sea level) from 27 June to 28 June 2022. The duration of the fog process [...] Read more.

The microphysics and visibility (Vis) of a sea fog event with precipitation were measured at the Baguan Hill Meteorological Station (BGMS) (36.07° N, 120.33° E; 86 m above sea level) from 27 June to 28 June 2022. The duration of the fog process was 880 min. The mean value of the number concentration (N_d) was 190.62 cm⁻³, and the mean value of the liquid water content (LWC) was 0.026 g m⁻³. Small droplets contributed 81% to N_d and had a greater impact on visibility attenuation, while larger droplets accounted for 58% of the total LWC. The observed droplet size distribution (DSD) was better represented by the G-exponential distribution than by the Gamma distribution. Incorporating both N_d and LWC in Vis parameterization resulted in the best prediction performance. This work enhances understanding of sea fog microphysics in the west coast of Yellow Sea in summer and highlights the need for long-term observations. Full article

(This article belongs to the Special Issue Advance in Transportation Meteorology (2nd Edition))

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24 pages, 6847 KiB

Open AccessArticle

Comparing Reflectivity from Space-Based and Ground-Based Radars During Detection of Rainbands in Two Tropical Cyclones

by Corene J. Matyas, Stephanie E. Zick and Kimberly M. Wood

Atmosphere 2025, 16(3), 307; https://doi.org/10.3390/atmos16030307 - 6 Mar 2025

With varying tangential winds and combinations of stratiform and convective clouds, tropical cyclones (TCs) can be difficult to accurately portray when mosaicking data from ground-based radars. This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to [...] Read more.

With varying tangential winds and combinations of stratiform and convective clouds, tropical cyclones (TCs) can be difficult to accurately portray when mosaicking data from ground-based radars. This study utilizes the Dual-frequency Precipitation Radar (DPR) from the Global Precipitation Measurement Mission (GPM) satellite to evaluate reflectivity obtained using four sampling methods of Weather Surveillance Radar 1988-Doppler data, including ground radars (GRs) in the GPM ground validation network and three mosaics, specifically the Multi-Radar/Multi-Sensor System plus two we created by retaining the maximum value in each grid cell (MAX) and using a distance-weighted function (DW). We analyzed Hurricane Laura (2020), with a strong gradient in tangential winds, and Tropical Storm Isaias (2020), where more stratiform precipitation was present. Differences between DPR and GR reflectivity were larger compared to previous studies that did not focus on TCs. Retaining the maximum value produced higher values than other sampling methods, and these values were closest to DPR. However, some MAX values were too high when DPR time offsets were greater than 120 s. The MAX method produces a more consistent match to DPR than the other mosaics when reflectivity is <35 dBZ. However, even MAX values are 3–4 dBZ lower than DPR in higher-reflectivity regions where gradients are stronger and features change quickly. The DW and MRMS mosaics produced values that were similar to one another but lower than DPR and MAX values. Full article

(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones (2nd Edition))

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23 pages, 29026 KiB

Open AccessArticle

Urban Impacts on Convective Squall Lines over Chicago in the Warm Season—Part I: Observations of Multi-Scale Convective Evolution

by Michael L. Kaplan, S. M. Shajedul Karim and Yuh-Lang Lin

Atmosphere 2025, 16(3), 306; https://doi.org/10.3390/atmos16030306 - 6 Mar 2025

In this study, our aim is to diagnose how two quasi-linear convective systems (QLCS) are organized so one can determine the possible role of the city of Chicago, IL, USA, in modifying convective precipitation systems. In this Part I of a two-part study, [...] Read more.

In this study, our aim is to diagnose how two quasi-linear convective systems (QLCS) are organized so one can determine the possible role of the city of Chicago, IL, USA, in modifying convective precipitation systems. In this Part I of a two-part study, we employ large-scale analyses, radiosonde soundings, surface observations, and Doppler radar data to diagnose the precursor atmospheric circulations that organize the evolution of two mesoscale convective systems and compare those circulations to radar and precipitation. Several multi-scale processes are found that organize and modify convection over the Chicago metroplex. Two sequential quasi-linear convective systems (QLCS #1 and #2) were organized that propagated over Chicago, IL, USA, during an eight-hour period on 5–6 July 2018. The first squall line (QLCS #1) built from the southwest to the northeast while strengthening as it propagated over the city, and the second (QLCS #2) propagated southeastwards and weakened as it passed over the city in association with a polar cold front. The weak upper-level divergence associated with a diffluent flow poleward of an expansive ridge built over and strengthened a low-level trough and confluence zone, triggering QLCS #1. Convective downdrafts from QLCS #1 produced a cold pool that interacted with multiple confluent low-level jets surrounding and focused on the metroplex urban heat island, thus advecting the convection poleward over the metroplex. The heaviest precipitation occurred just south-southeast of Midway Airport, Chicago. Subsequently, a polar cold front propagated into the metroplex, which triggered QLCS #2. However, the descending air above it under the polar jet and residual cold pool from QLCS #1 rapidly dissipated the cold frontal convection. This represents a case study where very active convection built over the metroplex and was likely modified by it, as evidenced in numerical simulations to be described in Part II. Full article

(This article belongs to the Section Meteorology)

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19 pages, 7685 KiB

Open AccessArticle

A Comprehensive Analysis of Urban Flooding Under Different Rainfall Patterns: A Full-Process Perspective in Haining, China

by Yuzhou Zhang, Luoyang Wang, Qing Zhang, Yao Li, Pin Wang and Tangao Hu

Atmosphere 2025, 16(3), 305; https://doi.org/10.3390/atmos16030305 - 6 Mar 2025

Urban flooding, driven by extreme rainfall events and urbanization, poses substantial risks to urban safety and infrastructure. This study employed a neighborhood-scale InfoWorks ICM model to analyze the full-process impacts of urban flooding under six rainfall return periods in Haining, China. The results [...] Read more.

Urban flooding, driven by extreme rainfall events and urbanization, poses substantial risks to urban safety and infrastructure. This study employed a neighborhood-scale InfoWorks ICM model to analyze the full-process impacts of urban flooding under six rainfall return periods in Haining, China. The results reveal distinct non-linear responses from the 3-year to 50-year rainfall return period: (1) the surface runoff volume increases by 64.3%, with peak timing advancing by about one minute; (2) the overflow nodes rise from 37.35% to 63.24%, with durations over 30 min increasing by 78.6%; (3) the inundation areas expand by 164.9%, with maximum depths increasing by 0.31 m, showing significant regional disparities; and (4) high-risk zones, such as Haining People’s Square and Railway Station, require targeted interventions due to severe surface overflow and inundation. This comprehensive analysis emphasizes the need for tailored and phased flood prevention measures that address each stage of urban flooding. It provides a strong framework to guide urban planning and enhance resilience against rainfall-induced urban flooding. Full article

(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)

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20 pages, 5528 KiB

Open AccessArticle

Analysis of Extreme Wind Pressure Based on Extreme Value Distribution Theory

by Weihu Chen, Yuji Tian and Fan Bai

Atmosphere 2025, 16(3), 304; https://doi.org/10.3390/atmos16030304 - 5 Mar 2025

When extreme wind pressure is predicted based on the extreme value distribution theory, the sampling time–distance and sample volume of wind pressure data are important influencing factors. To discuss and analyze the influence of time–distance and sample volume on the prediction results and [...] Read more.

When extreme wind pressure is predicted based on the extreme value distribution theory, the sampling time–distance and sample volume of wind pressure data are important influencing factors. To discuss and analyze the influence of time–distance and sample volume on the prediction results and accuracy of extreme wind pressure, according to the wind tunnel pressure test results of low buildings, the extreme wind pressure results corresponding to different working conditions of time–distance and sample volume are calculated, and the prediction results are compared and analyzed. At the same time, the wind pressure conversion ratio variable is introduced to analyze the prediction accuracy of extreme wind pressure. According to the calculation results of the wind pressure conversion ratio under different working conditions, the empirical calculation formula of wind pressure conversion ratio concerning time–distance and sample volume is further established, and the applicability of the empirical calculation formula is verified and analyzed. The results show that a reasonable increase in time–distance and sample volume can effectively improve the prediction accuracy of extreme wind pressure. Meanwhile, the empirical calculation formula of the wind pressure conversion ratio provides a method for quantitative analysis of the impact of time–distance and sample volume change on the prediction accuracy of extreme wind pressure, and it is also a theoretical reference for the uncertainty analysis of extreme wind pressure. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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28 pages, 9704 KiB

Open AccessArticle

Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification

by Afaq Khattak, Badr T. Alsulami and Caroline Mongina Matara

Atmosphere 2025, 16(3), 303; https://doi.org/10.3390/atmos16030303 - 5 Mar 2025

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet [...] Read more.

Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter optimization, using data from Open-Seneca sensors along the Nairobi Expressway, combined with meteorological and traffic data. First, analysis showed that the PBT-tuned ResNet34 was the most effective model, achieving a precision (0.988), recall (0.971), F1-Score (0.979), Matthews Correlation Coefficient (MCC) of 0.904, Geometric Mean (G-Mean) of 0.962, and Balanced Accuracy (BA) of 0.962, outperforming alternative models, including ResNet18, ResNet34, and baseline approaches such as Feedforward Neural Networks (FNN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Gene Expression Programming (GEP). Subsequent feature importance analysis using a permutation-based strategy, along with SHAP analysis, revealed that humidity and hourly traffic volume were the most influential features. The findings indicated that medium to high humidity values were associated with an increased likelihood of HE, while medium to high traffic volumes similarly contributed to the occurrence of HE. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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20 pages, 8927 KiB

Open AccessArticle

Lightning Return Stroke Positioning Method Based on CWT Narrowband Feature Extraction

by Jinxing Shen, Jiancheng Gong and Dong Zhou

Atmosphere 2025, 16(3), 302; https://doi.org/10.3390/atmos16030302 - 5 Mar 2025

Time of arrival (TOA) is a widely utilized method for positioning lightning return strokes, with its accuracy contingent upon the arrival times of signals from different detection sites. Typically, the peak value method is employed to directly extract the peak times of lightning [...] Read more.

Time of arrival (TOA) is a widely utilized method for positioning lightning return strokes, with its accuracy contingent upon the arrival times of signals from different detection sites. Typically, the peak value method is employed to directly extract the peak times of lightning electromagnetic pulse (LEMP) waveforms. By correlating these peak times with the coordinates of the sites, the spatiotemporal parameters of the LEMP can be determined. However, due to the dispersion phenomenon of broadband LEMP signals during propagation, the positioning accuracy of the peak method is relatively low. This paper introduces a novel lightning positioning technique that leverages continuous wavelet transform (CWT) for narrowband feature extraction. Specifically, narrowband signal characteristics were derived through CWT applied to simulation and measured data obtained from six detection sites. Subsequently, positional analysis was performed on both datasets. The results demonstrate that compared to traditional peak value methods, the proposed approach significantly enhances horizontal positioning accuracy for lightning; specifically, positioning error for simulation data decreased from 94.7 m to 5.6 m, while it reduced from 121 m to 9.2 m for practical measured data. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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28 pages, 7435 KiB

Open AccessArticle

Climatological and Hydrological Extremes of the Andaman and Nicobar Islands, India, and Its Database for Public Users

by Abhilash, Anurag Satpathi, Talaviya Harshangkumar, Thangavel Subramani, Iyyappan Jaisankar and Namendra Kumar Shahi

Atmosphere 2025, 16(3), 301; https://doi.org/10.3390/atmos16030301 - 4 Mar 2025

The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding [...] Read more.

The Andaman and Nicobar Islands experience a climate characterized by consistently high humidity, substantial annual precipitation, and moderate temperature fluctuations. The region’s susceptibility to extreme weather events—such as cyclones, heavy precipitation, and rising sea levels - highlights the need for a thorough understanding of its climatic patterns. In light of this, this study provides a comprehensive analysis of spatiotemporal variability and trends in mean and extreme precipitation across the Andaman and Nicobar Islands using long-term (i.e., 1981–2023) high-resolution Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Our findings indicate a significant increase in monsoonal precipitation, particularly in South Andaman, where the mean precipitation trend is 11.10 mm/year, compared to 6.54 mm/year in Nicobar. Light-to-moderate precipitation events occur more frequently than heavy precipitation across all districts, although heavy precipitation is more frequent in Andaman than in Nicobar. Significant decadal increases in light-to-moderate precipitation events are found across most of Nicobar, while parts of Andaman showed a rise in the frequency of moderate-to-heavy precipitation events. Trend analysis of the highest single-day precipitation annually reveals mixed patterns, with increases noted in North and Middle Andaman (3.66 mm per decade) and South Andaman (1.13 mm per decade), while Nicobar shows a slight decrease (−0.63 mm per decade). Maximum consecutive five-day precipitation trends indicate significant annual increases in North and Middle Andaman (14.98 mm per decade) and South Andaman (3.49 mm per decade), highlighting the variability in extreme precipitation events. The observed trends in precipitation and its extremes highlight the heterogeneity of precipitation patterns, which are critical for water resource management, agriculture, and disaster risk mitigation in the region, particularly in the context of increasing precipitation variability and intensity driven by climate change. Further investigation is needed to understand the physical mechanisms driving the increase in frequency and intensity of precipitation, which will be addressed in a separate paper. Full article

(This article belongs to the Special Issue Understanding the Spatio-Temporal Characteristics of Climatic Extremes: Insights from Regional Climate Modeling)

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16 pages, 4285 KiB

Open AccessArticle

Jazan Rainfall’s Seasonal Shift in Saudi Arabia: Evidence of a Changing Regional Climate

by M. Nazrul Islam, Arjan O. Zamreeq, Muhammad Ismail, Turki M. A. Habeebullah and Ayman S. Ghulam

Atmosphere 2025, 16(3), 300; https://doi.org/10.3390/atmos16030300 - 4 Mar 2025

In recent years, rainfall in the Jazan region of southwest Saudi Arabia has significantly increased, setting new records for monthly and daily rainfall in 2024 and leading to natural disasters. The distribution of monthly rainfall in Jazan and its variations over recent decades [...] Read more.

In recent years, rainfall in the Jazan region of southwest Saudi Arabia has significantly increased, setting new records for monthly and daily rainfall in 2024 and leading to natural disasters. The distribution of monthly rainfall in Jazan and its variations over recent decades have not been analyzed yet. This study examines the changes in seasonal rainfall patterns in the Jazan region utilizing observational and reanalysis datasets from 1978 to 2024. The rescaled adjusted partial sums technique is used to detect breaks in the rainfall time series, while statistical methods are applied to analyze rainfall extremes and their trends. The average annual rainfall for the period 1978–2024 is 149.4 mm, which has increased from 131.9 mm during the earlier decades (1978–2000) to 166.2 mm in recent decades (2001–2024), reflecting an increase of 34.3 mm. The annual rainfall has been increasing significantly at a rate of 92.9 mm/decade in recent decades, compared to 74.3 mm/decade in the previous decades. There has been a marked shift in the peak rainfall season from autumn to summer, in particular moving from October to August in recent decades. The highest monthly rainfall recorded in August, reached 54.9 mm in recent decades, compared to just 15.4 mm in earlier decades. In contrast, the peak rainfall in October was 19.9 mm in previous decades, which decreased to 18.7 mm in recent decades. Notably, August 2024 marked a record-breaking rainfall of 414.8 mm, surpassing the previous high of 157.5 mm set in October 1997. These data show clear evidence of the changing climate in the region. Moreover, the number of heavy rainfall days has risen, with a total of 608 wet days documented throughout the entire period, alongside a significant increase in light, heavy, and extremely heavy rainfall days in recent decades compared to earlier ones. Hence, the region has seen a rise in heavy to extremely heavy rainfall days, including a daily record of 113.7 mm on 23 August 2024, compared to 90.0 mm on 22 October 1997. Additionally, there has been a rise in the maximum consecutive 5-day rainfall compared to the maximum 1-day rainfall. Overall, these findings show substantial changes in rainfall patterns in the Jazan region, suggesting notable climatic shifts that warrant further investigation using the automatic weather stations, radar and satellite data, as well as climate model simulations. Full article

(This article belongs to the Section Climatology)

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25 pages, 10436 KiB

Open AccessArticle

Effects of the Geomagnetic Superstorms of 10–11 May 2024 and 7–11 October 2024 on the Ionosphere and Plasmasphere

by Viviane Pierrard, Tobias G. W. Verhulst, Jean-Marie Chevalier, Nicolas Bergeot and Alexandre Winant

Atmosphere 2025, 16(3), 299; https://doi.org/10.3390/atmos16030299 - 4 Mar 2025

On 10 May 2024 at 17 h:07 UTC, the simultaneous arrival of several solar coronal mass ejections (CMEs) generated the strongest geomagnetic storm of the last twenty years, with a minimum Dst = −412 nT, usually referred to as the Mother’s Day event. [...] Read more.

On 10 May 2024 at 17 h:07 UTC, the simultaneous arrival of several solar coronal mass ejections (CMEs) generated the strongest geomagnetic storm of the last twenty years, with a minimum Dst = −412 nT, usually referred to as the Mother’s Day event. On 10 October 2024, the second strongest event of solar cycle 25 appeared with a Dst = −335 nT, preceded on 8 October by an event with a Dst = −153 nT. In the present work, with measurements of the vertical total electron content and with ionosonde observations from Europe, USA, and South Korea, we show that the ionization of the upper atmosphere shortly increased at the arrival of the CME for these different events, followed by a fast decrease at all latitudes. The ionization remained very low for more than a full day. While the recovery started at the beginning of the second day after the onset for both events in October, the sudden recovery in the middle of the second day on 12 May is much more unusual. The analysis of the observations at different latitudes and longitudes shows that the causes of the ionization variations during the superstorms were mainly due to strong perturbations in the ionospheric F layer, amplified by the plasmasphere’s influence on the vertical total electron content (VTEC). The erosion of the plasmasphere during these two strong events led to a plasmapause located at exceptionally low radial distances smaller than 2 Re (Earth’s radii) in the post-midnight sector and a rotating plume in the afternoon–dusk sector clearly visible in the BSPM plasmasphere model. It took several days after the storms to recover normal ionization rates. Full article

(This article belongs to the Special Issue Ionospheric Disturbances and Space Weather)

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17 pages, 5812 KiB

Open AccessArticle

Significance of Cloud Microphysics and Cumulus Parameterization Schemes in Simulating an Extreme Flood-Producing Precipitation Event in the Central Himalaya

by Ujjwal Tiwari and Andrew B. G. Bush

Atmosphere 2025, 16(3), 298; https://doi.org/10.3390/atmos16030298 - 3 Mar 2025

Between 11 and 14 August 2017, the southern belt of the central Himalaya experienced extreme precipitation, with some stations recording more than 500 mm of accumulated rainfall, which resulted in widespread, devastating flooding. Precipitation was concentrated over the sub-Himalaya, and the established forecasting [...] Read more.

Between 11 and 14 August 2017, the southern belt of the central Himalaya experienced extreme precipitation, with some stations recording more than 500 mm of accumulated rainfall, which resulted in widespread, devastating flooding. Precipitation was concentrated over the sub-Himalaya, and the established forecasting systems failed to predict the event. In this study, we evaluate the performance of six cloud microphysics schemes in the Weather Research and Forecasting (WRF) model forced with the advanced ERA5 dataset. We also examine the importance of the cumulus scheme in WRF at 3 km horizontal grid spacing in highly convective events like this. Six WRF simulations, each with one of the six different microphysics schemes with the Kain–Fritsch cumulus scheme turned off, all fail to reproduce the spatial variability of accumulated precipitation during this devastating flood-producing precipitation event. In contrast, the simulations exhibit greatly improved performance with the cumulus scheme turned on. In this study, the cumulus scheme helps to initiate convection, after which grid-scale precipitation becomes dominant. Amongst the different simulations, the WRF simulation using the Morrison microphysics scheme with the cumulus turned on displayed the best performance, with the smallest normalized root mean square error (NRMSE) of 0.25 and percentage bias (PBIAS) of −6.99%. The analysis of cloud microphysics using the two best-performing simulations reveals that the event is strongly convective, and it is essential to keep the cumulus scheme on for such convective events and capture all the precipitation characteristics showing that in regions of extreme topography, the cumulus scheme is still necessary even down to the grid spacing of at least 3 km. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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19 pages, 8506 KiB

Open AccessArticle

Rapid Intensification of Typhoon Rammasun (2014) with Strong Vertical Wind Shear

by Weiyu Lu and X. San Liang

Atmosphere 2025, 16(3), 297; https://doi.org/10.3390/atmos16030297 - 2 Mar 2025

From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study [...] Read more.

From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study investigates the counterintuive phenomenon, using the multiscale window transform (MWT) and the theory of canonical transfer. For the first time, the diagnostic results show that the strong VWS provided additional available potential energy (APE) to the mid-to-upper troposphere through baroclinic instability. This APE was converted into kinetic energy (KE) via buoyancy conversion and transported to the lower troposphere by pressure gradient, increasing the lower-troposphere wind speed. The strong VWS facilitated the RI in two main ways. First, it was via baroclinic instability. Strong VWS facilitated the transfer of APE from the background flow window to the typhoon scale window, supplying additional APE to the mid-to-upper troposphere, hence enhancing the warm-core structure. Second, the VWS direction shifted from an east-west orientation to a north-south orientation. This directional change put the typhoon’s vertical alignment from a westward tilt back to a straighter one. This effectively suppressed the destructive effects of the asymmetric circulation, and promoted the conversion of APE into KE via buoyancy conversion, hence contributed to the RI. Full article

(This article belongs to the Section Meteorology)

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25 pages, 4118 KiB

Open AccessArticle

Effect of COVID-19 Pandemic on Aircraft Emissions at Václav Havel Airport Prague in 2020

by Bo Stloukal, Jakub Hospodka and Ivan Nagy

Atmosphere 2025, 16(3), 296; https://doi.org/10.3390/atmos16030296 - 1 Mar 2025

As a consequence of measures imposed during the COVID-19 pandemic, anthropogenic emissions worldwide decreased markedly in impacted sectors, including the aviation industry. The aim of this study is to investigate the effects of the pandemic on aircraft emissions below the mixing height (3000 [...] Read more.

As a consequence of measures imposed during the COVID-19 pandemic, anthropogenic emissions worldwide decreased markedly in impacted sectors, including the aviation industry. The aim of this study is to investigate the effects of the pandemic on aircraft emissions below the mixing height (3000 feet above ground) at Václav Havel Airport Prague during 2020. For this purpose, real aircraft emissions during 2020 were computed using provided surveillance data, while business-as-usual aircraft emissions that could have been expected at the airport that year under normal circumstances were estimated using traffic data from previous years and derived emission factors. We found that the median real emissions at the airport in 2020 were 220.859 t of NO_X, 101.364 t of CO, 15.025 t of HC, 44,039.468 t of CO₂, 17,201.825 t of H₂O and 11.748 t of SO₂. The median estimated reduction in emissions due to the pandemic in 2020 was −476.317 t of NO_X, −203.998 t of CO, −28.388 t of HC, −95,957.278 t of CO₂, −37,476.400 t of H₂O and −25.595 t of SO₂. Absolute differences between the real and business-as-usual emissions peaked in June 2020, while the relative differences peaked in April/May at −89.4% to −92.0%. Full article

(This article belongs to the Special Issue Transport Emissions and Their Environmental Impacts)

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17 pages, 3678 KiB

Open AccessArticle

Modeling Greenhouse Gas Emissions from Agriculture

by Alina Bărbulescu

Atmosphere 2025, 16(3), 295; https://doi.org/10.3390/atmos16030295 - 28 Feb 2025

This study analyzes the series of annual emissions of greenhouse gases (GHGs) from agriculture in the European Union countries for 32 years. The outliers, autocorrelation, and change points were detected for each series and the Total one using the boxplot, autocorrelation function (ACF), [...] Read more.

This study analyzes the series of annual emissions of greenhouse gases (GHGs) from agriculture in the European Union countries for 32 years. The outliers, autocorrelation, and change points were detected for each series and the Total one using the boxplot, autocorrelation function (ACF), and Pettit, Hubert, and CUSUM tests. The existence of a monotonic trend in the data series was checked against the randomness by the Mann–Kendall test; further, the slope of the linear trend was determined by Sen’s nonparametric approach and classical regression. The best distribution was fitted for each data series. The results indicate that most series present aberrant values (indicating periods with high emissions), are autocorrelated, and have a decreasing tendency over time (showing the diminishing of GHG emissions from agriculture during the study period). The distributions that best fit the individual series were of Wakeby, Johnson SB, Burr, and Log-logistic type. The Total series has a decreasing trend, presents a second-order autocorrelation, and is right-skewed. An ARIMA(1,1,2) model was built and validated for it and was used for the forecast. Full article

(This article belongs to the Special Issue Characteristics and Trends of Air Pollutants and Their Relationship to Atmospheric Circulations)

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17 pages, 2886 KiB

Open AccessArticle

Classification of Cloud Particle Habits Using Transfer Learning with a Deep Convolutional Neural Network

by Yefeng Xu, Ruili Jiao, Qiubai Li and Minsong Huang

Atmosphere 2025, 16(3), 294; https://doi.org/10.3390/atmos16030294 - 28 Feb 2025

The habits of cloud particles are a significant factor impacting microphysical processes in clouds. The accurate identification of cloud particle shapes within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we established a cloud particle image dataset [...] Read more.

The habits of cloud particles are a significant factor impacting microphysical processes in clouds. The accurate identification of cloud particle shapes within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we established a cloud particle image dataset encompassing nine distinct habit categories, totaling 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-Dimensional Stereo Probe (2D-S), and the High-Volume Precipitation Spectrometer (HVPS). Furthermore, this study performs a comparative analysis of ten different transfer learning (TL) models based on this dataset. It was found that the VGG-16 model exhibits the highest classification accuracy, reaching 97.90%. This model also demonstrates the highest recall, precision, and F1 measure. The results indicate that the VGG-16 model can reliably classify the shapes of ice crystal particles measured by both line scan imagers (2D-S, HVPS) and an area scan imager (CPI). Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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19 pages, 997 KiB

Open AccessArticle

Developing Emission Factors from Real-World Emissions of Euro VI Urban Diesel, Diesel-Hybrid, and Compressed Natural Gas Buses

by Michail Perdikopoulos, Traianos Karageorgiou, Leonidas Ntziachristos, Laure Deville Cavellin, Fabrice Joly, Jeremy Vigneron, Adrian Arfire, Christophe Debert, Olivier Sanchez, François Gaie-Levrel and Hélène Marfaing

Atmosphere 2025, 16(3), 293; https://doi.org/10.3390/atmos16030293 - 28 Feb 2025

Urban transportation is a key contributor to air pollution in cities. While urban buses impact air quality, they also promote sustainable mobility. In the Paris region, buses account for approximately 4% of traffic emissions. This study addresses the gap in real-world emissions data [...] Read more.

Urban transportation is a key contributor to air pollution in cities. While urban buses impact air quality, they also promote sustainable mobility. In the Paris region, buses account for approximately 4% of traffic emissions. This study addresses the gap in real-world emissions data for Euro VI diesel, diesel-hybrid, and compressed natural gas (CNG) urban buses by developing speed-dependent emission factors for CO, NO_X, SPN₂₃, and energy consumption. An optimized methodology was applied to portable emission measurement system data collected from 28 urban buses across various routes in the Paris metropolitan area, capturing emissions across different speeds and traffic conditions. Results showed that diesel buses emit around 2 g/km of NOx at low speeds, compared to 1.4 g/km for diesel hybrids and 0.6 g/km for CNG. CO emissions reached approximately 1 g/km for CNG and 0.5 g/km for diesel, while SPN₂₃ emissions for all powertrains were in the order of 10¹² particles/km. The resulting speed-dependent emission factors were incorporated into COPERT version 5.8, the European Union’s standard emission inventory software, improving the inventory accuracy. The findings underscore the need for additional reductions in air pollutant emissions to meet Euro 7 standards and provide a robust framework for improving air quality management. Full article

(This article belongs to the Section Air Quality)

48 pages, 13145 KiB

Open AccessReview

Time-Series Data-Driven PM_2.5 Forecasting: From Theoretical Framework to Empirical Analysis

by Chunlai Wu, Ruiyang Wang, Siyu Lu, Jiawei Tian, Lirong Yin, Lei Wang and Wenfeng Zheng

Atmosphere 2025, 16(3), 292; https://doi.org/10.3390/atmos16030292 - 28 Feb 2025

PM_2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM_2.5 prediction models to support decision-making and reduce risks. This review comprehensively explores the progress of PM_2.5 [...] Read more.

PM_2.5 in air pollution poses a significant threat to public health and the ecological environment. There is an urgent need to develop accurate PM_2.5 prediction models to support decision-making and reduce risks. This review comprehensively explores the progress of PM_2.5 concentration prediction, covering bibliometric trends, time series data characteristics, deep learning applications, and future development directions. This article obtained data on 2327 journal articles published from 2014 to 2024 from the WOS database. Bibliometric analysis shows that research output is growing rapidly, with China and the United States playing a leading role, and recent research is increasingly focusing on data-driven methods such as deep learning. Key data sources include ground monitoring, meteorological observations, remote sensing, and socioeconomic activity data. Deep learning models (including CNN, RNN, LSTM, and Transformer) perform well in capturing complex temporal dependencies. With its self-attention mechanism and parallel processing capabilities, Transformer is particularly outstanding in addressing the challenges of long sequence modeling. Despite these advances, challenges such as data integration, model interpretability, and computational cost remain. Emerging technologies such as meta-learning, graph neural networks, and multi-scale modeling offer promising solutions while integrating prediction models into real-world applications such as smart city systems can enhance practical impact. This review provides an informative guide for researchers and novices, providing an understanding of cutting-edge methods, practical applications, and systematic learning paths. It aims to promote the development of robust and efficient prediction models to contribute to global air pollution management and public health protection efforts. Full article

(This article belongs to the Special Issue New Insights into Exposure and Health Impacts of Air Pollution)

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15 pages, 15327 KiB

Open AccessTechnical Note

Establishment and Operation of an Early Warning Service for Agrometeorological Disasters Customized for Farmers and Extension Workers at Metropolitan-Scale

by Yong-Soon Shin, Hee-Ae Lee, Sang-Hyun Park, Yong-Kyu Han, Kyo-Moon Shim and Se-Jin Han

Atmosphere 2025, 16(3), 291; https://doi.org/10.3390/atmos16030291 - 28 Feb 2025

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For [...] Read more.

A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers. Full article

(This article belongs to the Special Issue ICT-Based High-Resolution Weather and Climate Research for an Early-Warning System for Agricultural Disasters)

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15 pages, 5421 KiB

Open AccessArticle

Indoor Radon Monitoring in Residential Areas in the Vicinity of Coal Mining Operations in the Mpumalanga Province, South Africa

by Paballo M. Moshupya, Seeke C. Mohuba, Tamiru A. Abiye, Ian Korir and Sifiso Nhleko

Atmosphere 2025, 16(3), 290; https://doi.org/10.3390/atmos16030290 - 28 Feb 2025

Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and [...] Read more.

Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and for heating, predominantly in informal settlements and township communities. Most of the existing coal-fired power plants are found in the Mpumalanga province. This paper presents long-term radon (²²²Rn) measurements in dwellings surrounding coal mining centres in the Mpumalanga province and evaluates their contributions to indoor radon exposures. The indoor radon measurements were conducted using solid-state nuclear track detectors and were performed during warm and cold seasons. It was found that the overall indoor radon activity concentrations ranged between 21 Bq/m³ and 145 Bq/m³, with a mean value of 40 Bq/m³. In all the measured dwellings, the levels were below the WHO reference level of 100 Bq/m³ and 300 Bq/m³ reference level recommended by the IAEA and ICRP, with the exception of one dwelling that was poorly ventilated. The results reveal that individuals residing in the surveyed homes are not exposed to radon levels higher than the WHO, ICRP, and IAEA reference levels. The main source influencing indoor radon activity concentrations was found to be primarily the concentration of uranium found in the geological formations in the area, with ventilation being an additional contributing factor of radon levels in dwellings. To maintain good air quality in homes, it is recommended that household occupants should keep their dwellings well ventilated to keep indoor radon levels as low as possible. Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

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18 pages, 3764 KiB

Open AccessArticle

The Sensitivity of Heatwave Climatology to Input Gridded Datasets: A Case Study of Ukraine

by Oleg Skrynyk, Enric Aguilar and Caterina Cimolai

Atmosphere 2025, 16(3), 289; https://doi.org/10.3390/atmos16030289 - 28 Feb 2025

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived [...] Read more.

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950–2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to developing climate adaptation strategies for extreme temperature events in Ukraine, its neighboring countries, and potentially across Europe. Full article

(This article belongs to the Section Climatology)

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