Atmosphere

20 pages, 8772 KB

Open AccessArticle

An Assessment of the Applicability of ERA5 Reanalysis Boundary Layer Data Against Remote Sensing Observations in Mountainous Central China

by Jinyu Wang, Zhe Li, Yun Liang and Jiaying Ke

Atmosphere 2025, 16(10), 1152; https://doi.org/10.3390/atmos16101152 - 1 Oct 2025

Abstract

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected [...] Read more.

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

(This article belongs to the Special Issue Data Analysis in Atmospheric Research)

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17 pages, 1225 KB

Open AccessArticle

Assessment of the ZJWARMS Forecast Model’s Adaptability and AI-Based Bias Correction over Complex Terrain

by Qi Zhang, Yiwen Shi, Yifan Wang, Shiyun Mou, Zhidan Zhu, Tu Qian, Zhijun Mao, Shujie Yuan, Lin Han and Xiaocan Lao

Atmosphere 2025, 16(10), 1151; https://doi.org/10.3390/atmos16101151 - 1 Oct 2025

Abstract

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake [...] Read more.

This study assesses the efficacy of the ZJWARMS model’s AI-based post-processing correction method for temperature and wind speed forecasts in complex terrain. By analyzing 72 h forecasts at four stations with varying elevations (from 273 m to 1327 m) in the Liuchun Lake region during December 2021–December 2022, the study found that AI-based corrections substantially enhanced both forecast accuracy and stability. The results indicate that, after correction, temperature forecast accuracy at all stations exceeded 99%, with the most notable relative gains at higher elevations (up to 48.1%). The mean absolute error (MAE) for temperature declined from 3.08 °C to below 0.8 °C at Octagonal Palace, and from 3.29 °C to below 0.6 °C at Mountaintop. Wind speed forecast accuracy also increased from approximately 60–70% to nearly 100%, with MAE generally constrained to the range of 0.2–0.4 m/s. In terms of extreme error control, the number of samples with temperature errors exceeding ±2 °C was markedly reduced. For instance, at Mountainside, the count dropped from 127 to 0. Extreme wind speed errors were also effectively eliminated. After correction, error distributions became more concentrated, and both temporal stability and spatial consistency showed notable improvement. These gains enhance operational forecasting and risk management in mountainous regions, for example, through threshold-based wind-hazard alerts and support for mountain-road icing, by providing more reliable, high-confidence guidance. Full article

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

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20 pages, 25657 KB

Open AccessArticle

Regional Divergence in Long-Term Trends of the Marine Heatwave over the East China Sea

by Qun Ma, Zhao-Jun Liu, Wenbin Yin, Ming-Xuan Lu and Jun-Bo Ma

Atmosphere 2025, 16(10), 1150; https://doi.org/10.3390/atmos16101150 - 1 Oct 2025

Abstract

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence [...] Read more.

Marine heatwaves (MHWs) pose a serious threat to the marine ecosystems and fishery resources in the East China Sea (ECS). Based on National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature High Resolution version 2 data, this study investigated the regional divergence in long-term trends of MHWs in the ECS from 1982 to 2023. The principal findings were as follows. Concerning MHWs, the coastal waters of China from northern Jiangsu coast to northeast of Taiwan Island experienced a relatively high annual average frequency, the longest duration, largest number of total days, strongest intensity, and the most pronounced seasonal signals. Additionally, the areas along the Kuroshio path showed significant levels of frequency, duration, and total days, but with comparatively weak intensity. In the empirical orthogonal function (EOF) analysis, EOF1 of the total days and cumulative intensity exhibited notable variation along the path of the Kuroshio and its offshoots, and in Chinese coastal areas. EOF2 showed significantly more conspicuous variation in areas extending from the Yangtze River Estuary to the northern Jiangsu coast. Furthermore, the MHW indices generally showed a positive trend in the ECS from 1982 to 2023. Importantly, the regions with high annual average MHW indices were also characterized by a significantly positive increasing trend. Moderate (79.10%) and strong (19.94%) events were most prevalent, whereas severe (0.82%) and extreme (0.14%) events occurred infrequently. The enhanced solar radiation and the reduced latent heat loss were the main contributing factors of MHWs in the ECS. These findings provide valuable insights into the ecological environment and resources of the ECS as a marine pastoral area. Full article

(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change (2nd Edition))

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23 pages, 6455 KB

Open AccessArticle

Detection of Sulfur from Industrial Emissions Across a Complex Mountainous Landscape: An Isotope Approach Using Plant-Based Biomonitors in Winter

by Ann-Lise Norman, Sunita LeGallou, Erin E. Caldwell, Patrick M. Blancher, Jelena Matic and Ralph Cartar

Atmosphere 2025, 16(10), 1149; https://doi.org/10.3390/atmos16101149 - 30 Sep 2025

Abstract

Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope [...] Read more.

Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope composition in moss (using moss bags and controls) as biomonitors in a region of southern Alberta, Canada influenced by industrial emissions. Tree rings allow comparisons of historical to current sulfur deposition patterns. Moss, which integrates atmospheric nutrients during growth, allows for concurrent comparisons. The contrast of inorganic and organic sulfur within conifer tree needles provides a measure of pollutant uptake over their short lifespans. Sulfur uptake within biomonitors in a southern Alberta ecosystem allow assessment of the presence (in moss, needles) and effects (on conifer growth) of atmospheric sulfur deposition from industrial emissions. These data were examined relative to California Puff (CALPuff) model projections and traditional active and passive air quality sampling. Patterns in sulfur isotope abundance (δ³⁴S) from moss bags placed throughout the eastern slopes of the southern Alberta foothills of the Rocky Mountains implicate local industry as the dominant atmospheric sulfur source over winter, with the tissues of conifers (needles and cores) and moss decreasing with distance from industrial emissions. This was consistent with apportionment calculations based on active and passive sampling, which also showed a surprising trend of sulfur deposition upwind of the industrial stack in the mountains to the west. δ³⁴S values for pine needles and tree rings were consistent with greater sulfur stress and reductions in tree growth associated with increased industrial sulfur concentrations and deposition. We conclude that plant biomonitors are effective short-term (tree needles and moss) and long-term (tree cores) indicators of sulfur pollution in a complex, mountainous landscape. Full article

(This article belongs to the Special Issue Biomonitoring—an Effective Tool for Air Pollution Assessment (2nd Edition))

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15 pages, 1897 KB

Open AccessArticle

Sources and Reactivity of Ambient VOCs on the Tibetan Plateau: Insights from a Multi-Site Campaign (2012–2014) for Assessing Decadal Change

by Fangkun Wu, Jie Sun, Yinghong Wang and Zirui Liu

Atmosphere 2025, 16(10), 1148; https://doi.org/10.3390/atmos16101148 - 30 Sep 2025

Abstract

Investigating atmospheric volatile organic compounds (VOCs) is critical for understanding their sources, chemical reactivity, and impacts on air quality, climate, and human health, especially in remote regions like the Tibetan Plateau where baseline data remains scarce. In this study, ambient VOCs species were [...] Read more.

Investigating atmospheric volatile organic compounds (VOCs) is critical for understanding their sources, chemical reactivity, and impacts on air quality, climate, and human health, especially in remote regions like the Tibetan Plateau where baseline data remains scarce. In this study, ambient VOCs species were simultaneously measured at four remote background sites on the Tibetan Plateau (Nyingchi, Namtso, Ngari, and Mount Everest) from 2012 to 2014 to investigate their concentration, composition, sources, and chemical reactivity. Weekly integrated samples were collected and analyzed using a Gas Chromatograph-Mass Spectrometer/Flame Ionization Detector (GC-MS/FID) system. The total VOC mixing ratios exhibited site-dependent variability, with the highest levels observed in Nyingchi, followed by Mount Everest, Ngari and Namtso. The VOC composition in those remote sites was dominated by alkanes (25.7–48.5%) and aromatics (11.4–34.7%), followed by halocarbons (19.1–28.1%) and alkenes (11.5–18.5%). A distinct seasonal trend was observed, with higher VOC concentrations in summer and lower levels in spring and autumn. Source analysis based on correlations between specific VOC species suggests that combustion emissions (e.g., biomass burning or residential heating) were a major contributor during winter and spring, while traffic-related emissions influenced summer VOC levels. In addition, long-range transport of pollutants from South Asia also significantly impacted VOC concentrations across the plateau. Furthermore, reactivity assessments indicated that alkenes were the dominant contributors to OH radical loss rates, whereas aromatics were the largest drivers of ozone formation potential (OFP). These findings highlight the complex interplay of local emissions and regional transport in shaping VOC chemistry in this high-altitude background environment, with implications for atmospheric oxidation capacity and secondary pollutant formation. Full article

(This article belongs to the Special Issue Atmospheric Chemistry in Urban Environments: Insights into Organic Compounds, Aerosols, and Haze Formation Mechanisms)

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14 pages, 5022 KB

Open AccessArticle

PM_2.5 Concentration Prediction Model Utilizing GNSS-PWV and RF-LSTM Fusion Algorithms

by Mingsong Zhang, Li Li, Galina Dick, Jens Wickert, Huafeng Ma and Zehua Meng

Atmosphere 2025, 16(10), 1147; https://doi.org/10.3390/atmos16101147 - 30 Sep 2025

Abstract

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along [...] Read more.

Inadequate screening of features and insufficient extraction of multi-source time-series data potentially result in insensitivity to historical noise and poor extraction of features for PM_2.5 concentration prediction models. Precipitable water vapor (PWV) data obtained from the Global Navigation Satellite System (GNSS), along with air quality and meteorological data collected in Suzhou city from February 2021 to July 2023, were employed in this study. The Spearman correlation analysis and Random Forest (RF) feature importance assessment were used to select key input features, including PWV, PM₁₀, O₃, atmospheric pressure, temperature, and wind speed. Based on RF, Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP) algorithms, four PM_2.5 concentration prediction models were developed using sliding window and fusion algorithms. Experimental results show that the root mean square error (RMSE) of the 1 h PM_2.5 concentration prediction model using the RF-LSTM fusion algorithm is 4.36

μ g / m^{3}

, while its mean absolute error (MAE) and mean absolute percentage error (MAPE) values are 2.63

μ g / m^{3}

and 9.3%. Compared to the individual LSTM and MLP algorithms, the RMSE of the RF-LSTM PM_2.5 prediction model improves by 34.7% and 23.2%, respectively. Therefore, the RF-LSTM fusion algorithm significantly enhances the prediction accuracy of the 1 h PM_2.5 concentration model. As for the 2 h, 3 h, 6 h, 12 h, and 24 h PM_2.5 prediction models using the RF-LSTM fusion algorithm, their RMSEs are 5.6

μ g / m^{3}

, 6.9

μ g / m^{3}

, 9.9

μ g / m^{3}

, 12.6

μ g / m^{3}

, and 15.3

μ g / m^{3}

, and their corresponding MAPEs are 13.8%, 18.3%, 28.3%, 38.2%, and 48.2%, respectively. Their prediction accuracy decreases with longer forecasting time, but they can effectively capture the fluctuation trends of future PM_2.5 concentrations. The RF-LSTM PM_2.5 prediction models are efficient and reliable for early warning systems in Suzhou city. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment (2nd Edition))

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15 pages, 3399 KB

Open AccessArticle

On the Quasi-Steady Vorticity Balance in the Mature Stage of Hurricane Irma (2017)

by Jasper de Jong, Aarnout J. van Delden and Michiel L. J. Baatsen

Atmosphere 2025, 16(10), 1146; https://doi.org/10.3390/atmos16101146 - 29 Sep 2025

Abstract

Vorticity budgets in traditional height or pressure coordinates are commonly examined to help explain how tropical cyclones evolve over time. One disadvantage of using these coordinates is that the vorticity flux due to diabatic heating cannot be easily assessed. Isentropic coordinates naturally lend [...] Read more.

Vorticity budgets in traditional height or pressure coordinates are commonly examined to help explain how tropical cyclones evolve over time. One disadvantage of using these coordinates is that the vorticity flux due to diabatic heating cannot be easily assessed. Isentropic coordinates naturally lend themselves to determine the effect of diabatic heating—the vorticity budget simplifies, and a clear-cut distinction can be made between adiabatic (advective) and diabatic vorticity fluxes. Above the boundary layer, advective vorticity fluxes alone would lead to a quick spin-down of the mature tropical cyclone. Do diabatic processes prevent this from happening? If so, how? This paper investigates the vorticity budget of Hurricane Irma (2017) in its mature quasi-steady phase. We analyse a simulation of Irma with an operational high-resolution weather forecasting model. During Irma’s remarkably long period (37 h) of steady peak intensity, the radially outward advective isentropic vorticity flux in the eyewall above the boundary layer is balanced by a radially inward diabatic isentropic vorticity flux. Frictional effects and asymmetrical flow properties are of little importance to the maintenance of cyclone intensity in its mature phase, provided enough latent heat is released in the eyewall to maintain an inward vorticity flux that balances the advective flux. Full article

(This article belongs to the Section Meteorology)

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17 pages, 4320 KB

Open AccessArticle

Can Heat Waves Fully Capture Outdoor Human Thermal Stress? A Pilot Investigation in a Mediterranean City

by Serena Falasca, Ferdinando Salata, Annalisa Di Bernardino, Anna Maria Iannarelli and Anna Maria Siani

Atmosphere 2025, 16(10), 1145; https://doi.org/10.3390/atmos16101145 - 29 Sep 2025

Abstract

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human [...] Read more.

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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37 pages, 3856 KB

Open AccessArticle

Urban Health Assessment Through a Planetary Health Perspective: Methods and First Results from the Rome NBFC Experiment

by Carmina Sirignano, Daiane De Vargas Brondani, Gianluca Di Iulio, Chiara Anselmi, Stefania Argentini, Alessandro Bracci, Carlo Calfapietra, Silvia Canepari, Giampietro Casasanta, Giorgio Cattani, Simona Ceccarelli, Hellas Cena, Tony Christian Landi, Rosa Coluzzi, Rachele De Giuseppe, Stefano Decesari, Annalisa Di Cicco, Alessandro Domenico Di Giosa, Luca Di Liberto, Alessandro Di Menno di Bucchianico, Marisa Di Pietro, Oxana Drofa, Simone Filardo, Raffaela Gaddi, Alessandra Gaeta, Clarissa Gervasoni, Alessandro Giammona, Michele Pier Luca Guarino, Laura De Gara, Maria Cristina Facchini, Vito Imbrenda, Antonia Lai, Stefano Listrani, Alessia Lo Dico, Lorenzo Marinelli, Lorenzo Massimi, Maria Cristina Monti, Luca Mortarini, Marco Paglione, Ferdinando Pasqualini, Danilo Ranieri, Laura Restaneo, Matteo Rinaldi, Eleonora Rubin, Andrea Scartazza, Rosa Sessa, Alice Traversa, Lina Fusaro, Annamaria Altomare, Gloria Bertoli and Francesca Costabile Show full author list Hide full author list

Atmosphere 2025, 16(10), 1144; https://doi.org/10.3390/atmos16101144 - 29 Sep 2025

Abstract

Addressing the planetary crisis associated with climate change, biodiversity loss, global pollution, and public health requires novel and holistic approaches. Here, we present the methodology and initial results of an experiment conducted in Rome within the framework of the National Biodiversity Future Center [...] Read more.

Addressing the planetary crisis associated with climate change, biodiversity loss, global pollution, and public health requires novel and holistic approaches. Here, we present the methodology and initial results of an experiment conducted in Rome within the framework of the National Biodiversity Future Center (NBFC) project, Spoke 6. The major objective of this study was to outline the planetary health approach as a lens to assess urban health. This transdisciplinary case study explored the relationship between urban traffic-related external exposome and pro-oxidative responses in humans and plants. This methodology is based on the integration of atmospheric dynamics modeling, state-of-the-art aerosol measurements, biomonitoring in human cohorts, in vitro cellular assays, and the assessment of functional trait markers in urban trees. The results indicate that short-term exposure to urban aerosols, even at low concentrations, triggers rapid oxidative and inflammatory responses in bronchial epithelial cells, modulates gene and miRNA expression, alters gut microbiota diversity, and induces functional trait changes in urban trees. This study also highlights the feedback mechanisms between vegetation and atmospheric conditions, emphasizing the role of urban greenery in modulating microclimate and exposure. The methodology and initial results presented here will be further analyzed in future studies to explore proof of a cause–effect relationship between short-term exposure to traffic-related environmental stressors in urban areas and oxidative stress in humans and plants, with implications for chronic responses. In a highly urbanized world, this evidence could be pivotal in motivating the widespread implementation of planetary health approaches for assessing urban health. Full article

(This article belongs to the Section Air Quality and Health)

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18 pages, 15249 KB

Open AccessArticle

Responses of the East Asian Winter Climate to Global Warming in CMIP6 Models

by Yuxi Jiang, Yutao Chi, Weidong Wang, Wenshan Li, Hui Wang and Jianxiang Sun

Atmosphere 2025, 16(10), 1143; https://doi.org/10.3390/atmos16101143 - 29 Sep 2025

Abstract

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the [...] Read more.

Global warming has been altering the East Asian climate at an unprecedented rate since the 20th century. In order to evaluate the changes in the East Asian winter climate (EAWC) and support policy-making for climate mitigation and adaptation strategies, this paper utilizes the multimodel ensemble from the Couple Model Intercomparison Project 6 and a temperature threshold method to investigate the EAWC changes during the period 1979–2100. The results show that the EAWC has been undergoing widespread and robust changes in response to global warming. The winter length in East Asia has shortened and will continue shortening owing to later onsets and earlier withdrawals, leading to a drastic contraction in length from 100 days in 1979 to 43 days (27 days) in 2100 under SSP2-4.5 (SSP5-8.5). While most regions of the East Asian continent are projected to become warmer in winter, the Japan and marginal seas of northeastern Asia will face the risks from colder winters with more frequent extreme cold events, accompanied by less precipitation. Meanwhile, the Tibetan Plateau is very likely to have colder winters in the future, though its surface snow amounts will significantly decline. Greenhouse gas (GHG) emissions are found to be responsible for the EAWC changes. GHG traps heat inside the Earth’s atmosphere and notably increases the air temperature; moreover, its force modulates large-scale atmospheric circulation, facilitating an enhanced and northward-positioned Aleutian low together with a weakened Siberian high, East Asian trough, and East Asian jet stream. These two effects work together, resulting in a contracted winter with robust and uneven regional changes in the EAWC. This finding highlights the urgency of curbing GHG emissions and improving forecasts of the EAWC, which are crucial for mitigating their major ecological and social impacts. Full article

(This article belongs to the Section Climatology)

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18 pages, 5196 KB

Open AccessArticle

How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015)

by Lin Wang, Hong Huang, Ju Wang, Xinjie Ouyang, Xiaolin Ma and Zhen Wang

Atmosphere 2025, 16(10), 1142; https://doi.org/10.3390/atmos16101142 - 28 Sep 2025

Abstract

A comprehensive understanding of the evolution and phase transitions of hydrometeors during the development of tropical cyclones (TCs) is essential for advancing research on the mechanisms of TC intensity change. In this study, utilizing the Weather Research and Forecasting numerical model, we simulate [...] Read more.

A comprehensive understanding of the evolution and phase transitions of hydrometeors during the development of tropical cyclones (TCs) is essential for advancing research on the mechanisms of TC intensity change. In this study, utilizing the Weather Research and Forecasting numerical model, we simulate the evolution of Super Typhoon Nangka (No. 1511), explore the relationship between the TC intensity variations and the internal hydrometeor distribution, and examine the secondary circulation characteristics. The results indicate that the total content of hydrometeor particles increased during the intensification of Typhoon Nangka. Ice-phase particles expanded outward radially as the typhoon intensified, while liquid-phase particles contracted inward. Ice-phase hydrometeor distributions varied in conjunction with TC intensity variations, whereas liquid-phase hydrometeor variations were closely related to the complex dynamic–thermodynamic–microphysical processes within the typhoon. The spatial pattern of the secondary circulation exhibits high consistency with the distribution of hydrometeor particles. Low-level radial inflow, upper-level radial outflow, and middle-level vertical updrafts played dominant roles in regulating the distribution and transport of particles at different stages. The intensification of Typhoon Nangka was primarily driven by water vapor convergence and the latent heat released by ascending liquid-phase particles near the eyewall, while the stagnation of its intensification was mainly attributed to the resistance exerted by descending ice-phase particles from upper levels and the heat consumption associated with their melting. These findings provide a foundation for better understanding how hydrometeors modulate TC intensity variations and offer valuable insights into energy conversion mechanisms during hydrometeor phase transitions under the influence of secondary circulations. Full article

(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))

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12 pages, 3514 KB

Open AccessArticle

Evaluation of Road Dust Resuspension from Internal Combustion Engine and Electric Vehicles of the Same Model

by Worawat Songkitti, Sirasak Pong-A-Mas, Chawwanwit Boonsom, Tanet Aroonsrisopon and Ekathai Wirojsakunchai

Atmosphere 2025, 16(10), 1141; https://doi.org/10.3390/atmos16101141 - 28 Sep 2025

Abstract

As many countries transition to electric vehicles (EVs) to reduce tailpipe emissions from internal combustion engine vehicles (ICEVs), both vehicle types continue to generate non-exhaust particulate matter (PM), including tire wear, brake wear, road surface wear, and particularly road dust resuspension. Among these, [...] Read more.

As many countries transition to electric vehicles (EVs) to reduce tailpipe emissions from internal combustion engine vehicles (ICEVs), both vehicle types continue to generate non-exhaust particulate matter (PM), including tire wear, brake wear, road surface wear, and particularly road dust resuspension. Among these, road dust resuspension is a major contributor to non-exhaust PM. While factors such as vehicle weight and drivetrain configuration have been extensively studied in fleet-level research, direct comparisons between ICEVs and EVs of the same model have not been explored. This study investigates the effects of drivetrain, vehicle weight, and payload on road dust resuspension emissions from ICEV and EV models. Two experimental approaches were employed: (1) acceleration from 0 to 60 km/h, and (2) a simulated real-world driving cycle (RDC). Each test was conducted under both light and heavy payload conditions. The results show that the EV consistently emitted more PM than the ICEV during both acceleration and RDC tests, based on factory-standard vehicle weights. Under identical vehicle weight conditions, the EV demonstrated higher PM resuspension levels, likely due to its higher torque and more immediate power delivery, which increases friction between the tires and the road, particularly during rapid acceleration. Both vehicle types exhibited significant increases in PM emissions under heavy payload conditions. These findings underscore the importance of addressing non-exhaust emissions from EVs, particularly road dust resuspension, and highlight the need for further research into mitigation strategies, such as vehicle lightweighting. Full article

(This article belongs to the Special Issue Brake and Tire Non-Exhaust Emissions and Air Pollution)

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18 pages, 13697 KB

Open AccessArticle

A New Anticyclone Identification Method Based on Mask R-CNN Model and Its Application

by Yang Kong, Hao Wu, Ping Xia and Yumin Zhang

Atmosphere 2025, 16(10), 1140; https://doi.org/10.3390/atmos16101140 - 28 Sep 2025

Abstract

In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method [...] Read more.

In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method using the Mask region-based convolutional neural network (Mask R-CNN) model to detect synoptic-scale anticyclones by capturing their two-dimensional structural features and investigating their relationship with snow-ice disasters in Northeast China. It is found that compared with traditional objective identification methods, the new method better captures the overall structural characteristics of anticyclones, significantly improving the description of large-scale, strong anticyclones. Specifically, it incorporates 7.3% of small-scale anticyclones into larger-scale systems. Anticyclones are closely correlated with local cooling and cold air mass changes over Northeast China, with 60% of anticyclones accompanying regional cold air mass accumulation and temperature drops. Two case studies of the rare rain-snow and cold wave events revealed that these events were preceded by the generation and eastward expansion of an upstream anticyclone identified by the new method. This demonstrates that the proposed method can effectively track anticyclones and the evolution of cold high-pressure systems, providing insights into extreme cold events. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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21 pages, 9610 KB

Open AccessArticle

Global Ionosphere Total Electron Content Prediction Based on Bidirectional Denoising Wavelet Transform Convolution

by Liwei Sun, Guoming Yuan, Huijun Le, Xingyue Yao, Shijia Li and Haijun Liu

Atmosphere 2025, 16(10), 1139; https://doi.org/10.3390/atmos16101139 - 28 Sep 2025

Abstract

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only [...] Read more.

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

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

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22 pages, 14363 KB

Open AccessArticle

Aerosol Transport from Amazon Biomass Burning to Southern Brazil: A Case Study of an Extreme Event During September 2024

by Fernando Primo Forgioni, Caroline Bresciani, André Reis, Gabriela Viviana Müller, Dirceu Luis Herdies, Jório Bezerra Cabral Júnior and Fabrício Daniel dos Santos Silva

Atmosphere 2025, 16(10), 1138; https://doi.org/10.3390/atmos16101138 - 27 Sep 2025

Abstract

Biomass burning in the Amazon region, especially during the dry season, generates aerosol dispersion events across the southern part of the continent, with impacts observable thousands of kilometers from the emission source. This study presents a long-range aerosol transport case from September 2024, [...] Read more.

Biomass burning in the Amazon region, especially during the dry season, generates aerosol dispersion events across the southern part of the continent, with impacts observable thousands of kilometers from the emission source. This study presents a long-range aerosol transport case from September 2024, in which smoke aerosols from forest fires in the central Amazon reached southeastern and southern Brazil, affecting the air quality in distant areas such as São Paulo and São Martinho. The event was simulated using the Weather Research and Forecasting model with Chemistry (WRF-Chem), configured with the MOZCART chemical mechanism, combined with MERRA-2 reanalysis data and by using the 3BEM biomass burning emission inventory. Satellite datasets from MODIS and MERRA-2 reanalysis were used to evaluate the model’s performance. The results indicate that the South American Low-Level Jet (SALLJ) played a key role in transporting carbonaceous aerosols over long distances. The model successfully captured the spatial and temporal evolution of the aerosol plume and its impacts, although it tended to underestimate aerosol optical depth (AOD) values compared with satellite observations. This study highlights the WRF-Chem’s capability to simulate extreme smoke transport events in South America and supports its potential application in forecasting and air quality assessments. Full article

(This article belongs to the Section Aerosols)

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20 pages, 6389 KB

Open AccessArticle

Study on Characteristics and Numerical Simulation of a Convective Low-Level Wind Shear Event at Xining Airport

by Juan Gu, Yuting Qiu, Shan Zhang, Xinlin Yang, Shi Luo and Jiafeng Zheng

Atmosphere 2025, 16(10), 1137; https://doi.org/10.3390/atmos16101137 - 27 Sep 2025

Abstract

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including [...] Read more.

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

(This article belongs to the Section Meteorology)

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23 pages, 348 KB

Open AccessReview

Machine Learning-Based Quality Control for Low-Cost Air Quality Monitoring: A Comprehensive Review of the Past Decade

by Yong-Hyuk Kim and Seung-Hyun Moon

Atmosphere 2025, 16(10), 1136; https://doi.org/10.3390/atmos16101136 - 27 Sep 2025

Abstract

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine [...] Read more.

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))

26 pages, 724 KB

Open AccessReview

Indoor Air Pollution of Volatile Organic Compounds (VOCs) in Hospitals in Thailand: Review of Current Practices, Challenges, and Recommendations

by Wissawa Malakan, Sarin KC, Thanakorn Jalearnkittiwut and Wilasinee Samniang

Atmosphere 2025, 16(10), 1135; https://doi.org/10.3390/atmos16101135 - 27 Sep 2025

Abstract

Indoor air pollution has become a significant concern, contributing to the decline in air quality through the presence of gaseous pollutants and particulate matter, especially under poor ventilation. Hospitals, functioning as non-industrial microenvironments, particularly in Thailand, face challenges due to insufficient and incomplete [...] Read more.

Indoor air pollution has become a significant concern, contributing to the decline in air quality through the presence of gaseous pollutants and particulate matter, especially under poor ventilation. Hospitals, functioning as non-industrial microenvironments, particularly in Thailand, face challenges due to insufficient and incomplete databases for effective air quality management. Within these environments, patients with heightened sensitivity, along with hospital staff who are predominantly exposed indoors, face increased risk of exposure to indoor air pollutants. This study aimed to review current evidence on VOCs in hospital settings in Thailand, identifying their sources, concentrations, and health impacts. It also aimed to provide recommendations for improved air quality monitoring and management. The review included studies published between 2008 and 2023 in English or Thai. Studies were selected based on relevance to VOCs in hospital environments, while excluding those lacking sufficient data or methodological rigor. Literature searches were conducted using Google Scholar, ScienceDirect, Scopus, and PubMed. Results from international studies were also considered to address gaps. Data extraction focused on VOC sources, concentrations, measurement methods, and associated health impacts. Results were synthesized into six thematic categories: characterization, health effects, control measures, etiological studies, monitoring systems, and comparative studies. The review identified 87 relevant studies. VOC exposure was associated with several adverse health impacts resulting from short- and long-term exposures, leading to an increased risk of cancer. Identified sources of VOC emissions within hospitals encompass anesthetic gases, sterilization processes, pharmaceuticals, laboratory chemicals, patient care, and household products, as well as building materials and furnishings. Commonly encountered VOCs include alcohols (e.g., ethanol, 2-methyl-2-propanol, isopropanol), ether, isoflurane, nitrous oxide, sevoflurane, chlorine, formaldehyde, aromatic hydrocarbons, limonene, and glutaraldehyde, among those commonly detected in hospital environments. Yet, limited knowledge exists regarding their source contributions, emissions, and concentrations associated with health impacts in Thai hospitals. Full article

(This article belongs to the Special Issue Indoor Air Pollution: A Silent Threat to Human Health and the Atmosphere)

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22 pages, 6860 KB

Open AccessArticle

Comparative Analysis of Summer Deep Convection Systems over the Tibetan Plateau and Sichuan Basin

by Xin Yan, Quanliang Chen, Yang Li and Yujing Liao

Atmosphere 2025, 16(10), 1134; https://doi.org/10.3390/atmos16101134 - 27 Sep 2025

Abstract

Based on GPM satellite observations during June to September from 2014 to 2023, deep convective systems (DCSs) over the Tibetan Plateau and Sichuan Basin exhibited distinct spatiotemporal and structural characteristics. Over the Plateau, DCSs were primarily concentrated in the central and eastern regions, [...] Read more.

Based on GPM satellite observations during June to September from 2014 to 2023, deep convective systems (DCSs) over the Tibetan Plateau and Sichuan Basin exhibited distinct spatiotemporal and structural characteristics. Over the Plateau, DCSs were primarily concentrated in the central and eastern regions, with echo-top heights typically ranging from 15 to 17 km and 40 dBZ echo tops mostly found between 6 and 8 km. In contrast, the Basin displayed a more spatially uniform distribution of convection, characterized by lower echo-top heights (12–14 km) and higher 40 dBZ echo tops. Although both regions experienced a seasonal peak in DCS frequency in July, their diurnal variations differed significantly. The Plateau exhibited a pronounced unimodal peak between 13:00 and 16:00, which was driven by strong surface heating. In the Basin, a bimodal pattern was observed, with elevated frequencies during 23:00–02:00 and 08:00–11:00. This pattern was likely influenced by local thermodynamic and topographic conditions. The altitude of maximum corrected radar reflectivity (MaxCRF) was predominantly between 4 and 7 km over the Plateau and confined to 2–4 km over the Basin. Over the Plateau, DCS frequency increased significantly with elevation, consistent with the enhancing role of high terrain, whereas no comparable relationship was found in the Basin. Instead, convective activity in the Basin appeared to be modulated primarily by atmospheric instability and moisture availability, highlighting the contrasting environmental controls between the two regions. Full article

(This article belongs to the Section Meteorology)

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