Atmosphere

38 pages, 13932 KB

Open AccessArticle

Monitoring Aerosol Dynamics in the Beijing–Tianjin–Hebei Region: A High-Resolution, All-Day AOD Dataset from 2018 to 2023

by Jinyu Yang, Boqiong Zhang, Yiyao Yang, Sijia Liu, Bo Li, Wenhao Zhang and Xiufeng Yang

Atmosphere 2026, 17(2), 168; https://doi.org/10.3390/atmos17020168 - 4 Feb 2026

Abstract

The Beijing–Tianjin–Hebei (BTH) region is a critical political and economic hub in China, which has long faced challenges related to atmospheric conditions. Traditional aerosol optical depth (AOD) monitoring methods suffer from issues of data discontinuity and gaps, limiting the ability for continuous long-term [...] Read more.

The Beijing–Tianjin–Hebei (BTH) region is a critical political and economic hub in China, which has long faced challenges related to atmospheric conditions. Traditional aerosol optical depth (AOD) monitoring methods suffer from issues of data discontinuity and gaps, limiting the ability for continuous long-term observation of aerosols. Aerosols have significant impacts on climate change and air quality, with AOD serving as a key indicator for characterizing atmospheric particulate concentration. Therefore, this study applied a machine learning model to improve all-day AOD estimation based on ground-level air quality and meteorological data, generating a long-term dataset spanning from 2018 to 2023. The results of the all-day AOD estimation method were evaluated through comparisons with Himawari-8, the Aerosol Robotic Network (AERONET), and the Copernicus Atmosphere Monitoring Service (CAMS). The estimated AOD demonstrated good agreement with AHI data, achieving an annual R² greater than 0.96 and RMSE less than 0.1. Spatially, the estimated AOD also showed strong consistency with AHI, AERONET, and CAMS. Additionally, the annual, seasonal, and hourly distribution characteristics of AOD from 2018 to 2023 were analyzed. Two typical cases of aerosol variation in the BTH region were selected and examined: a dust storm event in 2023 and changes during the Spring Festival in 2021. This method provides continuous data support for air pollution monitoring and control in the BTH region and offers valuable references for pollution prevention efforts. Full article

(This article belongs to the Special Issue Observation and Properties of Atmospheric Aerosol)

24 pages, 17469 KB

Open AccessArticle

Atmospheric Impact of Typhoon Hagibis: A Multi-Layer Investigation of Stratospheric and Ionospheric Responses

by Kousik Nanda, Debrupa Mondal, Sudipta Sasmal, Yasuhide Hobara, Ajeet K. Maurya, Masashi Hayakawa, Stelios M. Potirakis and Abhirup Datta

Atmosphere 2026, 17(2), 167; https://doi.org/10.3390/atmos17020167 - 4 Feb 2026

Abstract

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35

^{\circ}

N and 135–155

^{\circ}

E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that [...] Read more.

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35

^{\circ}

N and 135–155

^{\circ}

E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that involves the troposphere, stratosphere and upper ionosphere to examine the thermodynamic and electromagnetic coupling between these layers due to such extreme weather conditions. Using ERA5 reanalysis, we identify pronounced stratospheric temperature perturbations, elevated atmospheric gravity wave (AGW) potential energy, substantial spatiotemporal variability in the zonal (U) and meridional (V) wind components, relative humidity, and specific rainwater content throughout the cyclone’s evolution. Quantitatively, AGW potential energy increased from background levels of <5 J kg

^{- 1}

to >40 J kg

^{- 1}

near the cyclone core, while tropospheric wind anomalies reached

\pm 30

–40 m s

^{- 1}

, accompanied by relative humidity values exceeding 90% and specific rainwater content up to

1.5 \times 10^{- 3}

kg kg

^{- 1}

, indicative of vigorous moist convection and strong vertical energy transport. The ionospheric response, derived from GPS-based Total Electron Content (TEC) at 10 Japanese IGS stations, reveals vertical TEC (VTEC) perturbations whose amplitudes and temporal evolution vary systematically with GPS-station-to-typhoon-eye distance, including clear enhancements and reductions around the closest-approach day. These signatures indicate a measurable ionospheric response to cyclone-driven atmospheric forcing under geomagnetically quiet conditions, confirming that Hagibis produced vertically coupled disturbances linking stratospheric AGW activity with ionospheric electron density variability. Full article

(This article belongs to the Section Upper Atmosphere)

16 pages, 718 KB

Open AccessArticle

Design and Analysis of an Open-Pit Iron Mine Dust Pollution Evaluation Model Based on the AHP-FCE Method

by Dongmei Tian, Kaishuo Yang, Jian Yao, Weiyu Qu, Xiyao Wu, Jiayun Wang and Jimao Shi

Atmosphere 2026, 17(2), 166; https://doi.org/10.3390/atmos17020166 - 3 Feb 2026

Abstract

Currently, there is a lack of systematic and quantitative analytical tools for dust emission control in open-pit iron mines. To address this research gap, this study constructs a comprehensive evaluation index system by integrating the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive [...] Read more.

Currently, there is a lack of systematic and quantitative analytical tools for dust emission control in open-pit iron mines. To address this research gap, this study constructs a comprehensive evaluation index system by integrating the Analytic Hierarchy Process (AHP) and the fuzzy comprehensive evaluation (FCE) method. The framework includes four first-level indicators, 12 s-level indicators and 30 third-level indicators. The structural design was informed by laws and regulations, the relevant literature and the principle of dust hierarchical control to ensure the theoretical and empirical basis for the selection of indicators. The evaluation process was based on on-site monitoring data and production ledgers from the open-pit iron mine of the Shuichang Mine, as well as the results of multiple rounds of consultation by the Delphi method group composed of 30 experts in related industries. The results show that the comprehensive score of the mine is 87.14 points, and the overall prevention and control is effective. But the performance of each dimension is unbalanced: fundamental data on production processes scored highest, while individual exposure and protection measures were relatively weak, indicating that the personnel protection link needs to be strengthened. Sensitivity analysis further verified the structural stability of the index system and identified the ventilation and dust removal system as a key driving factor. This framework can provide quantitative decision support for mine managers, enhancing the precision and overall effectiveness of dust control through the accurate identification of weaknesses and optimized resource allocation. Full article

(This article belongs to the Section Air Pollution Control)

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21 pages, 21597 KB

Open AccessArticle

Topographic Influence on Cold-Air Pool Formation: A Case Study of the Eiras Valley (Coimbra, Portugal)

by António Rochette Cordeiro, André Lucas and José Miguel Lameiras

Atmosphere 2026, 17(2), 165; https://doi.org/10.3390/atmos17020165 - 3 Feb 2026

Abstract

Topography plays a crucial role in shaping local urban microclimates and can drive the formation of cold-air pools in valley bottoms. This study examines the Eiras Valley (Coimbra, Portugal), a rapidly growing peri-urban area, to identify the conditions under which cold-air pools form [...] Read more.

Topography plays a crucial role in shaping local urban microclimates and can drive the formation of cold-air pools in valley bottoms. This study examines the Eiras Valley (Coimbra, Portugal), a rapidly growing peri-urban area, to identify the conditions under which cold-air pools form and to characterize their spatial and vertical dynamics. Field measurements were carried out using Tinytag Plus 2 data loggers at the surface (≈1.5 m above ground) and mounted on an unmanned aerial vehicle (UAV) for vertical profiles, complemented by high-resolution thermal mapping through Empirical Bayesian Kriging. The results show that a nocturnal cold-air pool develops within the valley under clear, anticyclonic winter conditions, persisting into the early morning hours and dissipating after sunrise due to solar heating. In contrast, under overcast or summer conditions, no cold-air pooling was observed. The temperature inversion capping the cold-air pool was found at approximately 275 m altitude, inhibiting vertical mixing and trapping pollutants near the ground. These findings underscore the importance of topoclimatology in urban and regional planning, with implications for thermal comfort, air quality, and public health. The study contributes to urban climate research by highlighting how local topography and seasonal atmospheric stability govern cold-air pool formation in valley environments, supporting the development of mitigation strategies aligned with urban sustainability goals. Full article

(This article belongs to the Section Climatology)

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19 pages, 7588 KB

Open AccessArticle

Characterising and Differentiating Non-Exhaust Airborne Nanoparticle Sources in Urban Traffic and Background Environments

by Yingyue Wei, George Biskos and Prashant Kumar

Atmosphere 2026, 17(2), 164; https://doi.org/10.3390/atmos17020164 - 2 Feb 2026

Abstract

The contribution of non-exhaust emissions (NEEs) to particle number concentration (PNC) remains insufficiently quantified, particularly across different urban environments. In this study, we address this gap by quantifying the contribution of NEEs to airborne nanoparticles in urban areas. Using positive matrix factorisation (PMF), [...] Read more.

The contribution of non-exhaust emissions (NEEs) to particle number concentration (PNC) remains insufficiently quantified, particularly across different urban environments. In this study, we address this gap by quantifying the contribution of NEEs to airborne nanoparticles in urban areas. Using positive matrix factorisation (PMF), conditional probability function analysis, Pearson correlation, and source identification, we identified five source factors contributing to PNC at two sites in London: a traffic site and a background site. Five source factors were resolved at both sites: Aitken-mode traffic exhaust particles, nucleation-mode exhaust emission, secondary aerosol, non-exhaust emission, and regional background accumulation. Interestingly, the contribution of NEEs differed between the two sites. At the traffic site, NEEs contributed 14.9%, while at the background site, their contribution was higher at 28.5%, likely due to the favourable summer dispersion conditions. However, the contribution of nucleation-mode exhaust emission also showed significant differences: 26.6% at the traffic site and only 9.9% at the background site. Based on these findings, we propose that air quality policies should integrate NEEs into regulations, improve road maintenance, and use PNC-based along with metal tracers to identify and control PNC. This study offers valuable insights for developing strategies to manage urban nanoparticle pollution. Full article

(This article belongs to the Section Air Quality)

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20 pages, 3415 KB

Open AccessArticle

Enhancing Observation Point Analysis for Atmospheric State Estimation Using Self-Supervised Graph Neural Networks

by Guangyu Xu, Feng Bao, Siyu Lu, Chunlai Wu, Yuxin Liu and Wenfeng Zheng

Atmosphere 2026, 17(2), 163; https://doi.org/10.3390/atmos17020163 - 1 Feb 2026

Abstract

Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, [...] Read more.

Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, these methods have certain limitations when dealing with large-scale meteorological data and complex meteorological phenomena. In order to solve the above problems, this study first integrates and processes data from multiple datasets including ground, upper-air, satellite, and atmospheric state, representing these data as graph structures. Secondly, a graph neural network-based network model is constructed, which is pre-trained using self-supervised methods and fine-tuned for specific tasks. Finally, gradient-based interpretability analysis is used to evaluate the importance of observed nodes. The experimental results show that both the atmospheric state estimation model and the interpretable analysis method proposed in this paper are superior to some existing representative models and methods. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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28 pages, 9229 KB

Open AccessArticle

A Hybrid Offline–Online Kalman–RBF Framework for Accurate Relative Humidity Forecasting

by Athanasios Donas, George Galanis, Ioannis Pytharoulis and Ioannis Th. Famelis

Atmosphere 2026, 17(2), 162; https://doi.org/10.3390/atmos17020162 - 31 Jan 2026

Abstract

Accurate humidity forecasts are crucial for environmental and operational applications, yet Numerical Weather Prediction systems frequently exhibit systematic and random errors. To address this problem, this study introduces a modified hybrid post-processing approach that extends a previously developed methodology, enabling a direct comparison [...] Read more.

Accurate humidity forecasts are crucial for environmental and operational applications, yet Numerical Weather Prediction systems frequently exhibit systematic and random errors. To address this problem, this study introduces a modified hybrid post-processing approach that extends a previously developed methodology, enabling a direct comparison of computational efficiency and predictive capacity. The proposed framework integrates a quadratic Kalman Filter with a Radial Basis Function Neural Network trained via the Orthogonal Least Squares algorithm and updated online through Recursive Least Squares. This modified method was evaluated via a time-window process, using forecasts from the Weather Research and Forecasting model and recorded observations from stations in northern Greece. The results show substantial improvements in forecast accuracy, as the Bias was reduced by over 85%, and the MAE and RMSE decreased by approximately 65% and 58%, respectively, compared with the baseline model. Furthermore, the proposed framework also demonstrates enhanced computational efficiency, reducing processing time by more than 95% relative to the initial methodology. Full article

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

27 pages, 110853 KB

Open AccessArticle

Resilience and Threshold-like Behavior of Moroccan Tetraclinis articulata (Vahl) Mast. Ecosystems Under Four Decades of Climate Warming

by Mourad Touaf, Fatima Zahra Echogdali, Mohamed Abioui, Abdelhafed El Asbahani, Laila Boukhalef, Aicha Nait Douch, Fatima Ain-Lhout and Said Boutaleb

Atmosphere 2026, 17(2), 161; https://doi.org/10.3390/atmos17020161 - 31 Jan 2026

Abstract

Climate warming and land degradation are reshaping Mediterranean and semi-arid ecosystems, yet their combined effects remain poorly quantified in North Africa. Using four Landsat reference epochs spanning 1984–2024, and four spectral/thermal indices (NDVI, EVI, NDMI, LST), we assessed vegetation dynamics and eco-climatic resilience [...] Read more.

Climate warming and land degradation are reshaping Mediterranean and semi-arid ecosystems, yet their combined effects remain poorly quantified in North Africa. Using four Landsat reference epochs spanning 1984–2024, and four spectral/thermal indices (NDVI, EVI, NDMI, LST), we assessed vegetation dynamics and eco-climatic resilience of Tetraclinis articulata ecosystems in Morocco. Four study sites (Stehat, Merchouch, Tamanar, and Amskroud) distributed along a latitudinal gradient from the northern to southern limits of the species’ Moroccan range were chosen and analyzed. Results reveal a generalized decline in vegetation cover, strongly coupled with increasing land surface temperatures, with threshold-like patterns emerging above 74–75 °C that lead to a rapid reduction in NDVI. The northern site (Stehat) exhibited partial recovery, likely supported by local schist aquifers, whereas the arid southern sites (Tamanar and Amskroud) experienced near-total biomass loss and reduced climate buffering. Moisture indices limited hydrological mediation and suggest that shallow soil water availability constrains T. articulata functioning, amplifying vulnerability under recurrent warming. These findings demonstrate how local edaphic and hydrological conditions modulate the impacts of global change and provide early warning indicators of heightened vulnerability and potential threshold-like behavior in drylands. The study emphasizes the urgent need for targeted management strategies to sustain ecosystem resilience under accelerating climate stress. Full article

(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)

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26 pages, 3190 KB

Open AccessArticle

Modeling Particulate Matter Concentrations: A Comparative Analysis of Statistical and Machine Learning Models

by Ali Sercan Kesten and Mehran Panhwar

Atmosphere 2026, 17(2), 160; https://doi.org/10.3390/atmos17020160 - 31 Jan 2026

Abstract

This study compares Ordinary Least Squares (OLS) regression and Random Forest models to analyze particulate matter (PM₁, PM_2.5, and PM₁₀) concentrations based on meteorological and traffic data collected on major arterials in Karachi, Pakistan. OLS regression highlights [...] Read more.

This study compares Ordinary Least Squares (OLS) regression and Random Forest models to analyze particulate matter (PM₁, PM_2.5, and PM₁₀) concentrations based on meteorological and traffic data collected on major arterials in Karachi, Pakistan. OLS regression highlights temperature and humidity as significant contributors to PM levels, while wind speed shows an inverse relationship, especially with PM₁. Random Forest regression demonstrates superior performance with higher R² values and a lower RMSE, effectively capturing complex, non-linear relationships among variables. Wind speed thresholds for PM dispersion are identified, providing critical benchmarks for air quality management. This comparative analysis underscores the effectiveness of machine learning methods for accurate environmental modeling. Full article

(This article belongs to the Section Air Quality)

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31 pages, 13397 KB

Open AccessArticle

Seasonal and Diurnal Variations in Greenhouse Gas Methane (CH₄) in a Rural Area of Rome (Italy)

by Antonietta Ianniello, Giulio Esposito, Cristiana Bassani, Francesca Vichi, Valerio Paolini, Walter Stefanoni, Paolo Sconocchia, Luca Tofful, Mauro Montagnoli, Andrea Imperiali, Alma Iannilli, Valentina Terenzi, Patrizio Tratzi and Emanuele Pallozzi

Atmosphere 2026, 17(2), 159; https://doi.org/10.3390/atmos17020159 - 31 Jan 2026

Abstract

First continuous measurements of atmospheric CH₄ were carried out for one year (June 2023–May 2024) at Liberti Observatory of CNR-IIA, in a semi-rural site near Rome. Seasonal and diurnal variations were analyzed. CH₄ monthly mean concentrations showed maximum and minimum values [...] Read more.

First continuous measurements of atmospheric CH₄ were carried out for one year (June 2023–May 2024) at Liberti Observatory of CNR-IIA, in a semi-rural site near Rome. Seasonal and diurnal variations were analyzed. CH₄ monthly mean concentrations showed maximum and minimum values in winter and summer, respectively, which agree with the other European trends. Minimum CH₄ values during summer could likely be due to a combination of favorable atmospheric mixing properties and increased atmospheric CH₄ oxidation. The correlation analysis showed that temperature, global radiation, and wind speed revealed significant negative correlations with this greenhouse gas, indicating the influence of local sources. However, poor correlations during different seasonal periods also suggested the role of air mass transport sources. The CH₄ concentrations exhibited clear diurnal cycles with daytime low and night-time high values, mainly driven by atmospheric stability conditions and photochemistry. A cluster analysis of air mass trajectories showed that CH₄ concentrations were influenced all year by anthropogenic emissions. Elevated concentrations arrived from NE Europe, except in winter when the influence of NW European and local contributions became more significant. Furthermore, level-3 XCH₄ data from the satellite TROPOMI showed a methane columnar concentration increase from 2018 to 2024 in agreement with the global annual increase from the NOAA network during the same period. Full article

(This article belongs to the Section Air Quality)

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17 pages, 3523 KB

Open AccessArticle

Characteristics and Driving Mechanisms of Net Ecosystem Productivity in a Subtropical Moso Bamboo Forest Based on XGBoost

by Kun Zhao, Cheng Li, Huifang Liu, Xiaoyi Hua, Boxuan Duan, Manyi Li, Wenjing Chen and Chuan Jin

Atmosphere 2026, 17(2), 158; https://doi.org/10.3390/atmos17020158 - 31 Jan 2026

Abstract

As a critical agroforestry crop in Southern China, Moso bamboo, maintains regional timber security and bamboo shoot production, with its net ecosystem productivity (NEP) directly determining dry matter accumulation and economic yield. This study integrates 2024 continuous flux observations with XGBoost and SHAP [...] Read more.

As a critical agroforestry crop in Southern China, Moso bamboo, maintains regional timber security and bamboo shoot production, with its net ecosystem productivity (NEP) directly determining dry matter accumulation and economic yield. This study integrates 2024 continuous flux observations with XGBoost and SHAP explanations to characterize the subtropical bamboo forest carbon budget and its nonlinear driving mechanisms. The results show a weak carbon sink in 2024 with an annual cumulative NEP of 120 g C m⁻², as high respiration of 860 g C m⁻² limited organic matter conversion by consuming nearly 88% of the 980 g C m⁻² total primary production. The peak production period during May and June was offset by growth stagnation in August, caused by extreme heat and drought. Net radiation served as the primary driver, with a positive contribution threshold of 75.28 W m⁻², whereas precipitation exceeding 1.85 mm or air temperatures over 17.85 °C hindered carbon accumulation through radiation attenuation and metabolic heat loss. Strong radiation–precipitation interactions confirm that water’s impacts on yield are deeply contingent upon radiation backgrounds. These nonlinear regulatory pathways provide a scientific foundation for stabilizing bamboo forest productivity through synergistic water-radiation management and structural optimization during extreme climate events. Full article

(This article belongs to the Special Issue Agriculture, Ecosystems and Environment: Monitoring, Modeling and Mitigation Under Climate Change)

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16 pages, 1235 KB

Open AccessArticle

How Frequent Is an Extraordinary Episode of Precipitation? Spatially Integrated Frequency in the Júcar–Turia System (Spain)

by Pol Pérez-De-Gregorio and Robert Monjo

Atmosphere 2026, 17(2), 157; https://doi.org/10.3390/atmos17020157 - 31 Jan 2026

Abstract

An extraordinary episode is a torrential rainfall event that produces significant societal impacts, which poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating [...] Read more.

An extraordinary episode is a torrential rainfall event that produces significant societal impacts, which poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating return periods of extraordinary precipitation in the Júcar and Turia basins, moving beyond traditional point-based or micro-catchment analyses. Our methodology consists of progressive spatial aggregation of time series within a basin to better estimate return periods of exceeding specific catastrophic rainfall thresholds. This technique allows us to compare 10 min rainfall data of a reference station (e.g., Turís, València, 29 October 2024 catastrophe) with long-term annual maxima from 98 stations. Temporal structure is characterized using the fractal–intermittency n-index, while tail behavior is modeled using several extreme-value distributions (Gumbel, GEV, Weibull, Gamma, and Pareto) and guided by empirical errors. Results show that

n \approx 0.3

–0.4 is consistent for extreme rainfall, while return periods systematically decrease as stations are added, stabilizing with about 15–20 stations, once the relevant spatial heterogeneity is sampled. Specifically, the probability of exceeding extraordinary thresholds is between 3 and 10 times higher for the areal than the point approach, so recurrence of a catastrophe would be once a few decades rather than centuries. Overall, the results demonstrate that spatially integrated return-period estimation is operational, physically consistent, and better suited for basin-scale risk assessment than purely point-based approaches, providing a relevant baseline for interpreting recent catastrophic events in the context of ongoing climatic warming in the Mediterranean region. Full article

(This article belongs to the Special Issue Observational and Model-Based Extreme Precipitation Analysis)

27 pages, 8497 KB

Open AccessArticle

Evaluation of Global Climate and Storm-Resolving Model Representations of Mixed-Phase Clouds and Their Hemispheric Contrasts

by Olimpia Bruno, Jonah K. Shaw, Trude Storelvmo and Corinna Hoose

Atmosphere 2026, 17(2), 156; https://doi.org/10.3390/atmos17020156 - 31 Jan 2026

Abstract

Mixed-phase clouds, in which liquid droplets and ice crystals coexist at temperatures between

- 38^{\circ}

C and

0^{\circ}

C, play a critical role in Earth’s radiation budget. Here, we assess the ability of climate and storm-resolving models to represent mixed-phase cloud [...] Read more.

Mixed-phase clouds, in which liquid droplets and ice crystals coexist at temperatures between

- 38^{\circ}

C and

0^{\circ}

C, play a critical role in Earth’s radiation budget. Here, we assess the ability of climate and storm-resolving models to represent mixed-phase cloud properties and their hemispheric contrasts as inferred from satellite observations. We compare observations from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) with one global climate model, the Community Atmosphere Model version 6, Oslo configuration (CAM6-Oslo), and three storm-resolving models: the ICOsahedral Non-hydrostatic model (ICON), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), and the Goddard Earth Observing System model (GEOS). Our results show that all models reproduce the geographic distribution of mixed-phase clouds but differ significantly in detail. CAM6-Oslo yields the closest agreement in hemispheric contrasts of supercooled liquid fraction and its relationship with the liquid effective radius. Our results highlight the role of aerosol–cloud interactions and microphysics schemes in determining model performance and demonstrate that storm-resolving models still do not overcome the challenge of representing mixed-phase clouds at global scales. Full article

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

15 pages, 863 KB

Open AccessArticle

Exposure Toxicity of Dust Storm Particles Based on Plasmid Scission Assay: An Example from Beijing

by Xinyu Xue, Shushen Yang, Susu Fan, Yaxin Cao, Wenhua Wang and Longyi Shao

Atmosphere 2026, 17(2), 155; https://doi.org/10.3390/atmos17020155 - 30 Jan 2026

Abstract

To investigate the health risks of particulate matter during spring dust storms in Beijing, this study selected atmospheric particulate samples collected during a typical dust storm event in March 2021. The DNA damage rates induced by PM_2.5 and PM₁₀ were measured [...] Read more.

To investigate the health risks of particulate matter during spring dust storms in Beijing, this study selected atmospheric particulate samples collected during a typical dust storm event in March 2021. The DNA damage rates induced by PM_2.5 and PM₁₀ were measured using the Plasmid Scission Assay (PSA) and were used as an indicator of their oxidative potential. Water-soluble heavy metal elements (WSHM) in the samples were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results indicate that due to the influence of the dust storm, the monthly average PM_2.5 mass concentration in March 2021 reached as high as 83 μg/m³, which could potentially raise the difficulty of air pollution control. It was found that during the dust storm event, PM_2.5 induced a higher DNA damage rate (mean 42.35% at an experimental dose of 200 μg/mL in the PSA) than PM₁₀ (mean 40.46% under the same experimental dosage). The DNA damage rates of dust storm particles showed a positive correlation trend (r = 0.60) with total WSHM concentrations. Exposure toxicity, calculated by multiplying the DNA damage rates under certain experimental PM doses by the PM mass concentrations, showed that the exposure risk of PM_2.5 during dust storms even exceeded that of PM_2.5 during haze events. This study reveals the potential toxicity and health risks associated with PM during dust storms, which calls for increased attention. Full article

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

20 pages, 28396 KB

Open AccessArticle

Evaluating the Effect of Emission Schemes on Dust Simulation in East Asia During Spring 2023

by Shengkai Wang, Xiao-Yi Yang and Chenghan Luo

Atmosphere 2026, 17(2), 154; https://doi.org/10.3390/atmos17020154 - 30 Jan 2026

Abstract

In the spring of 2023, dust outbreaks were unusually active in East Asia, posing substantial risks to air quality. Accurately simulating dust storms is essential for improving regional dust prediction and impact assessment. In this study, we evaluated dust simulations over East Asia [...] Read more.

In the spring of 2023, dust outbreaks were unusually active in East Asia, posing substantial risks to air quality. Accurately simulating dust storms is essential for improving regional dust prediction and impact assessment. In this study, we evaluated dust simulations over East Asia using different dust emission schemes in the FLEXDUST/FLEXPART model and quantified the regional dust budget. Overall, the GOCART (Goddard Chemistry Aerosol Radiation and Transport) scheme shows the highest skill among the evaluated schemes. Under mild dust conditions (300–1000 μg m⁻³), it yielded a mean PM₁₀ bias of −89.2 μg m⁻³, markedly smaller than those from other schemes/models (−450.2 to −265.6 μg m⁻³). It also better reproduced the dominant spatial patterns of dust optical depth over Xinjiang and Inner Mongolia, with lower errors and higher correlations. Budget diagnostics show that the Taklamakan and Gobi Deserts are net dust exporters (7.4 and 11.6 Tg, respectively), whereas East Asia exhibits a negative net external flux (−12.1 Tg). The comparable magnitudes of these terms underscore the role of inter-regional transport in shaping the East Asian dust budget. These results offer insights for improving dust emission schemes in the FLEXDUST/FLEXPART model, thereby enhancing dust simulations over East Asia. Full article

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

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28 pages, 11269 KB

Open AccessArticle

Relationship Between Deep Convection, Water Vapor, Lightning, and Precipitation over Northern Coastal Brazil

by Diana Islas-Flores, David K. Adams, Ludmila Monteiro da Silva Dutra, Galdino Viana Mota and Rui M. S. Fernandes

Atmosphere 2026, 17(2), 153; https://doi.org/10.3390/atmos17020153 - 30 Jan 2026

Abstract

A key component necessary to improve the performance of climate and weather forecasting models is understanding the physical mechanisms controlling tropical deep convection. In this study, the thermodynamic variables linked to deep convection within this equatorial sea-breeze convective regime are analyzed. A range [...] Read more.

A key component necessary to improve the performance of climate and weather forecasting models is understanding the physical mechanisms controlling tropical deep convection. In this study, the thermodynamic variables linked to deep convection within this equatorial sea-breeze convective regime are analyzed. A range of data sets are employed: GNSS-based PWV and surface precipitation data, lightning and daily radiosonde observations, and GOES-13/16 and GPM satellite products. Our results indicate that the convective indices of CAPE and CIN, often used as predictors of deep convection, do not clearly distinguish deep-convective and non-convective days. In contrast, the variables representative of the atmospheric water vapor content, PWV and vertical water vapor distribution as well as an entrainment-based buoyancy measure, are better markers of potential deep convection. For this region, however, the water vapor/deep convection relationship with precipitation does not appear as strong as over tropical oceans and tropical continental regions. Finally, our results show that there is no strong link between daily average precipitation intensity and daily lightning count. However, deep-convective days without lightning had higher water vapor at the beginning of the day, as compared to days with lightning, which resulted in convective showers earlier in the day. Full article

(This article belongs to the Section Meteorology)

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41 pages, 1977 KB

Open AccessReview

A Review of What Can Be Learnt from Tweeks and Related Topics

by Michael J. Rycroft

Atmosphere 2026, 17(2), 152; https://doi.org/10.3390/atmos17020152 - 30 Jan 2026

Abstract

Tweeks are ELF/VLF radio signals originating from lightning discharges that exhibit dispersion due to their propagation in the Earth-ionosphere waveguide. Examples of the waveforms of tweeks and their dynamic frequency-time spectra are presented and interpreted. Tweeks observed in the daytime and night-time are [...] Read more.

Tweeks are ELF/VLF radio signals originating from lightning discharges that exhibit dispersion due to their propagation in the Earth-ionosphere waveguide. Examples of the waveforms of tweeks and their dynamic frequency-time spectra are presented and interpreted. Tweeks observed in the daytime and night-time are compared and contrasted. Tweeks observed during a solar eclipse are also discussed, as are those due to volcanic lightning and those claimed to be recorded some hours or days before a strong earthquake. The variations of tweek occurrence with season and geomagnetic activity, and with variations of solar radiation over the 11-year solar cycle, are reviewed. Wherever possible, geophysical interpretations are discussed. Theoretical models of tweek waveforms and spectra are considered; they vary according to the lightning current model used, the distance from the source (≥1 Mm), the vertical profile of ionospheric D-region ionisation and the specific mode theory used. The simplest interpretation shows that the first-order tweek cut- off frequency ~1.8 kHz is explained as reflection by the ionosphere at a height of ~83 km where the electron density is ~27 × 10⁶ m⁻³. More complex interpretations are also reviewed and compared with electron density observations made by rockets and with profiles given by lower ionospheric models such as the International Reference Ionosphere or the Faraday International Reference Ionosphere. Full article

(This article belongs to the Special Issue Understanding Space Physics and Atmospheric Electricity with VLF/ELF Signals)

13 pages, 1094 KB

Open AccessArticle

Neural Network Algorithms for Estimating Snow Depth and Scattering Mean Free Path from ICESat-2 Measurements of Multiple Scattering Inside Snow

by Yinuo Zhou, Kyle Hu and Xiaomei Lu

Atmosphere 2026, 17(2), 151; https://doi.org/10.3390/atmos17020151 - 30 Jan 2026

Abstract

Lidar measurements of green laser light traveling inside snow can be modeled using Monte Carlo simulations. These simulations generate databases that link snow properties (such as snow depth and scattering mean free path) with lidar backscatter vertical profile measurements. In this study, these [...] Read more.

Lidar measurements of green laser light traveling inside snow can be modeled using Monte Carlo simulations. These simulations generate databases that link snow properties (such as snow depth and scattering mean free path) with lidar backscatter vertical profile measurements. In this study, these simulated datasets are used to train neural networks to explore the potential for estimating snow properties from ICESat-2 lidar measurements. The networks use simulated snow backscatter profiles as inputs and corresponding snow properties as outputs. Our results indicate that the near-surface portion of the snow backscatter signal contains information relevant to snow depth and scattering mean free path, demonstrating the feasibility of using machine learning frameworks for efficient analysis of spaceborne lidar observations. These findings are presented as a proof-of-concept, with comprehensive external validation and uncertainty quantification identified as future work. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing (2nd Edition))

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20 pages, 20237 KB

Open AccessArticle

Ionospheric Response to the Geomagnetic Storm of 12–14 November 2025, Based on Multi-Parameter Analysis of Data from the LAERT Topside Sounder

by Sergey Pulinets, Nadezhda Kotonaeva, Victor Depuev and Konstantin Tsybulya

Atmosphere 2026, 17(2), 150; https://doi.org/10.3390/atmos17020150 - 30 Jan 2026

Abstract

As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between 12 and 14 November 2025 stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to [...] Read more.

As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between 12 and 14 November 2025 stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to conduct a comprehensive multi-parameter analysis of this event. Such an analysis relied upon data from the four LAERT topside sounders mounted aboard the recently launched Ionosfera-M satellites. Global ionospheric dynamics were thoroughlyexamined during the storm period, particularly focusing on the polar and auroral zones, along with the equatorial anomaly region. Notable features included sharp electron density gradients, widespread F-layer disturbances, and the formation of giant plasma bubbles. These elements collectively contributed to the dynamic picture of the ionospheric storm captured through multi-parameter measurements by the LAERT sounders. Full article

(This article belongs to the Special Issue Advances in Observation and Simulation Studies of Ionosphere)

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