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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 16.9 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
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 Atmosphere.
Companion journals for Atmosphere include: Meteorology and Aerobiology.

Impact Factor: 2.3 (2024); 5-Year Impact Factor: 2.5 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2073-4433

Latest Articles

46 pages, 88566 KiB

Open AccessArticle

Spatiotemporal Patterns of 45-Day Precipitation in Rio Grande Do Sul State, Brazil: Implications for Adaptation to Climate Variation

by Luana Centeno Cecconello, Angela Maria de Arruda, André Becker Nunes and Tirzah Moreira Siqueira

Atmosphere 2025, 16(8), 963; https://doi.org/10.3390/atmos16080963 (registering DOI) - 12 Aug 2025

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for [...] Read more.

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for understanding drivers of extreme events like the catastrophic floods of 2024. A total of 138 precipitation fields were generated from 670 spatial points. Spatial analysis revealed median precipitation values ranging from 130 to 329 mm/45 days, with the northeast showing the highest accumulations and the southwest showing the driest conditions. Temporal variability was marked by abrupt anomalies, with median peaks up to 462 mm and minima of 33 mm. Significant temporal autocorrelation (lag-1, 45 days) was identified in the central and northern regions, while lag-2 (90 days) showed inverse patterns in the south (correlation coefficient ≈ −0.45). Principal component analysis (KMO = 0.909; Bartlett’s χ² = 187,990.945; p < 0.05) identified seven dominant modes, with PC1 explaining 26% of total variance and highlighting extremely wet anomalies (e.g., SPI > 2.0). Correlation with the Oceanic Niño Index revealed heterogeneous responses to ENSO phases, with strong El Niño episodes (2009, 2015–2016) associated with precipitation peaks up to 966 mm/45 days. These results underscore the importance of subseasonal scales for understanding climate anomalies and support the development of regional forecast strategies and water management policies under increasing climate variability. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

17 pages, 7327 KiB

Open AccessArticle

Coastal Wind in East Iceland Using Sentinel-1 and Model Data Reanalysis

by Eduard Khachatrian, Yngve Birkelund and Andrea Marinoni

Atmosphere 2025, 16(8), 962; https://doi.org/10.3390/atmos16080962 (registering DOI) - 12 Aug 2025

This study evaluates three wind data sources in East Iceland’s coastal environment: the high-resolution SAR-based Sentinel-1, the regional reanalysis CARRA, and the global reanalysis ERA5. We focus on assessing the advantages and limitations of each dataset, especially considering their differences in spatial and [...] Read more.

This study evaluates three wind data sources in East Iceland’s coastal environment: the high-resolution SAR-based Sentinel-1, the regional reanalysis CARRA, and the global reanalysis ERA5. We focus on assessing the advantages and limitations of each dataset, especially considering their differences in spatial and temporal resolutions. While ERA5 aligns well with CARRA and Sentinel-1 offshore, it tends to underestimate wind speeds and misrepresent wind directions near complex coastlines and fjords, with RMSD values reaching up to 3.98 m/s in these areas. CARRA’s higher resolution allows it to better capture coastal wind dynamics and shows strong agreement with Sentinel-1. Sentinel-1 excels in revealing detailed local wind features, such as katabatic winds in fjords, highlighting the value of satellite observations in complex terrain. By combining these complementary datasets, this study enhances understanding of coastal wind variability and supports improved hazard assessment in Iceland’s challenging coastal environments. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

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12 pages, 2439 KiB

Open AccessArticle

Research on Temperature Prediction of Passion Fruit Planting Bases in Southwest Fujian Province

by Shiyun Mou, Shujie Yuan, Yuchen Shi, Lin Han, Kai Yang and Hongyi Li

Atmosphere 2025, 16(8), 961; https://doi.org/10.3390/atmos16080961 (registering DOI) - 12 Aug 2025

This article utilized hourly temperature, humidity, pressure and wind speed data from passion fruit meteorological observation stations in three southwestern cities of Fujian Province (Longyan, Sanming, Zhangzhou) from 2020 to 2022, as well as national ground conventional meteorological observation stations. BP neural network [...] Read more.

This article utilized hourly temperature, humidity, pressure and wind speed data from passion fruit meteorological observation stations in three southwestern cities of Fujian Province (Longyan, Sanming, Zhangzhou) from 2020 to 2022, as well as national ground conventional meteorological observation stations. BP neural network and stepwise regression method were applied to construct temperature prediction models for the passion fruit planting bases. The results showed that: (1) The simulation effect of the passion fruit station temperature prediction model based on BP neural network (referred to as BP model) was better than that of the model based on stepwise regression method (referred to as regression model). The average absolute error (MSE) of BP model (2.75–3.42 °C) was smaller than that of regression model (3.32–3.94 °C). (2) For the simulation results of daily temperature changes in the passion fruit station, the difference in hourly average temperature between the BP model predictions (regression model predictions) and observed temperatures at passion fruit station was −4.1–4.4 °C (−6.0–10.2 °C). The BP model showed a daily temperature trend that was closer to the measured values; (3) For the simulation results of high and low temperatures in the passion fruit station, the BP neural network model (regression model) showed a prediction error range of −5.6 °C to 5.2 °C compared to observed temperatures, while the stepwise regression model’s error range was −4.1 °C to 8.8 °C. The BP model’s predicted temperature trend was closer to the measured values. (4) Both models have significant shortcomings in the prediction of high-temperature individual cases and hourly averages, with relatively large errors (generally exceeding 3 °C), especially during the period from 10 to 16 o’clock. The future version needs to be optimized. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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14 pages, 1912 KiB

Open AccessArticle

Seasonal Variations of Carbonaceous Aerosols of PM_2.5 at a Coastal City in Northern China: A Case Study of Qinhuangdao

by Xian Li, Mengyang Wang, Jiajia Shao, Qiong Wu, Yutao Gao, Xiuyan Zhou and Wenhua Wang

Atmosphere 2025, 16(8), 960; https://doi.org/10.3390/atmos16080960 (registering DOI) - 12 Aug 2025

Carbonaceous aerosols exert significant impacts on human health and climate systems. This study investigates the seasonal variations of carbonaceous components in fine particulate matter (PM_2.5) in Qinhuangdao, a coastal city in northern China, throughout 2023. The mass concentrations of organic carbon [...] Read more.

Carbonaceous aerosols exert significant impacts on human health and climate systems. This study investigates the seasonal variations of carbonaceous components in fine particulate matter (PM_2.5) in Qinhuangdao, a coastal city in northern China, throughout 2023. The mass concentrations of organic carbon (OC) and elemental carbon (EC) averaged 9.44 ± 4.57 μg m⁻³ and 0.84 ± 0.33 μg m⁻³, contributing 26.49 ± 8.74% and 2.81 ± 1.56% to total PM_2.5, respectively. OC exhibited a distinct seasonal trend: winter (12.02 μg m⁻³) > spring (11.96 μg m⁻³) > autumn (8.15 μg m⁻³) > summer (5.71 μg m⁻³), whereas EC followed winter (1.31 μg m⁻³) > autumn (0.73 μg m⁻³) > spring (0.70 μg m⁻³) > summer (0.63 μg m⁻³). Both OC and EC levels were elevated at night compared to daytime. Secondary organic carbon (SOC), estimated via the EC-tractor method, constituted 37.94 ± 14.26% of total OC. A positive correlation between SOC/OC ratios and PM_2.5 concentrations suggests that SOC formation critically influences haze events. In autumn and winter, SOC formation was higher at night, likely driven by aqueous-phase reactions, whereas in summer SOC formation was more pronounced during the day, likely due to enhanced photochemical reactions. Source apportionment analysis revealed that gasoline and diesel vehicles were major contributors to carbonaceous aerosols, accounting for 27.35–29.06% and 14.97–31.83%, respectively. Coal combustion contributed less (10.51–21.55%), potentially due to strict regulations prohibiting raw coal use for domestic heating in surrounding regions. Additionally, fugitive dust was found to have a high contribution to carbonaceous aerosols during spring and summer. Full article

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

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14 pages, 2696 KiB

Open AccessArticle

Chemistry of Precipitation Acidity at Irkutsk, Russia

by Hiroshi Hara and Izumi Noguchi

Atmosphere 2025, 16(8), 959; https://doi.org/10.3390/atmos16080959 - 12 Aug 2025

Precipitation pH at a Russin EANET site, Irkutsk, was discussed and explained from the viewpoint of acid–base chemical equilibrium theory. The datasets employed were the wet-only daily basis observations at a subarctic climate urban site from 1999 to 2020. A distinct seasonal variation [...] Read more.

Precipitation pH at a Russin EANET site, Irkutsk, was discussed and explained from the viewpoint of acid–base chemical equilibrium theory. The datasets employed were the wet-only daily basis observations at a subarctic climate urban site from 1999 to 2020. A distinct seasonal variation in pH was discovered for the monthly means over the entire period, from pH5.24 in July to pH6.66 in February, bracketing pH5.6, a criterion for acid rain. Monthly concentration variations in major ions, nss-SO₄²⁻, NO₃⁻, NH₄⁺, and nss-Ca²⁺, divulged that nss-Ca²⁺ and nss-SO₄²⁻ were predominant species and that nss-Ca²⁺ dominated nss-SO₄²⁻ during winter months and vice versa in summer. Individual pHs were explored with the concentration difference between two ions, ([nss-SO₄²⁻] − [nss-Ca²⁺]), defined as D₂, as well as two other differences in selected ion groups, D₄ = ([nss-SO₄²⁻] + [NO₃⁻]) − ([NH₄⁺] + [nss-Ca²⁺]) and D₆ = ([nss-SO₄²⁻] + [NO₃⁻]) − ([NH₄⁺] + [nss-Ca²⁺] + [nss-Mg²⁺] + [nss-K⁺]), to show some clear relationships with pH. The observed results were compared with theoretical calculations, D_n = [H⁺] − HP_CO2K_a1/[H⁺], where n is 2, 4, or 6. The distribution of ΔpH (=pH_calc − pH_obs) for D₂ demonstrated the most reasonable pattern with a median of zero among the three D_ns. These quantitative results conclude that the Irkutsk pH was controlled dominantly by sulfuric acid and calcium carbonate. Full article

(This article belongs to the Section Air Quality)

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32 pages, 9675 KiB

Open AccessArticle

A Spatiotemporal Multimodal Framework for Air Pollution Prediction Based on Bayesian Optimization—Evidence from Sichuan, China

by Fengfan Zhang, Jiabei Hu and Ming Zeng

Atmosphere 2025, 16(8), 958; https://doi.org/10.3390/atmos16080958 - 11 Aug 2025

In regions characterized by complex terrain and diverse pollution sources, high-precision air pollution prediction remains challenging due to nonlinear spatiotemporal coupling and the difficulty of modeling local pollutant agglomeration. To address these issues, this study proposes a CNN–LSTM–Transformer multimodal prediction framework integrated with [...] Read more.

In regions characterized by complex terrain and diverse pollution sources, high-precision air pollution prediction remains challenging due to nonlinear spatiotemporal coupling and the difficulty of modeling local pollutant agglomeration. To address these issues, this study proposes a CNN–LSTM–Transformer multimodal prediction framework integrated with Bayesian Optimization. First, the Local Moran’s Index (LMI) is introduced as a spatial perception feature and concatenated with pollutant concentration sequences before being input into the CNN module. This design enhances the model’s ability to identify local pollutant clustering and spatial heterogeneity. Second, the LSTM architecture adopts a dual-channel structure: the main channel employs bidirectional LSTM to extract temporal dependencies, while the auxiliary channel uses unidirectional LSTM to capture evolutionary trends. A Transformer with a multi-head attention mechanism is then introduced to perform global modeling. Bayesian Optimization is employed to automatically adjust key hyperparameters, thereby improving the model’s stability and convergence efficiency. Empirical results based on atmospheric pollution monitoring data from Sichuan Province during 2021–2024 demonstrate that the proposed model outperforms various mainstream methods in predicting six pollutants in Chengdu. For instance, the MAE for PM_2.5 decreased by 14.9–22.1%, while the coefficient of determination (R²) remained stable between 87% and 89%. The accuracy decay rate across four-day forecasts was controlled within 12.4%. Furthermore, in PM_2.5 generalization prediction tasks across four other cities—Yibin, Zigong, Nanchong, and Mianyang—the model exhibited superior stability and robustness, achieving an average R² of 87.4%. These findings highlight the model’s long-term stability and regional generalization capability, offering reliable technical support for air pollution prediction and control strategies in Sichuan Province and potentially beyond. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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26 pages, 2952 KiB

Open AccessArticle

Evaluation of the Reanalysis and Satellite Surface Solar Radiation Datasets Using Ground-Based Observations over India

by Ashwin Vijay Jadhav, Ketaki Belange, Nikhil Gajbhiv, Vinay Kumar, P. R. C. Rahul, B. L. Sudeepkumar and Rohini Lakshman Bhawar

Atmosphere 2025, 16(8), 957; https://doi.org/10.3390/atmos16080957 - 11 Aug 2025

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR [...] Read more.

Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (K_t). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m²), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m²; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes. Full article

(This article belongs to the Section Climatology)

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16 pages, 1990 KiB

Open AccessArticle

Applicability Assessment of ERA5 Surface Wind Speed Data Across Different Landforms in China

by Peng Zuo, Xiangdong Chen and Lihua Zhu

Atmosphere 2025, 16(8), 956; https://doi.org/10.3390/atmos16080956 - 11 Aug 2025

Accurate surface wind speed data are vital for atmospheric science, climatology, and energy applications. European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5), as one of the most widely used global reanalysis datasets, has insufficient assessment of its applicability across diverse landform types. [...] Read more.

Accurate surface wind speed data are vital for atmospheric science, climatology, and energy applications. European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5), as one of the most widely used global reanalysis datasets, has insufficient assessment of its applicability across diverse landform types. Using the gridded observational dataset over China (CN05.1) and the Global Basic Landform Units dataset, this study evaluated the surface wind speed data from ERA5 over various altitudinal zones and undulating terrains in China via root-mean-square differences (RMSD) and mean absolute percentage error (MAPE) against CN05.1 observations. Results reveal significant regional variations, with ERA5 effectively capturing the spatial distribution of mean wind speeds but systematically underestimating magnitudes, particularly in plateau and mountainous regions. ERA5 reanalysis fails to reproduce the observed altitudinal increase in surface wind speed. Elevation-dependent biases are prominent, with RMSD and MAPE increasing from low-altitude to high-altitude areas. Terrain complexity exacerbates errors, showing maximum deviations in high-relief mountains and minimum deviations in hilly regions. These biases evolve seasonally, peaking in spring and reaching minima in winter. In summary, discrepancies between observations and ERA5 vary with altitude, topographic relief, and season. The most significant deviations occur for spring surface winds in high-altitude, high-relief mountains, with mean RMSD reaching 3.3 m/s and MAPE 553%. The findings highlight the limitations of ERA5 reanalysis data in scientific and operational contexts over complex terrains. Full article

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

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11 pages, 2212 KiB

Open AccessArticle

Vertical Evolution of Volatile Organic Compounds from Unmanned Aerial Vehicle Measurements in the Pearl River Delta, China

by Meng-Xue Tang, Bi-Xuan Wang, Yong Cheng, Hui Zeng and Xiao-Feng Huang

Atmosphere 2025, 16(8), 955; https://doi.org/10.3390/atmos16080955 - 10 Aug 2025

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using [...] Read more.

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using an unmanned aerial vehicle (UAV) equipped with a VOC multi-channel sampling system, up to a height of 500 m in Shenzhen, China. Results showed that total VOC (TVOC) concentrations decreased with altitude in the morning, reflecting the influence of surface-level local emissions, but increased with height at midday, likely driven by regional transport and potentially stronger photochemical processes. Source apportionment revealed substantial industrial emissions across all altitudes, vehicular emissions concentrated near the surface, and biomass burning primarily impacting higher layers. Clear evidence of enhanced secondary formation of oxygenated VOCs (OVOCs) was observed along the vertical gradient, particularly at midday, indicating intensified photochemical processes at higher altitudes. These findings underscore the importance of considering vertical heterogeneity in VOC distributions when modeling O₃ formation or developing measures to reduce emissions at different altitudes, and also demonstrate the potential of UAV platforms to provide high-resolution atmospheric chemical data in complex urban environments. Full article

(This article belongs to the Special Issue Biogenic Volatile Organic Compound: Measurement and Emissions)

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19 pages, 60167 KiB

Open AccessArticle

Mapping Ecosystem Carbon Storage in the Nanling Mountains of Guangdong Province Using Machine Learning Based on Multi-Source Remote Sensing

by Wei Wang, Liangbo Tang, Ying Zhang, Junxing Cai, Xiaoyuan Chen and Xiaoyun Mao

Atmosphere 2025, 16(8), 954; https://doi.org/10.3390/atmos16080954 - 10 Aug 2025

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This [...] Read more.

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

(This article belongs to the Topic Big Data Analytics for Climate and Human Impacts on Terrestrial Ecosystems)

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23 pages, 2327 KiB

Open AccessReview

Development and Application of Climate Zoning for Asphalt Pavements in China: A Review and Perspective

by Huanyu Chang, Xuesen Wang and Naren Fang

Atmosphere 2025, 16(8), 953; https://doi.org/10.3390/atmos16080953 - 10 Aug 2025

Asphalt pavements are highly sensitive to climatic conditions, and their performance and longevity are significantly affected by temperature fluctuations, precipitation, and extreme weather events. With increasing climate variability, the development of refined and adaptive climate zoning systems for pavement engineering has become essential. [...] Read more.

Asphalt pavements are highly sensitive to climatic conditions, and their performance and longevity are significantly affected by temperature fluctuations, precipitation, and extreme weather events. With increasing climate variability, the development of refined and adaptive climate zoning systems for pavement engineering has become essential. This study reviews the evolution, methodologies, and applications of asphalt pavement climate zoning in China. First, it delineates the historical progression of climate zoning into three stages, from general natural zoning to the specialized three-indicator model and performance grade (PG) system, and finally to refined spatial processing based on meteorological data. Notably, 48% of provinces have conducted localized zoning studies, with South and Northeast China as key focus areas. Second, this study classifies existing zoning models into three major categories: the traditional three-indicator model (based on high temperature, low temperature, and precipitation), the hydrothermal coefficient model tailored to hot, humid climates, and clustering models incorporating spatial interpolation and multivariate analysis. While the three-indicator model remains the most widely applied due to its simplicity, it may result in coarse divisions in climatically diverse regions. The hydrothermal model offers general guidance but limited accuracy, whereas clustering methods provide high-resolution, adaptive zoning results at the cost of increased computational complexity. Third, the application of climate zoning results to the PG system for asphalt binder classification is analyzed. Although SHRP, LTPP, and C-SHRP formulas are commonly used, C-SHRP tends to overestimate pavement temperatures by 6.0–8.6 °C in China. Approximately 68.8% of studies rely on existing formulas, while 31.2% propose localized conversions to improve PG grading accuracy. Overall, this review identifies both the methodological diversity and key challenges in China’s climate zoning practices and provides a scientific foundation for more performance-oriented, climate-resilient pavement design strategies. Full article

(This article belongs to the Section Climatology)

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24 pages, 2422 KiB

Open AccessArticle

Global Land Monsoon Area Response to Natural Forcing Drivers over the Last Millennium in a Community Earth System Model Ensemble

by Sizheng Gao, Zhiyuan Wang and Jia Jia

Atmosphere 2025, 16(8), 952; https://doi.org/10.3390/atmos16080952 - 9 Aug 2025

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly [...] Read more.

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly understood, the responses of the GLM area remain less explored. This study investigates the forced interdecadal variability in the GLM area using the Community Earth System Model Ensemble, focusing on two key drivers: global mean surface temperature (GMST) changes and variations in the tropical Pacific temperature gradient (TPTG). Our analysis reveals that these drivers explain approximately 33% of forced GLM area variance. Global cooling (Cool-GMST) and weakened Pacific gradients (Weak-TPTG) induce significant area contractions of −0.37% and −0.74%, respectively. Most notably, the response to compound forcing is highly non-linear. Concurrent episodes of strong cooling and Weak-TPTG induce a substantially amplified GLM area reduction of −1.37%, far exceeding the linear sum of the individual driver effects. This non-linear amplification, driven by synergistic decreases in both APR and SPF, challenges the conventional assumptions used to model and attribute monsoon boundary changes. This discovery of a non-linear threshold-dependent behavior in the monsoon’s spatial extent, which contrasts with the more linear response of monsoon intensity, is a key finding of our study. This distinction is critical for interpreting paleoclimate records, and serves as a strong indication that future climate projections must account for such non-linearities to avoid underestimating the risk of abrupt monsoon boundary shifts under combined natural and anthropogenic stressors. Full article

(This article belongs to the Section Climatology)

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22 pages, 7438 KiB

Open AccessArticle

Expanding Continuous Carbon Isotope Measurements of CO₂ and CH₄ in the Italian ICOS Atmospheric Consortium: First Results from the Continental POT Station in Potenza (Basilicata)

by Antonella Buono, Isabella Zaccardo, Francesco D’Amico, Emilio Lapenna, Francesco Cardellicchio, Teresa Laurita, Davide Amodio, Canio Colangelo, Gianluca Di Fiore, Aldo Giunta, Michele Volini, Claudia Roberta Calidonna, Alcide Giorgio di Sarra, Serena Trippetta and Lucia Mona

Atmosphere 2025, 16(8), 951; https://doi.org/10.3390/atmos16080951 - 8 Aug 2025

Carbon isotope fractionation is an efficient tool used for the discrimination and differentiation of sinks and emission sources. Carbon dioxide (CO₂) and methane (CH₄) are among the key drivers of climate change, and a detailed evaluation of variations in [...] Read more.

Carbon isotope fractionation is an efficient tool used for the discrimination and differentiation of sinks and emission sources. Carbon dioxide (CO₂) and methane (CH₄) are among the key drivers of climate change, and a detailed evaluation of variations in the ¹³C/¹²C ratio in either compound provides vital information for the field of atmospheric sciences. The Italian atmospheric ICOS (Integrated Carbon Observation System) consortium is currently implementing δ¹³C-CO₂ and δ¹³C-CH₄ measurements, with four observation sites now equipped with Picarro G2201-i CRDS (Cavity Ring-Down Spectrometry) analyzers. In this work, results from the first two months of measurements performed at the Potenza station in southern Italy between 20 February and 20 April 2025 are presented and constitute the first evaluation of continuous atmospheric δ¹³C-CO₂ and δ¹³C-CH₄ measurements from an Italian station. These results provide a first insight on how these measurements can improve the current understanding of CO₂ and CH₄ variability in the Italian peninsula and the central Mediterranean sector. Although preliminary in nature, the findings of these measurements indicate that fossil fuel burning is responsible for the observed peaks in CO₂ concentrations. CH₄ has a generally stable pattern; however, abrupt peaks in its isotopic delta, observed during March, may constitute the first direct evidence in Italy of Saharan dust intrusion affecting carbon isotope fractionation in the atmosphere. This study also introduces an analysis of the weekly behavior in isotopic deltas. Full article

(This article belongs to the Section Air Pollution Control)

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16 pages, 18507 KiB

Open AccessArticle

Spatiotemporal Ionospheric TEC Prediction with Deformable Convolution for Long-Term Spatial Dependencies

by Jie Li, Jian Xiao, Haijun Liu, Xiaofeng Du and Shixiang Liu

Atmosphere 2025, 16(8), 950; https://doi.org/10.3390/atmos16080950 - 7 Aug 2025

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for [...] Read more.

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for irregular TEC variations. To address this limitation, we enhance SA-ConvLSTM by incorporating deformable convolution, proposing SA-DConvLSTM. This achieves adaptive spatial feature extraction through learnable offsets in convolutional kernels. Building on this improvement, we design ED-SA-DConvLSTM, a TEC spatiotemporal prediction model based on an encoder–decoder architecture with SA-DConvLSTM as its fundamental block. Firstly, the effectiveness of the model improvement was verified through an ablation experiment. Subsequently, a comprehensive quantitative comparison was conducted between ED-SA-DConvLSTM and baseline models (C1PG, ConvLSTM, and ConvGRU) in the region of 12.5° S–87.5° N and 25° E–180° E. The experimental results showed that the ED-SA-DConvLSTM exhibited superior performance compared to C1PG, ConvGRU, and ConvLSTM, with prediction accuracy improvements of 10.27%, 7.65%, and 7.16% during high solar activity and 11.46%, 4.75%, and 4.06% during low solar activity, respectively. To further evaluate model performance under extreme conditions, we tested the ED-SA-DConvLSTM during four geomagnetic storms. The results showed that the proportion of its superiority over the baseline models exceeded 58%. Full article

(This article belongs to the Section Upper Atmosphere)

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41 pages, 11949 KiB

Open AccessArticle

On the Assessment of Hourly Means of Solar Irradiance at Ground Level in Clear-Sky Conditions by the ERA5, JRA-3Q, and MERRA-2 Reanalyses

by Yves-Marie Saint-Drenan and Lucien Wald

Atmosphere 2025, 16(8), 949; https://doi.org/10.3390/atmos16080949 - 7 Aug 2025

Meteorological reanalyses are one of the means to assess the solar irradiance reaching the ground. This paper deals with estimates of the hourly means of irradiance in clear-sky conditions provided by the ERA5, JRA-3Q, and MERRA-2 reanalyses. They are compared to coincident ground-based [...] Read more.

Meteorological reanalyses are one of the means to assess the solar irradiance reaching the ground. This paper deals with estimates of the hourly means of irradiance in clear-sky conditions provided by the ERA5, JRA-3Q, and MERRA-2 reanalyses. They are compared to coincident ground-based measurements from 28 BSRN stations located worldwide, selected by a new algorithm for detecting cloud-free instants. Although ERA5 most often underestimates measurements, it is quite reliable over time because it captures the temporal variability of measurements well and provides a constant level of uncertainty. JRA-3Q offers a complex pattern with negative and positive biases depending on station and season. It captures well the temporal variability but, as a whole, is not reliable over time. None of the three reanalyses is reliable in space. Because of its use of the mean solar time instead of the true solar time, MERRA-2 suffers many drawbacks over intraday scales. Its statistical indicators exhibit marked patterns depending on the season and station. Its assimilation of aerosol properties offers advantages when compared to the climatologies used in ERA5 and JRA-3Q. This work exposes the strengths and weaknesses of each reanalysis in clear-sky conditions and formulates suggestions to providers for further improvements. Full article

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

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17 pages, 3821 KiB

Open AccessArticle

Evaluation Model of Climatic Suitability for Olive Cultivation in Central Longnan, China

by Li Liu, Ying Na and Yun Ma

Atmosphere 2025, 16(8), 948; https://doi.org/10.3390/atmos16080948 - 7 Aug 2025

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes [...] Read more.

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

(This article belongs to the Special Issue Understanding and Forecasting Seasonal Weather and Climate Extreme Events)

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20 pages, 15138 KiB

Open AccessArticle

Optimizing Pedestrian-Friendly Spaces in Xi’an’s Residential Streets: Accounting for PM_2.5 Exposure

by Xina Ma, Handi Xie and Jingwen Wang

Atmosphere 2025, 16(8), 947; https://doi.org/10.3390/atmos16080947 - 7 Aug 2025

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators [...] Read more.

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

(This article belongs to the Special Issue Characteristics and Control of Particulate Matter)

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18 pages, 4029 KiB

Open AccessArticle

Characterizing CO₂ Emission from Various PHEVs Under Charge-Depleting Conditions

by Nan Yang, Xuetong Lian, Zhenxiao Bai, Liangwu Rao, Junxin Jiang, Jiaqiang Li, Jiguang Wang and Xin Wang

Atmosphere 2025, 16(8), 946; https://doi.org/10.3390/atmos16080946 - 7 Aug 2025

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle [...] Read more.

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))

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23 pages, 11564 KiB

Open AccessArticle

Cloud-Based Assessment of Flash Flood Susceptibility, Peak Runoff, and Peak Discharge on a National Scale with Google Earth Engine (GEE)

by Ivica Milevski, Bojana Aleksova, Aleksandar Valjarević and Pece Gorsevski

Atmosphere 2025, 16(8), 945; https://doi.org/10.3390/atmos16080945 - 7 Aug 2025

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, [...] Read more.

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, and Google Earth Engine (GEE) were used to assess flood-prone zones across North Macedonia’s watersheds. The presented GEE-based assessment was accomplished by a custom script that automates the FFPI calculation process by integrating key factors derived from publicly available sources. These factors, which define susceptibility to torrential floods, include slope (Copernicus GLO-30 DEM), land cover (Copernicus GLO-30 DEM), soil type (SoilGrids), vegetation (ESA World Cover), and erodibility (CHIRPS). The spatial distribution of average FFPI values across 1396 small catchments (10–100 km²) revealed that a total of 45.4% of the area exhibited high to very high susceptibility, with notable spatial variability. The CHIRPS rainfall data (2000–2024) that combines satellite imagery and in situ measurements was used to estimate peak 24 h runoff and discharge. To improve the accuracy of CHIRPS, the data were adjusted by 30–50% to align with meteorological station records, along with normalized FFPI values as runoff coefficients. Validation against 328 historical river flood and flash flood records confirmed that 73.2% of events aligned with moderate to very high flash flood susceptibility catchments, underscoring the model’s reliability. Thus, the presented cloud-based scenario highlights the potential of the GEE’s efficacy in scalability and robustness for flash flood modeling and regional risk management at national scale. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

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16 pages, 1018 KiB

Open AccessArticle

A Study on the Improvement Pathways of Carbon Emission Efficiency in China from a Configurational Perspective Based on the Dynamic Qualitative Comparative Analysis

by Tingyu Tao and Hao Zhang

Atmosphere 2025, 16(8), 944; https://doi.org/10.3390/atmos16080944 - 6 Aug 2025

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the [...] Read more.

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the dynamic qualitative comparative analysis (DQCA) is employed to explore CEE improvement pathways from a configurational perspective, and regression analysis is used to compare the driving effects of different pathways. The findings reveal that (1) single factors cannot independently achieve high CEE; instead, multiple factors must work synergistically to form various improvement pathways, including “technology–organization dual-driven”, “environment-dominated”, and “multi-equilibrium” pathways, with industrial structure upgrading as the core factor for improving CEE; (2) temporally, these improvement pathways demonstrate universality, while, spatially, they exhibit significant provincial heterogeneity; and (3) in terms of marginal effects, the “multi-equilibrium” pathway has the strongest driving effect on CEE. The findings provide valuable policy implications for developing targeted CEE enhancement strategies across provinces at different developmental stages. Full article

(This article belongs to the Topic Climate, Health and Cities: Building Aspects for a Resilient Future)

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