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Atmosphere
Volume 17
Issue 4
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Atmosphere, Volume 17, Issue 4 (April 2026) – 94 articles

Cover Story (view full-size image): This paper presents a performance analysis of a three-channel Rayleigh lidar system used to retrieve temperature profiles in the middle atmosphere over Alaska. Multi-channel lidar systems improve signal detection over a wide altitude range, but instrumental effects such as pulse pile-up, detector gain switching, and signal-amplitude bias can distort measurements. We develop and apply correction methods for each effect, then combine the three receiver channels into a single optimized signal. The corrected temperatures show strong agreement among channels and compare well with MERRA-2 reanalysis data. These results demonstrate a reliable approach for improving accuracy in modern multi-channel lidar temperature retrievals. View this paper
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21 pages, 26507 KB  
Article
Assessment of Wind Energy Resources at 100 m in the South China Sea: Climatology and Interdecadal Variation
by Hai Xu, Jingchao Long, Zhengyao Lu, Wenji Li, Shuqi Zhuang, Shuqin Zhang and Jianjun Xu
Atmosphere 2026, 17(4), 425; https://doi.org/10.3390/atmos17040425 - 21 Apr 2026
Viewed by 268
Abstract
Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated [...] Read more.
Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated using ERA5 100 m wind data from 1944 to 2023, validated against ASCAT observations. High wind speeds and high wind power density (WPD) are concentrated southwest of Taiwan and southeast of Vietnam. Annual wind availability exceeds 6457 h across most regions, reaching up to 8283 h in optimal locations. WPD and capacity factor peak in winter (up to 2.4 × 108 Wh·m−2 and >50% capacity factor), with the most stable conditions occurring in the southwestern Taiwan Strait, southeast of the Pearl River Delta, and the Beibu Gulf. Empirical orthogonal function analysis reveals that the first mode of winter WPD accounts for 65.7% of the total variance, with a statistically significant increasing trend since 1990. The interannual variation in wind energy resources in the SCS during winter is controlled by the combined effects of sea surface temperature (SST) anomalies in the tropical Pacific and the Arctic Barents Sea. Specifically, in the years with strong wind anomalies in the SCS, mega-La Niña-type SST patterns in the tropical Pacific trigger anomalous cyclonic circulation in the SCS and the eastern Philippine Sea, while warm anomalies in the Arctic Barents Sea surface drive a wave-like structure of “anticyclone–cyclone–anticyclone” from Siberia to South China. The coupling of the two systems jointly promotes the strengthening of the South China Sea monsoon, leading to increased wind speeds and elevated WPD in the northern SCS. These findings provide a scientific basis for wind farm siting and long-term operational planning in the region. Full article
(This article belongs to the Section Climatology)
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23 pages, 2490 KB  
Article
A Unified Spatio-Temporal Data Processing Framework for Multi-Source Air Quality Forecasting
by Arun Raj Velraj and Senthil Kumar Jagatheesaperumal
Atmosphere 2026, 17(4), 424; https://doi.org/10.3390/atmos17040424 - 21 Apr 2026
Viewed by 238
Abstract
Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework designed to support multi-source air quality forecasting by jointly leveraging regulatory monitoring [...] Read more.
Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework designed to support multi-source air quality forecasting by jointly leveraging regulatory monitoring stations of the Central Pollution Control Board (CPCB) as reference-grade anchors and community-driven Internet of Things (IoT) sensing platforms for spatial densification. The proposed end-to-end workflow addresses key challenges associated with heterogeneity, data quality, and interoperability through systematic schema harmonization, multi-stage data cleaning, and robust missing data imputation using a Robocentric Iterated Extended Kalman Filter (RIEKF). The processed data are temporally aligned to a uniform sampling grid and enriched with spatial descriptors, including geospatial coordinates, administrative boundaries, and proximity-based emission features. These enriched observations are subsequently fused into a unified spatio-temporal representation that captures both spatial dependencies and temporal dynamics across the sensor network. Dynamic graphs constructed from this representation are processed using a Mobility-Aware Peripheral-Enhanced Graph Neural Network to forecast pollutant concentrations and generate categorical air quality indices. The framework is evaluated using regression metrics reported as RMSE/MAE in µg/m3 and MAPE in %, together with standard AQI classification metrics, demonstrating its effectiveness in improving predictive accuracy and robustness for real-world air quality forecasting applications. Full article
(This article belongs to the Section Air Quality)
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20 pages, 9688 KB  
Article
Analysis of Ionospheric TEC and DCB Using GPS/Galileo Observations from a Moving Navy Training Ship
by Byung-Kyu Choi, Dong-Hyo Sohn, Jong-Kyun Chung and Dong-Jin Han
Atmosphere 2026, 17(4), 423; https://doi.org/10.3390/atmos17040423 - 21 Apr 2026
Viewed by 304
Abstract
This study investigates ionospheric total electron content (TEC) estimation based on GPS and Galileo data collected aboard the training ship (HANS) from DOY 249 to 300 in 2024. The combined GPS/Galileo TEC is compared with the CODE global ionospheric map (GIM), resulting in [...] Read more.
This study investigates ionospheric total electron content (TEC) estimation based on GPS and Galileo data collected aboard the training ship (HANS) from DOY 249 to 300 in 2024. The combined GPS/Galileo TEC is compared with the CODE global ionospheric map (GIM), resulting in a mean difference of −2.41 TEC units (TECU) and a root mean square (RMS) error of 6.53 TECU. Furthermore, although inland GNSS stations in South Korea are incorporated to stabilize receiver differential code bias (DCB) estimation, the results still exhibited significant temporal fluctuations. The relationship between the Dst indices and the GPS satellite DCB values is observed. In addition, the Pearson correlation (R) and a significance level (p) are considered to analyze the relationship between receiver DCB variations and some factors, such as geomagnetic indices (Dst, Kp, and F10.7a), vertical TEC (VTEC), and multipath error. Geomagnetic indices show no clear correlation. Furthermore, receiver DCB changes exhibit a weak correlation (R~0.33 and p~0.01) with VTEC. Multipath errors show negligible correlation with receiver DCB changes. Significant fluctuations in receiver DCB values prevent a clear correlation. Therefore, we suggest that there are inherent difficulties in precisely estimating ionospheric TEC and DCB values on a HANS trajectory. Full article
(This article belongs to the Section Upper Atmosphere)
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15 pages, 1850 KB  
Article
Lower Direct N2O Emission Factors in Chinese Croplands than IPCC Defaults: A Systematic Meta-Analysis
by Ke Xu, Duo Xu, Pinrong Ji and Caiqing Qin
Atmosphere 2026, 17(4), 422; https://doi.org/10.3390/atmos17040422 - 21 Apr 2026
Viewed by 270
Abstract
Nitrous oxide (N2O) is a major agricultural greenhouse gas. Its direct emission factor (EF) is a key parameter for greenhouse gas inventories and developing mitigation strategies. However, the Intergovernmental Panel on Climate Change (IPCC) default EF may not reflect actual emissions [...] Read more.
Nitrous oxide (N2O) is a major agricultural greenhouse gas. Its direct emission factor (EF) is a key parameter for greenhouse gas inventories and developing mitigation strategies. However, the Intergovernmental Panel on Climate Change (IPCC) default EF may not reflect actual emissions from Chinese croplands. This study compiled extensive field observations from key agricultural regions in China. A systematic meta-analysis was conducted to evaluate annual N2O emissions and nitrogen fertilizer-induced direct emission factors. Subgroup analyses revealed that fertilizer type, land use, soil texture, and climate zone all significantly influence EF. Univariate meta-regression indicated that EF is positively correlated with nitrogen (N) application rate and mean annual temperature but negatively correlated with soil pH, highlighting these factors as key drivers of N2O emissions. The mean EF in Chinese croplands was about 0.68%, much lower than the 1% global default recommended by the IPCC. The combined effects of optimized agricultural management, cropping systems, and local environmental conditions help explain these lower emission factors. These findings provide a scientific basis for developing region-specific emission factors, improving cropland mitigation strategies, and enhancing the accuracy of greenhouse gas inventories. Full article
(This article belongs to the Special Issue Gas Emissions from Soils During Global Environmental Change (2nd Edition))
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19 pages, 5741 KB  
Article
Objective Classification of Convective Precipitation in Chengdu Terminal Area Using a Self-Organizing Map and Its Impacts on Terminal Area Operations
by Haotian Li, Haoya Liu, Lian Duan, Ran Li, Yecheng Zhang and Xiaowei Hu
Atmosphere 2026, 17(4), 421; https://doi.org/10.3390/atmos17040421 - 21 Apr 2026
Viewed by 226
Abstract
Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, [...] Read more.
Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, Type 1, characterized by a pattern with strong high pressure and abundant water vapor, yields the most intense precipitation. Type 2, a pattern with moderately strong high pressure and water vapor convergence, produces the second-highest precipitation. Type 3, associated with a low trough and weak water vapor conditions, has the weakest precipitation. Two indicators of the Weather Severity Index (WSI) and Node Coverage Index (NCI), respectively describing the coverage extent of heavy precipitation over the terminal area and over key arrival and departure nodes, are established and calculated based on heavy precipitation samples. The results show that Type 1 exhibits the highest WSI and NCI values, indicating the greatest potential impact. Type 2 displays a lower WSI than Type 1 but retains a relatively higher NCI, suggesting a more directionally biased impact, whereas Type 3 records the lowest values for both indicators, indicating a relatively weak impact. The integration of synoptic weather classification and spatial impact indicators offers a reference for weather-impact identification and scenario-based operational assessment in terminal areas. However, some limitations remain in the current study. The weather classification is primarily based on reanalysis data, and the correspondence between the WSI/NCI and actual airport operational constraints requires further validation. Full article
(This article belongs to the Special Issue Meteorological Extreme in China)
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3 pages, 135 KB  
Editorial
Heatwaves End—Heat Exposure Does Not
by Andreas Matzarakis
Atmosphere 2026, 17(4), 420; https://doi.org/10.3390/atmos17040420 - 21 Apr 2026
Viewed by 394
Abstract
Although heatwaves are typically defined by meteorological thresholds over consecutive days, their health impacts often extend far beyond periods of elevated temperatures [...] Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
23 pages, 1627 KB  
Article
Spatiotemporal Analysis of Methane Emissions and Mitigation Potential in China: A Scenario-Based Study Using the Greenhouse Gas—Air Pollution Interactions and Synergies—Methane Framework
by Yinhe Deng, Yun Shu, Hong Sun, Shule Liu, Zhanyun Ma, Lena Höglund-Isaksson and Qingxian Gao
Atmosphere 2026, 17(4), 419; https://doi.org/10.3390/atmos17040419 - 21 Apr 2026
Viewed by 368
Abstract
This study estimates China’s methane (CH4) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution [...] Read more.
This study estimates China’s methane (CH4) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH4 budget of 1114 MtCO2e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH4 abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article
(This article belongs to the Topic Greenhouse Gas Emission Reductions and Carbon Sequestration in Agriculture)
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31 pages, 5855 KB  
Article
Comparative Evaluation of Machine Learning and Deep Learning Models for Tropical Cyclone Track and Intensity Forecasting in the North Atlantic Basin
by Henry A. Ogu, Liping Liu and Yuh-Lang Lin
Atmosphere 2026, 17(4), 418; https://doi.org/10.3390/atmos17040418 - 20 Apr 2026
Viewed by 274
Abstract
Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, [...] Read more.
Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination (R2) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article
(This article belongs to the Special Issue Machine Learning for Atmospheric and Remote Sensing Research)
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20 pages, 1246 KB  
Article
Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment
by Aashish Upreti, Kira B. Shonkwiler, Stuart N. Riddick and Daniel J. Zimmerle
Atmosphere 2026, 17(4), 417; https://doi.org/10.3390/atmos17040417 - 20 Apr 2026
Viewed by 319
Abstract
Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global [...] Read more.
Methane (CH4) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH4 emissions at the facility-level. Near-field quantification of methane (CH4) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH4 h−1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article
(This article belongs to the Special Issue Atmospheric Emissions from Unconventional Oil and Gas Development: Measurements and Modeling)
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23 pages, 6728 KB  
Article
Formation Mechanism of Consecutive Dense Fog Events over the Ma-Zhao Expressway in Yunnan, Southwest China, Late Autumn 2022
by Yuchao Ding, Dayong Wen, Xingtong Chen, Xuekun Yang and Chang’an Xiong
Atmosphere 2026, 17(4), 416; https://doi.org/10.3390/atmos17040416 - 19 Apr 2026
Viewed by 208
Abstract
Fog is a near-surface weather phenomenon with low visibility that significantly threatens transportation safety. Understanding the spatiotemporal evolution and formation mechanisms of fog is essential for improving fog forecasting and warning services to reduce related casualties and economic losses. This study examines consecutive [...] Read more.
Fog is a near-surface weather phenomenon with low visibility that significantly threatens transportation safety. Understanding the spatiotemporal evolution and formation mechanisms of fog is essential for improving fog forecasting and warning services to reduce related casualties and economic losses. This study examines consecutive dense fog events with long duration and high intensity that occurred along the Ma-Zhao Expressway in northeastern Yunnan from 24 to 30 October 2022. Yunnan is a typical low-latitude plateau region in southwestern China with complex terrain and diverse climates, leading to particularly complicated fog formation processes. Correlation analysis indicates that thermal and vapor factors show stronger correlations with visibility, with correlation coefficients reaching 0.68 for vertical temperature difference and −0.63 for surface relative humidity, both significant at the 99% confidence level. These values are notably higher than those of dynamic factors such as near-surface wind speed, which yields a correlation coefficient of 0.47. The results confirm the dominant role of thermal and vapor conditions in the formation and maintenance of these dense fog events, together with favorable conditions including near-surface air saturation, weak dynamic processes, and a temperature inversion in the lower troposphere. Standardized anomaly analysis reveals obvious atmospheric anomalies during the fog episodes. A strong southerly wind anomaly appears in the lower troposphere, driven by a cyclone over the Philippines and an anomalous anticyclone east of Yunnan. This southerly transport delivers warm and moist air toward the Ma-Zhao Expressway, accompanied by a positive temperature anomaly of 1.7, standard deviations near 700 hPa and a positive specific humidity anomaly of more than 2 standard deviations in the lower troposphere. These conditions favor the development of temperature inversions and atmospheric saturation, further promoting the occurrence and persistence of consecutive dense fog events. This study clarifies the key effects of thermal and vapor conditions as well as low-level southerly wind anomalies on dense fog over the Yunnan low-latitude plateau. These conclusions deepen the understanding of fog formation mechanisms in complex plateau terrain and provide a scientific reference for fog forecasting and early warning along mountain expressways in similar low-latitude plateau regions. Full article
(This article belongs to the Section Meteorology)
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23 pages, 877 KB  
Article
Statistical Analysis of NO2 Emissions from Eskom’s Majuba Coal-Fired Power Station in Mpumalanga, South Africa
by Mpendulo Wiseman Mamba and Delson Chikobvu
Atmosphere 2026, 17(4), 415; https://doi.org/10.3390/atmos17040415 - 19 Apr 2026
Viewed by 259
Abstract
Gaseous emissions from coal combustion during electricity generation continue to be a challenge in South Africa. To meet the regulatory limits, it is crucial to understand the statistical distribution of such emissions from the power generating plants. The current paper characterises the nitrogen [...] Read more.
Gaseous emissions from coal combustion during electricity generation continue to be a challenge in South Africa. To meet the regulatory limits, it is crucial to understand the statistical distribution of such emissions from the power generating plants. The current paper characterises the nitrogen dioxide (NO2) emissions from Eskom’s Majuba coal-fired power station by making use of the quantile–quantile (QQ) plots and derivative plots of three statistical parent distributions, namely, the Weibull, Lognormal, and Pareto distributions. These distributions are fitted and compared according to their tail heaviness as they cater for data that may have tails lighter or heavier than that of the Exponential distribution. Of the three distributions evaluated here, the Lognormal gave the best fit for the full body of the data according to the QQ and derivative plots, and the goodness-of-fit tools (bootstrap Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Akaike Information Criterion (AIC), Schwarz’s Bayesian Information Criterion (BIC), and the BIC-corrected Vuong test for non-nested distributions). The Lognormal distribution also gave the best fit for the overall upper tail, while at the very top six largest NO2 emission observations in the upper tail, a Pareto-type tail was observed. The practical implication of a heavy tail like the Pareto is that it models more frequent larger sized NO2 emissions compared to lighter tails like the Weibull and Lognormal tails. The methods used in this study give a framework on how emissions of NO2 from a coal-fired power station can be modelled using statistical parent distributions whilst also taking into account the distribution of the data in the tails which is mostly ignored when fitting statistical parent distributions. Understanding the distribution of the upper tail is very important since higher and rare emissions are of the most concern and are dangerous to human health and the environment. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Air Quality: From Data to Predictions)
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18 pages, 14170 KB  
Article
Dual-Pathway Superposition: Independent Forcings of Spring Indian Ocean SST and Summer Tibetan Plateau Heating on Middle and Lower Yangtze Rainfall
by Miao Li, Yaoming Ma, Xiaohua Dong, Mingjing Wang, Penghui Yang, Qian Zhang and Chengqi Gong
Atmosphere 2026, 17(4), 414; https://doi.org/10.3390/atmos17040414 - 18 Apr 2026
Viewed by 241
Abstract
The Tibetan Plateau (TP) atmospheric heat source crucially modulates East Asian summer monsoon precipitation, yet its synergy with upstream oceanic signals remains elusive. Using observations (1971–2020) and CMIP6 simulations, we investigate mechanisms coupling the summer TP heating and precipitation over the Middle and [...] Read more.
The Tibetan Plateau (TP) atmospheric heat source crucially modulates East Asian summer monsoon precipitation, yet its synergy with upstream oceanic signals remains elusive. Using observations (1971–2020) and CMIP6 simulations, we investigate mechanisms coupling the summer TP heating and precipitation over the Middle and Lower Yangtze River (MLYR). SVD analysis reveals a robust positive coupling between them. Mechanistically, TP heating triggers a quasi-stationary Rossby wave train, inducing a “saddle-like” circulation that drives intense MLYR moisture convergence (contributing >90% to precipitation changes). Crucially, we re-examine the upstream oceanic precursor to propose a “dual-pathway superposition” framework. Contrary to the assumed linear causal chain, four-quadrant analysis reveals the spring Indian Ocean Basin Warming (IOBW) and summer TP heating are largely independent drivers (R = 0.24). While IOBW thermodynamically excites an Anomalous Anticyclone supplying abundant MLYR moisture, it lacks robust control over TP heating, which is dominated by internal atmospheric dynamics. However, our findings reveal a critical non-linear synergy: extreme MLYR rainfall strictly requires the coincidental phase overlap of these independent pathways (strong dynamic lifting coupled with oceanic moisture). CMIP6 simulations corroborate this independence, further emphasizing that extreme MLYR rainfall results from phase superposition rather than a single causal chain. Full article
(This article belongs to the Special Issue The Qinghai-Tibet Plateau: Its Climate/Ecology and Its Impact on Global Climate/Ecology)
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16 pages, 5559 KB  
Article
Enhancing the Usability of CALIPSO Low-Confidence Cloud Products Using a Multilayer Perceptron-Based Data Refinement Framework
by Xiaolu Luo, Wenkai Song, Shiqi Yan, Miao Zhang and Ge Han
Atmosphere 2026, 17(4), 413; https://doi.org/10.3390/atmos17040413 - 18 Apr 2026
Viewed by 208
Abstract
The CALIPSO V4.10 5 km cloud-layer product contains a small yet influential fraction of low-confidence and “unknown” cloud-type labels, which constrains its effectiveness in climatological analyses and limits its utility for downstream Earth system applications. To improve the practical usability and completeness of [...] Read more.
The CALIPSO V4.10 5 km cloud-layer product contains a small yet influential fraction of low-confidence and “unknown” cloud-type labels, which constrains its effectiveness in climatological analyses and limits its utility for downstream Earth system applications. To improve the practical usability and completeness of these observations, this study develops a multilayer perceptron (MLP)-based refinement framework using global summer daytime CALIPSO data from 2006–2021. High-confidence cloud samples (76% of the dataset), defined as cases with high Feature Type QA and high Ice/Water Phase QA, were used as the reliable supervision subset to train the MLP model using 11 geolocation-, optical-, and microphysics-related variables, including cloud optical depth, cloud thickness, depolarization ratio, and color ratio. The trained model was subsequently applied to a separately defined low-confidence cloud subset (~5% of the dataset), consisting of cases with high Feature Type QA but low Ice/Water Phase QA, of which over 60% were originally labeled as “unknown”, to generate probabilistic assignments of three cloud types: ice clouds, water clouds, and oriented ice crystals. Evaluation using withheld high-confidence samples indicates a strong level of agreement with operational CALIPSO classifications (~94.99%). Moreover, the refined low-confidence results exhibit physically coherent vertical structural characteristics consistent with established cloud thermodynamic regimes. It is emphasized that the proposed framework does not establish an independent physical truth beyond CALIOP’s measurement capability; instead, it provides a physically consistent and statistically robust approach to improving the completeness and practical usability of CALIPSO cloud-type products for large-scale scientific and modeling applications. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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22 pages, 11683 KB  
Article
Spatiotemporal Characteristics and Driving Factors of Drought-Flood Abrupt Alternation in the Sichuan Basin
by Zongying Yang, Shizhong Jiang, Hong Xie and Yule Hou
Atmosphere 2026, 17(4), 412; https://doi.org/10.3390/atmos17040412 - 18 Apr 2026
Viewed by 365
Abstract
The Sichuan Basin is a high-incidence area for China’s drought–flood abrupt alternation (DFAA) events. To reveal the spatiotemporal evolution characteristics and driving factors of drought–flood abrupt alternation (DFAA) compound disasters in the Sichuan Basin, this study identified drought-to-flood (DF) and flood-to-drought (FD) events [...] Read more.
The Sichuan Basin is a high-incidence area for China’s drought–flood abrupt alternation (DFAA) events. To reveal the spatiotemporal evolution characteristics and driving factors of drought–flood abrupt alternation (DFAA) compound disasters in the Sichuan Basin, this study identified drought-to-flood (DF) and flood-to-drought (FD) events using the Standardized Precipitation Evapotranspiration Index based on meteorological data and circulation factors from 1963 to 2022. By constructing a standardized drought–flood abrupt alternation magnitude index to classify event grades, combined with methods such as trend analysis, Morlet wavelet and Random Forest, the study explored the trend variation laws, spatial distribution patterns, and core driving factors of DFAA events in the basin. The results showed that on the interannual scale, the upward trend of FD events was more obvious than that of DF events, with a significant increase in the proportion of moderate and severe events; both the frequency and intensity of summer FD events increased significantly, and the intensity of winter FD events also exhibited a marked upward trend. Spatially, DF events occurred frequently in Guang’an and Chongqing, while FD events were concentrated in the western edge of the basin, as well as Yibin and Luzhou. Moderate and severe events were more prominent in the edge areas of the basin. The occurrence of DFAA events was generally jointly driven by the meteorological factors and regulation of large-scale sea surface temperature-circulation factors: the triggering factors of DF events showed a diversified and decentralized characteristic, while FD events were mainly driven by the subtropical high, and tropical sea surface temperature anomalies were the common precursor signal for both types of events. This study provides a scientific basis and technical support for the formulation of disaster prevention and mitigation strategies and the optimal management of water resources for compound extreme meteorological disasters in the Sichuan Basin. Full article
(This article belongs to the Special Issue Compound Events and Climate Change Impacts in Agriculture)
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24 pages, 7992 KB  
Article
Ensemble Artificial Intelligence Fusing Satellite, Reanalysis, and Ground Observations for Improved PM2.5 Prediction
by Muhammad Haseeb, Zainab Tahir, Syed Amer Mehmood, Hania Arif, Sumaira Kousar, Sundas Ghafoor and Khalid Mehmood
Atmosphere 2026, 17(4), 411; https://doi.org/10.3390/atmos17040411 - 18 Apr 2026
Viewed by 279
Abstract
Air pollution caused by fine particulate matter (PM2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM2.5 forecasting systems. This [...] Read more.
Air pollution caused by fine particulate matter (PM2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO2, SO2), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R2 = 0.77 (RMSE = 24.75 µg m−3), XGBoost R2 = 0.76 (RMSE = 26.32 µg m−3), CatBoost R2 = 0.73 (RMSE = 30.39 µg m−3), and LightGBM R2 = 0.70 (RMSE = 32.75 µg m−3). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R2 = 0.77; RMSE = 23.37 µg m−3). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO2 were the most influential predictors of PM2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article
(This article belongs to the Special Issue High-Resolution Modeling of Urban Air Quality: From Multi-Source Emissions to Scalable Prediction Systems)
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3 pages, 130 KB  
Editorial
Editorial for the Special Issue “Atmospheric Dispersion and Chemistry Models: Advances and Applications” (Second Edition)
by Daniel Viúdez-Moreiras
Atmosphere 2026, 17(4), 410; https://doi.org/10.3390/atmos17040410 - 17 Apr 2026
Viewed by 306
Abstract
Atmospheric dispersion and chemical transport models (CTMs) are indispensable tools for understanding the behavior of pollutants in the atmosphere and their link to anthropogenic emission sources [...] Full article
(This article belongs to the Special Issue Atmospheric Dispersion and Chemistry Models: Advances and Applications (2nd Edition))
16 pages, 4722 KB  
Article
Evaluating Future Global Wetland Methane Response to Extreme Heat and Precipitation Using a Wetland Methane Model LPJ-wsl
by Wei Deng, Zhen Zhang and Qiuan Zhu
Atmosphere 2026, 17(4), 409; https://doi.org/10.3390/atmos17040409 - 17 Apr 2026
Viewed by 280
Abstract
Wetlands are the largest natural source of atmospheric methane (CH4), and their emissions are projected to increase during the 21st century in response to climate change. However, how extreme climate events such as extreme heat, extreme precipitation, and their compound occurrences [...] Read more.
Wetlands are the largest natural source of atmospheric methane (CH4), and their emissions are projected to increase during the 21st century in response to climate change. However, how extreme climate events such as extreme heat, extreme precipitation, and their compound occurrences modulate future wetland methane emissions, remains poorly constrained. Here, we quantify the impacts of extreme temperature, precipitation, and compound hot–wet events on global wetland methane emissions (eCH4) using simulations from the dynamic global vegetation model LPJ-wsl driven by four CMIP5 climate models under a high-emission scenario (RCP8.5) for the period 2006–2099. Our results show that extreme heat events intensify and become substantially more frequent, with global occurrence increasing by more than 303% by the end of the century. Correspondingly, their contribution to global wetland methane emissions rises from ~26–28% in 2006 to ~73–83% by 2099, making extreme heat the dominant driver of future eCH4 increases. Extreme precipitation events exhibit relatively modest changes in frequency and mixed intensity. In contrast, compound hot–wet events, despite their low baseline frequency, increase by more than 600% and are associated with disproportionately strong methane responses, driven by the combined effects of elevated temperatures and enhanced anaerobic conditions. Across all event types, tropical wetlands account for 75–90% of global methane emissions, while contributions from mid-latitudes increase modestly and high-latitude contributions remain comparatively small. These findings highlight the emerging importance of climate extremes—particularly extreme heat and compound hot–wet events—in shaping future wetland methane emissions. Explicit consideration of extreme-event dynamics is therefore essential for improving projections of methane–climate feedback under continued global warming. Full article
(This article belongs to the Section Air Quality)
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20 pages, 7292 KB  
Article
Data-Driven Spatial Mapping of Air Pollution Exposure and Mortality Burden in Lisbon Metropolitan Area
by Farzaneh Abedian Aval, Sina Ataee, Behrouz Nemati, Bárbara T. Silva, Diogo Lopes, Vânia Martins, Ana Isabel Miranda, Evangelia Diapouli and Hélder Relvas
Atmosphere 2026, 17(4), 408; https://doi.org/10.3390/atmos17040408 - 17 Apr 2026
Viewed by 413
Abstract
Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across [...] Read more.
Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km2) annual mean concentrations of key pollutants (PM2.5, PM10 and NO2) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km2 grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article
(This article belongs to the Special Issue Urban Air Quality, Heat Islands and Public Health)
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23 pages, 10471 KB  
Article
The Interannual Variability in Madden–Julian Oscillation Intensity: Insights from Changes in Background Mean States
by Jingwen Hou, Yang Yang and Kuiping Li
Atmosphere 2026, 17(4), 407; https://doi.org/10.3390/atmos17040407 - 17 Apr 2026
Viewed by 418
Abstract
The significant interannual variability in Madden–Julian Oscillation (MJO) intensity remains incompletely understood. Empirical orthogonal function (EOF) analysis reveals that the first three leading EOF modes of the annual mean MJO intensity are significantly correlated with the Quasi-Biennial Oscillation (QBO), Eastern Pacific El Niño-Southern [...] Read more.
The significant interannual variability in Madden–Julian Oscillation (MJO) intensity remains incompletely understood. Empirical orthogonal function (EOF) analysis reveals that the first three leading EOF modes of the annual mean MJO intensity are significantly correlated with the Quasi-Biennial Oscillation (QBO), Eastern Pacific El Niño-Southern Oscillation (ENSO), and Central Pacific ENSO. Focusing on the distinct EOFs related to three key tropical interannual variabilities, we conduct an investigation into the potential governing processes through which the changes in background mean states impact MJO intensity based on the MJO moisture mode theory. Observations suggest that the accumulation of moist static energy (MSE) during MJO moistening phases and its dissipation during drying phases play a crucial role in regulating MJO amplitude. At the interannual timescale, regions characterized by positive EOF values display positive (negative) MSE tendency anomalies during MJO moistening (drying) phases, leading to amplified MSE accumulation (dissipation) throughout the MJO lifecycle and subsequently facilitating an increase in MJO amplitude. Conversely, regions with negative EOF values exhibit opposing trends. Further analysis reveals that these MSE tendency anomalies are mainly associated with the zonal advection term, which is influenced by interannual changes in the background mean MSE and low-level winds. The spatial pattern of the background mean MSE is strongly linked to sea surface temperature (SST) anomalies, with low-level background winds aligning well with the horizontal gradients of SST anomalies. Full article
(This article belongs to the Special Issue Research on ENSO: Types and Impacts)
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11 pages, 3587 KB  
Article
Urban–Suburban PM2.5 Trends in China Under Different Urban Classification Methods
by Ning Yang, Yuanwei Zhong, Fengjuan Fan, Guangjin Liu, Zonghan Xue, Yanru Bai and Nan Lu
Atmosphere 2026, 17(4), 406; https://doi.org/10.3390/atmos17040406 - 16 Apr 2026
Viewed by 303
Abstract
Urban–suburban PM2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM2.5 monitoring data and 1 km × 1 km gridded [...] Read more.
Urban–suburban PM2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km−2) at national, Eastern and Western China scales. Results showed significant national PM2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m−3 yr−1 in summer and −6.0 to −6.3 µg m−3 yr−1 in winter, and faster air quality improvement in winter. Urban–suburban PM2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m−3 in summer, 5 µg m−3 in winter), while the 300 people km−2 threshold yielded much larger ones (11 µg m−3 in summer, 29 µg m−3 in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings. Full article
(This article belongs to the Section Air Quality)
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15 pages, 869 KB  
Article
Microbial Contamination and Ventilation Strategies in HVAC Systems: A Case-Study Assessment of Infection Risk, Energy Consumption, and Thermal Comfort
by Gabriele Battista, Leone Barbaro and Emanuele de Lieto Vollaro
Atmosphere 2026, 17(4), 405; https://doi.org/10.3390/atmos17040405 - 16 Apr 2026
Viewed by 354
Abstract
Heating, ventilation, and air conditioning (HVAC) systems are essential for indoor air quality and thermal comfort but can simultaneously act as vectors for microbial contamination, particularly bacteria and fungi. While the COVID-19 pandemic intensified focus on airborne viral transmission, bacterial and fungal contamination [...] Read more.
Heating, ventilation, and air conditioning (HVAC) systems are essential for indoor air quality and thermal comfort but can simultaneously act as vectors for microbial contamination, particularly bacteria and fungi. While the COVID-19 pandemic intensified focus on airborne viral transmission, bacterial and fungal contamination in indoor environments remains a persistent and significant health risk. This study presents a detailed case study of a restaurant HVAC system, analysing the impact of different ventilation strategies on bacterial contamination, infection transmission risk, energy consumption, and thermal comfort. By focusing on a real-world application, the research evaluates practical challenges and trade-offs associated with HVAC operation modifications aimed at mitigating microbial risks while maintaining acceptable energy and comfort levels. The research compares three operational scenarios: normal operation with air recirculation, 24 h operation with 100% outdoor air, and extended operation periods. Results demonstrate that while strategies emphasizing outdoor air intake and extended operation reduce infection probability by up to 60–65%, they simultaneously increase energy consumption by over 1700% and compromise thermal comfort parameters. In the h24 case, the pre-heat coil rises from 2421.7 to 43,923.7 kWh and the post-heat coil from 24,812.8 to 152,970.4 kWh, while the Plus 2 h strategy reduces the energy penalty by roughly 42–51% with respect to the h24 case. The findings are contextualized within current research on bacterial and fungal risks in HVAC systems, highlighting the critical need for balanced ventilation strategies that integrate health protection, energy efficiency, and comfort considerations. Full article
(This article belongs to the Special Issue Air Quality in the Era of Net-Zero Buildings)
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29 pages, 10790 KB  
Article
The Particularity of the Warm Rain in Catalonia
by Francesc Figuerola, Dolors Ballart, Tomeu Rigo and Montse Aran
Atmosphere 2026, 17(4), 404; https://doi.org/10.3390/atmos17040404 - 16 Apr 2026
Viewed by 274
Abstract
Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily [...] Read more.
Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we identified and characterized warm rain cases, and second, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, and weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern. Full article
(This article belongs to the Section Meteorology)
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23 pages, 4645 KB  
Article
A Method to Calculate the Annual Occupational Ultraviolet Exposure of Outdoor Workers from Arbitrary Personal Exposure Measurements
by Alexander Dzwonek, Florian Lubitz, Emmerich Kitz, Philipp Weihs and Alois W. Schmalwieser
Atmosphere 2026, 17(4), 403; https://doi.org/10.3390/atmos17040403 - 16 Apr 2026
Viewed by 326
Abstract
The annual occupational personal ultraviolet radiation (UVR) exposure of outdoor workers is vital for several purposes, including non-melanoma skin cancer risk assessment and the recognition of UVR-related pathologies as occupational diseases. Estimations of annual personal exposure (PE) are based on measurements, which are [...] Read more.
The annual occupational personal ultraviolet radiation (UVR) exposure of outdoor workers is vital for several purposes, including non-melanoma skin cancer risk assessment and the recognition of UVR-related pathologies as occupational diseases. Estimations of annual personal exposure (PE) are based on measurements, which are influenced by the measuring period respectively by the start and end time of the measurements, and PEs gained from different periods may differ noticeably. Therefore, we present a method that recalculates PE measurements to any other period (time and duration) during the day, and which is also applicable for measured ambient UVR to determine the relative personal UVR exposure (ERTA). The application shows the necessity of considering not only duration but especially time, as noon hours contribute differently than morning and evening hours. The uncertainties of recalculations are within ±5% if the measuring or target periods last at least 5 h and noon hours are covered. Furthermore, we propose a method to calculate annual PE using ERTA. The application for Austria shows that depending on the work-time model (working hours, working times) and date of holidays, annual PE may differ by up to 30%. Additionally, interannual variability of 16% within a ten-year period suggests avoiding a single year for consideration. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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33 pages, 5648 KB  
Article
Extreme Daily Rainfall Assessment in Arid Environments Through Statistical Modeling
by Ali Aldrees and Abubakr Taha Bakheit Taha
Atmosphere 2026, 17(4), 402; https://doi.org/10.3390/atmos17040402 - 16 Apr 2026
Viewed by 369
Abstract
Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant [...] Read more.
Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article
(This article belongs to the Section Meteorology)
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21 pages, 14159 KB  
Article
Long-Term Links Between Precipitation Regimes and PM2.5 in an Urban Area of Eastern Amazonia (Belém, Brazil), 1980–2024
by Rafael Palácios, Andrea Machado, Rita de Cássia Franco, Fernando G. Morais, Marco A. Franco, Francisco Oliveira, Glauber Cirino, Breno Imbiriba, João de Athaydes Silva, Júnior, Leone F. A. Curado, Thiago R. Rodrigues, Amaury de Souza, João Basso, Marcelo Biudes, Maurício Moura, Julia Cohen and Danielle Nassarden
Atmosphere 2026, 17(4), 399; https://doi.org/10.3390/atmos17040399 - 16 Apr 2026
Viewed by 416
Abstract
Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM2.5 variability, and precipitation is often expected to reduce particle loads through [...] Read more.
Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM2.5. Stratified analyses show that PM2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article
(This article belongs to the Special Issue Advances in Atmospheric Aerosol Measurement Techniques)
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26 pages, 1501 KB  
Article
Heat and Cold Extremes and Urban Mortality in Greece: An Event-Based Assessment Using Cumulative Thermal Stress Indices
by Ilias Petrou and Pavlos Kassomenos
Atmosphere 2026, 17(4), 401; https://doi.org/10.3390/atmos17040401 - 15 Apr 2026
Viewed by 373
Abstract
Extreme temperatures increasingly threaten public health, yet temperature–mortality relationships vary substantially across regions and are often obscured by average exposure–response models. This study investigates heat- and cold-related mortality in five climatically diverse Greek cities—Athens, Thessaloniki, Larissa, Patra, and Heraklion—during 1992–2024 using an event-based [...] Read more.
Extreme temperatures increasingly threaten public health, yet temperature–mortality relationships vary substantially across regions and are often obscured by average exposure–response models. This study investigates heat- and cold-related mortality in five climatically diverse Greek cities—Athens, Thessaloniki, Larissa, Patra, and Heraklion—during 1992–2024 using an event-based framework that integrates cumulative thermal stress with synoptic atmospheric conditions. Heat and cold events were defined using the Excess Heat Factor and Excess Cold Factor, combined with persistence criteria and Spatial Synoptic Classification air masses. Mortality responses were assessed through daily mortality ratios, regression analyses, and event severity categories. Dry Moderate air masses dominated across cities, accounting for more than 60% of all days in each city, indicating that extremes typically reflect departures from generally mild background conditions. Linear associations between cumulative thermal stress and mortality were weak overall, with correlation coefficients generally below |0.15| for cold events and below 0.20 for heat events. However, severe heat events produced substantial mortality increases, with mean mortality ratios reaching 1.69 in Larissa and exceeding 1.30 in all cities, despite relatively low event frequency. In contrast, cold-related mortality was often linked to frequent lower-severity events, particularly in Thessaloniki (more than 200 cold events) and Athens. These findings demonstrate that mortality risk concentrates in discrete high-impact episodes rather than increasing linearly with thermal stress, underscoring the value of event-based approaches for locally tailored adaptation and early-warning strategies. Full article
(This article belongs to the Special Issue The Drivers and Impacts of Climate Change Over the Eastern Mediterranean)
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19 pages, 3921 KB  
Article
Temperature Retrievals for a Three-Channel Rayleigh Lidar System
by Satyaki Das, Richard Collins and Jintai Li
Atmosphere 2026, 17(4), 400; https://doi.org/10.3390/atmos17040400 - 15 Apr 2026
Viewed by 271
Abstract
We present the performance of a middle atmosphere Rayleigh lidar system that employs three receiver channels. We characterize the biases in the density and temperature profiles retrieved from each of the receiver channels as well as the combined receiver signal. We associate these [...] Read more.
We present the performance of a middle atmosphere Rayleigh lidar system that employs three receiver channels. We characterize the biases in the density and temperature profiles retrieved from each of the receiver channels as well as the combined receiver signal. We associate these biases with pulse pile-up, gain switching, and variations in the detector gain due to signal amplitude. We use a top-down temperature convergence methodology to determine the upper altitude up to which the signals should be compensated for the variations in detector gain. We find that the channels have warm biases in their temperatures of 2–8 K at 40 km. These biases decrease to between 1 K and 3 K at 60 km. Uncertainty estimates derived from the photon-counting statistics indicate temperature uncertainties on the order of 2–5 K in the 40–70 km region, which are consistent with the observed level of inter-channel variability after correction. A comparison with MERRA-2 reanalysis indicates an overall agreement in temperatures and differences that are consistent with the comparisons between the Rayleigh lidars and MERRA-02 at other sites. These results demonstrate that the proposed approach proves reliable for processing the multi-channel Rayleigh lidar data, particularly for systems employing more than two detection channels, and improves the fidelity and accuracy of the temperature retrievals. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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16 pages, 5238 KB  
Article
Projected Increase in Clear-Air Turbulence over Southwest China Under Climate Change
by Ruping Zhang, Zhigang Cheng, Wenjun Sang, Yu Huang and Tingwei Cao
Atmosphere 2026, 17(4), 398; https://doi.org/10.3390/atmos17040398 - 15 Apr 2026
Viewed by 384
Abstract
Changes in aviation turbulence at cruise altitudes have important implications for aviation safety under global warming scenarios in the future. Using projections from the NorESM2-MM model within the CMIP6 framework, this study evaluates changes in clear-air turbulence (CAT) at 250 hPa over Southwest [...] Read more.
Changes in aviation turbulence at cruise altitudes have important implications for aviation safety under global warming scenarios in the future. Using projections from the NorESM2-MM model within the CMIP6 framework, this study evaluates changes in clear-air turbulence (CAT) at 250 hPa over Southwest China during the twenty-first century based on an ensemble of 15 diagnostic indices. The results show: (1) Historical moderate-or-greater (MOG) CAT peaks in a zonal belt near 30–35° N, with annual frequencies up to 1.6% over the Hengduan and Karakoram Mountains. Future increases remain focused in this belt, are stronger and more extensive under SSP5-8.5, peak in winter and spring, and weaken over much of the Plateau interior in summer. (2) Future changes are intensity-dependent: stronger categories show larger relative increases, and PDF changes are concentrated in the right tail, indicating amplified extreme turbulence. The 19-year moving-average time series shows that MOG-CAT increases by 28.3% and 36.5% under SSP2-4.5 and SSP5-8.5, respectively, by the mid-twenty-first century, and by 26.0% and 69.4% by the late twenty-first century. (3) Along the Chengdu–Lhasa corridor, winter MOG-CAT increases in all three segments. Under SSP5-8.5, median increases are about 50% in the Basin and Plateau segments and about 85% in the Transition segment, with most diagnostics ranging from 50% to 180%. (4) High-emission scenarios are more likely to cause turbulence and instability in the southwestern region, potentially posing greater challenges for aviation turbulence warning and safety assurance. Full article
(This article belongs to the Special Issue Climate Change and Extreme Weather Disaster Risks (2nd Edition))
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17 pages, 6835 KB  
Article
Effect of Mountain Terrain near Lightning Channels on Electric Fields at Sprite Halos Initiation Region
by Xin Wang, Jinbo Zhang, Jinxin Wu, Yan Tao, Jiawei Niu, Zhibin Xie and Qilin Zhang
Atmosphere 2026, 17(4), 397; https://doi.org/10.3390/atmos17040397 - 15 Apr 2026
Viewed by 351
Abstract
The electric fields generated by lightning discharges propagate upward and couple with the lower ionosphere, triggering various mesospheric optical emissions. The potential role of local terrain in modulating the lightning-generated electric fields in the lower ionosphere remains poorly understood. To investigate the effect [...] Read more.
The electric fields generated by lightning discharges propagate upward and couple with the lower ionosphere, triggering various mesospheric optical emissions. The potential role of local terrain in modulating the lightning-generated electric fields in the lower ionosphere remains poorly understood. To investigate the effect of mountain terrain on the lightning-generated electric fields at high altitudes (70–85 km), a two-dimensional (2D) finite-difference time-domain (FDTD) simulation model was developed. The simplified mountain is parameterized by its height, width, and horizontal distance from the lightning channel. Simulation results show that mountain terrain significantly influences the lightning-driven electric field waveforms in the initiation region of sprite halos. Increased mountain height leads to greater attenuation of the high-altitude electric field amplitudes, thereby suppressing sprite halos initiation. The shielding effect of mountain width on the electric fields is less pronounced than that of mountain height, and it stabilizes when the width exceeds 40 km. When the horizontal distance between the mountain and lightning channel is less than 40 km, the electric field attenuation increases significantly with decreasing distance. The attenuation effect gradually weakens beyond a distance of 40 km, yet the electric field waveforms exhibit considerable fluctuations due to the reflection process. Full article
(This article belongs to the Section Meteorology)
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19 pages, 5991 KB  
Article
A Tropical Depression over the South China Sea in June 2025—Observational and Forecasting Aspects
by Pak Wai Chan, Yuk Sing Lui, Yin Lam Ng, Chun Kit Ho, Ching Chi Lam, Sin Ki Lai and Junyi He
Atmosphere 2026, 17(4), 396; https://doi.org/10.3390/atmos17040396 - 14 Apr 2026
Viewed by 374
Abstract
A tropical depression (TD) formed over the northern part of the South China Sea and affected Hong Kong during 25–26 June 2025. Based on the historical database, there were not many TDs following a similar track in the past, namely, a northwestward track [...] Read more.
A tropical depression (TD) formed over the northern part of the South China Sea and affected Hong Kong during 25–26 June 2025. Based on the historical database, there were not many TDs following a similar track in the past, namely, a northwestward track towards Hainan Island and the Leizhou Peninsula. This paper serves to document a number of aspects of the forecasting service for this TD, including: (1) consideration of the upgrade of the system from a tropical disturbance to a TD, possible further upgrade into a tropical storm, and the location of the centre in “multiple centre” situation (broad tropical cyclone centre and a mesocyclone embedded in the convection near the centre); (2) forecasting of the intensity and the impact on local winds; and (3) wind structure analysis based on dropsonde and wind profiler data. Moreover, this case demonstrates that artificial intelligence models are proven to provide earlier alerting of the possible occurrence of this TD and its subsequent movement towards the coast of southern China, whereas the conventional physics-based models remain useful in the forecasting of the impact of TD on the winds in Hong Kong for the operation of the tropical cyclone warning signal services. Full article
(This article belongs to the Section Meteorology)
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