Atmosphere

17 pages, 5157 KiB

Open AccessArticle

Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning

by Jiangyan Qi, Xionghui Zou and Ren He

Atmosphere 2025, 16(5), 497; https://doi.org/10.3390/atmos16050497 - 25 Apr 2025

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine [...] Read more.

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine operating parameters, and emission after-treatment device operating parameters are selected as input features for the model. The correlation analysis results indicate that the Pearson correlation coefficients of engine coolant temperature and selective catalytic reduction (SCR) after-treatment device temperature show a significant linear negative correlation with instantaneous NOx emission mass. The Mutual Information (MI) analysis reveals that engine intake air volume, SCR after-treatment device temperature and engine fuel consumption have strong nonlinear relationships with instantaneous NOx emission mass. The two-layer stacking ensemble learning model selects eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and an optimized BP neural network as base learners, with a linear regression model as the meta-learner, effectively predicting the instantaneous NOx emission mass of hybrid electric buses. The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models. Full article

(This article belongs to the Section Air Pollution Control)

27 pages, 8230 KiB

Open AccessArticle

Development of High-Precision Local and Regional Ionospheric Models Based on Spherical Harmonic Expansion and Global Navigation Satellite System Data in Serbia

by Dušan Petković, Oleg Odalović, Aleksandra Nina, Miljana Todorović-Drakul, Aleksandra Kolarski, Sanja Grekulović and Stefan Krstić

Atmosphere 2025, 16(5), 496; https://doi.org/10.3390/atmos16050496 - 25 Apr 2025

Abstract

The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS data to model ionospheric parameters. This study presents an indirect [...] Read more.

The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS data to model ionospheric parameters. This study presents an indirect approach in which the total electron content (TEC), a fundamental parameter for ionospheric conditions, is modelled as a harmonic function using spherical harmonic (SH) expansion. Station-specific (local) and regional ionospheric models are developed by decomposing ionospheric influence into deterministic and stochastic components. GNSS data from seven evenly distributed stations in Serbia were used to estimate TEC coefficients. Local models were provided in the ION format as SH coefficients, allowing TEC determination at any epoch, while regional models had a

{0.5}^{\circ} \times {0.5}^{\circ}

spatial and 2 h temporal resolution. The TEC root mean square (RMS) values ranged from 0.2 to 0.5 TECU (total electron content unit), with a mean of 0.3 TECU. Validation against global ionospheric maps showed agreement within 5.0 TECU. The impact of the SH expansion degree and order on TEC values was also analysed. These results refine regional ionospheric modelling, improving GNSS positioning accuracy in Serbia and beyond. Full article

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

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24 pages, 26319 KiB

Open AccessArticle

Modeling PM_2.5 Levels Due to Combustion Activities and Fireworks in Quito (Ecuador) for Forecasting Using WRF-Chem

by Rene Parra

Atmosphere 2025, 16(5), 495; https://doi.org/10.3390/atmos16050495 - 25 Apr 2025

Abstract

PM_2.5 levels increase in cities during the first hours of the year due to combustion activities and the use of fireworks. In Quito (2800 masl), the capital of Ecuador, air quality records at the beginning of 2020 to 2025 (6 years) ranged [...] Read more.

PM_2.5 levels increase in cities during the first hours of the year due to combustion activities and the use of fireworks. In Quito (2800 masl), the capital of Ecuador, air quality records at the beginning of 2020 to 2025 (6 years) ranged between 13.4 and 217.8 µg m⁻³ (maximum mean levels for 24 h), most of them being higher than 15.0 µg m⁻³, the current recommended concentration by the World Health Organization (WHO), highlighting the need to decrease these emissions and promote actions to reduce the exposure to these extreme events. Air pollution forecasting as a preventive warning system could help achieve this objective. Therefore, the primary aim of this research was to analyze the variation in PM_2.5 levels in this city during the initial hours of the year to define, through numerical experiments, the spatiotemporal configuration of PM_2.5 emissions to reproduce the observed PM_2.5 levels and obtain insights to build an emission-based forecasting tool. For this purpose, we modeled atmospheric variables and the PM_2.5 levels using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Consistent with the behavior suggested by records of associated meteorological variables, the modeled planetary boundary layer height (PBLH) was generally lower in the city’s south compared with the center and the north. The records and modeled results indicated that in the south, the higher PM_2.5 levels were produced by higher emissions and lower values of the PBLH compared with the center and north, highlighting the importance of reducing the PM_2.5 emissions. The emission maps used for modeling the dispersion at the beginning of 2024 and 2025 are proposed as inputs for the future forecasting of the PM_2.5 levels at the start of the year, as preventive information for the public, to discourage, in advance, both combustion activities and the use of fireworks and to take action to avoid exposure. Full article

(This article belongs to the Special Issue Atmospheric Dispersion and Chemistry Models: Advances and Applications (2nd Edition))

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14 pages, 5256 KiB

Open AccessArticle

The Spatiotemporal Variation Trends of Major Air Pollutants in Beijing from 2014 to 2023

by Yangyang Xie and Jiaqing Zhao

Atmosphere 2025, 16(5), 494; https://doi.org/10.3390/atmos16050494 - 24 Apr 2025

Abstract

Based on the hourly concentration data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated the annual [...] Read more.

Based on the hourly concentration data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated the annual average concentration variation of these pollutants, the differences between regions, and the factors influencing these changes and differences. Seasonal variations in the pollutants are examined through monthly average concentrations, and Pearson correlation coefficients are used to study their relationships. The results are as follows: (1) Over the past decade, the concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO have decreased by −67.5%, −58.6%, −81.4%, −51.9%, and −59.3%, respectively, indicating significant progress in controlling these pollutants. However, O₃ fluctuates significantly between 57 μg/m³ and 66 μg/m³, suggesting the need to improve O₃ management. (2) Air pollution levels exhibit distinct spatial variations, with better air quality in mountainous and suburban areas compared to more heavily trafficked urban zones, emphasizing the need for localized control strategies. (3) The correlation coefficients between PM_2.5, PM₁₀, SO₂, NO₂, and CO all exceeded 0.90, indicating strong positive correlations. In contrast, O₃ showed negative correlations with these five pollutants, with its most pronounced negative correlation being NO₂. Full article

(This article belongs to the Section Air Quality)

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24 pages, 16501 KiB

Open AccessArticle

Analysis of Spatio-Temporal Variation Characteristics of Air Pollutants in Zaozhuang China from 2018 to 2022

by Xiaoli Xia and Shangpeng Sun

Atmosphere 2025, 16(5), 493; https://doi.org/10.3390/atmos16050493 - 24 Apr 2025

Abstract

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) [...] Read more.

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) Overall, air pollutant concentrations in Zaozhuang decrease from 2018 to 2022, with NO₂, SO₂, PM_2.5, and PM₁₀ concentrations declining by 17.3%, 52.2%, 28.9%, and 33.6%, respectively. However, O₃ concentration increases by 2.5% in 2022 compared to 2018. Seasonally, SO₂, PM_2.5, and PM₁₀ concentrations are the highest in winter and lowest in summer, while CO, NO₂, and O₃ follow a winter > autumn > spring > summer pattern. Weekly variations show that daily average concentrations of CO, NO₂, SO₂, PM_2.5, and PM₁₀ peak on Mondays, with concentrations slightly higher on weekdays than weekends. (2) Spatially, CO, NO₂, PM_2.5, and PM₁₀ concentrations are higher in the southern region, while O₃ and SO₂ concentrations are elevated in Shizhong District, Xuecheng District, and Tengzhou City. (3) Correlation analysis reveals that meteorological parameters, such as precipitation, significantly influence pollutant concentrations, with precipitation playing a role in reducing pollutant levels. This study highlights the effectiveness of the Kriging method in analyzing the complex spatio-temporal dynamics of air pollutants, offering valuable insights for environmental policy and urban planning. Full article

(This article belongs to the Section Meteorology)

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12 pages, 1063 KiB

Open AccessArticle

An Improved One-Dimensional Variational Method for a Ground-Based Microwave Radiometer

by Hualong Yan, Di Zhou, Renxin Ji and Rongmei Geng

Atmosphere 2025, 16(5), 492; https://doi.org/10.3390/atmos16050492 - 24 Apr 2025

Abstract

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave [...] Read more.

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave radiometers. Some studies have shown that the accuracy of the BPNN retrieval algorithm is affected by training data with large deviations. In this paper, an improved 1D-VAR method is proposed, which can effectively correct the bias; the results show that the improved 1D-VAR method can provide more accurate inversion results. Compared to the BPNN and 1D-VAR methods, the root mean square errors of temperature for the improved 1D-VAR method are reduced by

60.8 %

and

29.4 %

during daytime and by

54.2 %

and

49.7 %

during nighttime, respectively. Full article

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

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19 pages, 7514 KiB

Open AccessArticle

Exploring the Influencing Factors of Surface Ozone Variability by Explainable Machine Learning: A Case Study in the Basilicata Region (Southern Italy)

by Roberta Valentina Gagliardi and Claudio Andenna

Atmosphere 2025, 16(5), 491; https://doi.org/10.3390/atmos16050491 - 24 Apr 2025

Abstract

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective [...] Read more.

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O₃ is, therefore, of great significance. In this study, a methodological approach combining both supervised and unsupervised machine learning algorithms (MLAs) with the Shapley additive explanations (SHAP) method was used to understand the key factors behind O₃ variability and to explore the nonlinear relationships linking O₃ to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O₃ variability, mainly relative humidity, wind speed, and temperature throughout the study period; (ii) an increase in the contribution of temperature, nitrogen oxides, and carbon monoxide to high O₃ concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O₃ daily patterns clustered using the k-means technique. The results obtained are expected to be useful for the definition of effective measures to prevent and/or mitigate the health damage associated with ozone exposure. Full article

(This article belongs to the Section Air Quality)

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27 pages, 8606 KiB

Open AccessArticle

The Applications of AI Tools in the Fields of Weather and Climate—Selected Examples

by Agnieszka Krzyżewska

Atmosphere 2025, 16(5), 490; https://doi.org/10.3390/atmos16050490 - 23 Apr 2025

Abstract

Large language models (LLMs) based on artificial intelligence have found applications across various sectors—including medicine, education, science, literature, and marketing. Although they offer considerable opportunities, their limitations also raise important concerns. This study evaluates several AI tools in the context of meteorology and [...] Read more.

Large language models (LLMs) based on artificial intelligence have found applications across various sectors—including medicine, education, science, literature, and marketing. Although they offer considerable opportunities, their limitations also raise important concerns. This study evaluates several AI tools in the context of meteorology and climatology. The tools examined include ChatGPT o3-mini, o1, 4.o, 4.0; Gemini Advanced 1.5 and 2.0; Copilot; Perplexity; DataAnalyst; Consensus; ScholarGPT; SciSpace; Claude; and DeepSeek. The evaluation tasks comprised cloud recognition and classification from photographs, gap-filling in literature reviews, map creation based on provided datasets, comparative interpretation of maps, and archival data retrieval from line graphs converted to numerical data. Each task was rated on a 0–5 scale. Conducted between February 2024 and February 2025, the study found that ChatGPT o3-mini excelled in cloud classification; ChatGPT4.o and ScholarGPT produced high-quality maps; Claude 3.5 Sonnet and SciSpace provided the most detailed map descriptions; and Consensus and ChatGPT o1 were the most effective for literature review support. However, all tools performed poorly in regards to archival data retrieval, with Claude 3.5 Sonnet yielding the smallest errors. Overall, substantial progress was observed over the study period. Full article

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

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Graphical abstract

19 pages, 3144 KiB

Open AccessArticle

Short-Term Temporal Variability of Radon in Finnish Dwellings and the Use of Temporal Correction Factors

by Tuukka Turtiainen, Katja Kojo and Päivi Kurttio

Atmosphere 2025, 16(5), 489; https://doi.org/10.3390/atmos16050489 - 23 Apr 2025

Abstract

(1) Background: Affordable electronic radon instruments have become increasingly popular as alternatives to traditional home radon measurements, which require a minimum duration of two months. This study aimed to determine how results obtained from these devices should be interpreted and whether short-term measurements [...] Read more.

(1) Background: Affordable electronic radon instruments have become increasingly popular as alternatives to traditional home radon measurements, which require a minimum duration of two months. This study aimed to determine how results obtained from these devices should be interpreted and whether short-term measurements lasting 2–5 days can be reliably used to assess the need for radon remediation in buildings, estimate residents’ exposure, or assess public exposure. (2) Methods: A year-long radon measurement was conducted in 55 dwellings, selected to represent the Finnish housing stock as accurately as possible. Radon concentrations were recorded hourly, and the results were analysed using probabilistic analysis to calculate the likelihood of erroneous assessments. (3) Results: If a maximum false-negative rate of 1% is accepted, the action level for a 2–5-day measurement is 90–100 Bq/m³. For measurements exceeding this threshold, a longer measurement period is necessary. (4) Conclusions: Based on this study, short-term radon measurements cannot yet be recommended as a replacement for current methods. However, the study revealed significant radon level fluctuations in September, suggesting that this period should be reconsidered for inclusion in the measurement season. Full article

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

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15 pages, 4501 KiB

Open AccessArticle

External Vibration-Assisted Carbon Dioxide Sequestration in Heavy Oil Reservoirs: The Influences of Frequency and Cavity Distribution

by Shixuan Lu, Zhengyuan Zhang, Liming Dai and Na Jia

Atmosphere 2025, 16(5), 488; https://doi.org/10.3390/atmos16050488 - 22 Apr 2025

Abstract

This study investigates the effect of external vibration stimulation on CO₂ dissolution behavior in heavy oil reservoirs, focusing on the influence of vibration frequency and cavity distribution within porous media. Experiments reveal that 5 Hz vibration significantly enhances CO₂ dissolution, while [...] Read more.

This study investigates the effect of external vibration stimulation on CO₂ dissolution behavior in heavy oil reservoirs, focusing on the influence of vibration frequency and cavity distribution within porous media. Experiments reveal that 5 Hz vibration significantly enhances CO₂ dissolution, while higher frequencies (10 Hz and 20 Hz) hinder the process. A more homogeneous and extensive distribution of oil-depleted cavities further improves dissolution rates, particularly in post-gas flooding scenarios. The dissolution process, observed under constant pressure conditions, is categorized into three stages: cavity filling, fast dissolution, and slow dissolution. Vibration stimulation effectively enhances the fast dissolution stage but has a minimal impact on the slow dissolution stage. Intermittent vibration shows mixed effects, improving dissolution at 100% oil saturation but reducing rates at 90% saturation due to cavity-induced flow disruptions. These findings demonstrate the potential of vibration-stimulated CO₂ dissolution (VS-CO₂ dissolution) as a novel technique for enhancing CO₂ storage and heavy oil recovery in reservoirs. This study provides critical insights for optimizing vibration frequency and cavity distribution, paving the way for improved field applications of this innovative technology. Full article

(This article belongs to the Special Issue CO₂ Geological Storage and Utilization (2nd Edition))

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32 pages, 13922 KiB

Open AccessArticle

Urban Air Pollution in the Global South: A Never-Ending Crisis?

by Rasa Zalakeviciute, Jesus Lopez-Villada, Alejandra Ochoa, Valentina Moreno, Ariana Byun, Esteban Proaño, Danilo Mejía, Santiago Bonilla-Bedoya, Yves Rybarczyk and Fidel Vallejo

Atmosphere 2025, 16(5), 487; https://doi.org/10.3390/atmos16050487 - 22 Apr 2025

Abstract

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter’s effect has been amplified with the arrival of 2023/24 El Niño, causing an exceptional drought in [...] Read more.

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter’s effect has been amplified with the arrival of 2023/24 El Niño, causing an exceptional drought in the Amazon basin, significantly affecting fire conditions and hydroelectric power production in several South American countries, including Ecuador. This study analyzes five criteria pollutants—carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 µm (PM_2.5)—during 2019–2024 in Quito, Ecuador, a high-elevation tropical metropolis. Despite long-term efforts to regulate emissions, air pollution levels continue to rise, driven by overlapping crises, including energy shortages, political unrest, and extreme weather events. The persistent failure to improve air quality underscores the vulnerability of developing nations to climate change-induced energy instability and the urgent need for adaptive, diversified, and resilient future energy planning. Without immediate shifts in climate adaptation policies, cities like Quito will continue to experience worsening air quality, with severe implications for public health and environmental sustainability. Full article

(This article belongs to the Special Issue Air Quality in Metropolitan Areas and Megacities (Second Edition))

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13 pages, 10859 KiB

Open AccessArticle

A Lightning Very-High-Frequency Mapping DOA Method Based on L Array and 2D-MUSIC

by Chuansheng Wang, Nianwen Xiang, Zhaokun Li, Zengwei Lyu, Yu Yang and Huaifei Chen

Atmosphere 2025, 16(5), 486; https://doi.org/10.3390/atmos16050486 - 22 Apr 2025

Abstract

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to [...] Read more.

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to estimate the Direction of Arrival (DOA) of lightning VHF radiation sources, specifically tailored for both non-uniform and uniform L-shaped arrays (2D-MUSIC). The proposed approach integrates the Random Sample Consensus (RANSAC) algorithm with 2D-MUSIC, thereby enhancing the precision and robustness of the reconstruction process. Initially, the array data are subjected to denoising via the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Following this, the covariance matrix of the processed array data is decomposed to isolate the signal subspace, which corresponds to the signal components, and the noise subspace, which is orthogonal to the signal components. By exploiting the orthogonality between these subspaces, the method achieves an accurate estimation of the signal incidence direction, thereby facilitating the precise reconstruction of the lightning channel. To validate the feasibility of this method, comprehensive numerical simulations were conducted, revealing remarkable accuracy with elevation and azimuth angle errors both maintained below 1 degree. Furthermore, VHF non-uniform and uniform L-shaped lightning observation systems were established and deployed to analyze real lightning events occurring in 2021 and 2023. The empirical results demonstrate that the proposed method effectively reconstructs lightning channel structures across diverse L-shaped array configurations. This innovative approach significantly augments the capabilities of various broadband VHF arrays in radiation source imaging and makes a substantial contribution to the study of lightning development processes. The findings of this study underscore the potential of the proposed methodology to advance our understanding of lightning dynamics and enhance the accuracy of lightning channel reconstruction. Full article

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

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9 pages, 3305 KiB

Open AccessArticle

Impact of East Pacific La Niña on Caribbean Climate

by Mark R. Jury

Atmosphere 2025, 16(4), 485; https://doi.org/10.3390/atmos16040485 - 21 Apr 2025

Abstract

Statistical cluster analysis applied to monthly 1–100 m ocean temperatures reveals El Niño–Southern Oscillation (ENSO) dipole patterns with a leading mode having opposing centers of action across the dateline and tropical east Pacific. We focus on the La Niña cold phase and study [...] Read more.

Statistical cluster analysis applied to monthly 1–100 m ocean temperatures reveals El Niño–Southern Oscillation (ENSO) dipole patterns with a leading mode having opposing centers of action across the dateline and tropical east Pacific. We focus on the La Niña cold phase and study its impact on the Caribbean climate over the period of 1980–2024. East dipole time scores are used to identify composite years, and anomaly patterns are calculated for Jan-Jun and Jul-Dec. Convective responses over the Caribbean exhibit seasonal contrasts: dry winter–spring and wet summer–autumn. Trade winds and currents across the southern Caribbean weaken and lead to anomalous warming of upper ocean temperatures. Sustained coastal upwelling off Peru and Ecuador during east La Niña is teleconnected with easterly wind shear and tropical cyclogenesis over the Caribbean during summer, leading to costly impacts. This ocean–atmosphere coupling is quite different from the more common central Pacific ENSO dipole. Full article

(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability (2nd Edition))

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25 pages, 8173 KiB

Open AccessArticle

Advancing Heat Health Risk Assessment: Hotspot Identification of Heat Stress and Risk Across Municipalities in Algiers, Algeria

by Dyna Chourouk Zitouni, Djihed Berkouk, Mohamed Elhadi Matallah, Mohamed Akram Eddine Ben Ratmia and Shady Attia

Atmosphere 2025, 16(4), 484; https://doi.org/10.3390/atmos16040484 - 21 Apr 2025

Abstract

With accelerating surface warming trends in urban regions, cities like Algiers are increasingly exposed to extreme heat, contributing to a growing concern over heat-related illnesses. For a comprehensive long-term assessment (2001–2023) of heat-related risks in Algiers, multi-decade satellite, meteorological, and census data were [...] Read more.

With accelerating surface warming trends in urban regions, cities like Algiers are increasingly exposed to extreme heat, contributing to a growing concern over heat-related illnesses. For a comprehensive long-term assessment (2001–2023) of heat-related risks in Algiers, multi-decade satellite, meteorological, and census data were used in this study to map and assess spatial patterns of the Heat Health Risk Index (HHRI) within the framework established by the Intergovernmental Panel on Climate Change (IPCC) incorporating hazard, exposure and vulnerability components. The Universal Thermal Climate Index (UTCI) was then calculated to assess thermal stress levels during the same period. Following this, the study addressed a critical research gap by coupling the HHRI and UTCI and identified hotspots using the Getis-Ord Gi* statistical analysis tool. Our findings reveal that the intensity of HHRI has increased over time since “very-low” risk areas had an outstanding decrease (93%) and a 6 °C UTCI rise over 23 years reaching the “very strong heat stress” level. The coupled index demonstrated greater and different risk areas compared to the HHRI alone, suggesting that the coupling of both indicators enhances the sensitivity of heat risk assessment. Finally, persistently identified hotspots in central and eastern regions call for localized, targeted interventions in those areas and highlight the value of remote sensing in informing policymakers and enhancing climate resilience. Full article

(This article belongs to the Special Issue Extreme Weather Events in a Warming Climate)

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15 pages, 24025 KiB

Open AccessArticle

Investigating the Trend Changes in Temperature Extreme Indices in Iran

by Saeedeh Kamali, Ebrahim Fattahi and Maral Habibi

Atmosphere 2025, 16(4), 483; https://doi.org/10.3390/atmos16040483 - 21 Apr 2025

Abstract

It is necessary to evaluate the response of extreme events to global warming across different climates and geographical regions. This study aims to examine the trend changes of 13 temperature extreme indices over a 30-year statistical period from 1990 to 2020, using daily [...] Read more.

It is necessary to evaluate the response of extreme events to global warming across different climates and geographical regions. This study aims to examine the trend changes of 13 temperature extreme indices over a 30-year statistical period from 1990 to 2020, using daily maximum and minimum temperature data from 37 synoptic stations in Iran. The Mann–Kendall trend test was employed to analyze the data trends. The results indicate that, except for the two indices, frost days (FDs) and ice days (IDs), the temperature extreme indices show an increasing trend across the country. The forward and backward graphs of the Mann–Kendall test reveal that the trend of the indices is significant at the 0.05 significance level, with the two indices TNn and TXn intersecting in 2009. This indicates that a mutation occurred in that year, where the increasing slope of the trend after the mutation is greater than the slope of the trend before the mutation. Moreover, the decadal changes of the indices in the three decades 1990–2000, 2000–2010, and 2010–2020 demonstrate that the highest increasing trend in temperature occurred in the second and third decades. Full article

(This article belongs to the Special Issue Extreme Climate in Arid and Semi-arid Regions)

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24 pages, 8538 KiB

Open AccessArticle

Drought Trend Analysis Using Standardized Precipitation Evapotranspiration Index in Cold-Climate Regions

by Yaser Sabzevari, Saeid Eslamian, Abhiram Siva Prasad Pamula and Mohammad Hadi Bazrkar

Atmosphere 2025, 16(4), 482; https://doi.org/10.3390/atmos16040482 - 21 Apr 2025

Abstract

This study aimed to conduct a drought trend analysis using the standardized precipitation evapotranspiration index (SPEI) in two mountainous and cold-climate regions in Iran and the United States (US). The Mann–Kendall test was employed to assess the trend in the Upper Colorado River [...] Read more.

This study aimed to conduct a drought trend analysis using the standardized precipitation evapotranspiration index (SPEI) in two mountainous and cold-climate regions in Iran and the United States (US). The Mann–Kendall test was employed to assess the trend in the Upper Colorado River Basin (UCRB) in the US and Lorestan province. The results reveal a predominantly decreasing trend in drought occurrences across Lorestan, especially in southern and southwestern areas with lower elevations. In contrast, the UCRB showed a positive trend, indicating a wet period. The western parts of the UCRB were predominantly affected by droughts. Among the stations, the Khorram Abad station exhibited the most statistically significant trend at the 99% confidence level (Z > 2.57). A temporal trend analysis of droughts revealed more positive and negative abrupt changes in the UCRB than in Lorestan. This indicates a higher degree of small-scale variability in the UCRB compared to Lorestan. This study indicates that factors such as elevation, land use changes, and proximity to water sources may contribute to the observed variations in drought trends. Additionally, the findings highlight that rising temperatures have a significantly greater impact on drought severity than reductions in precipitation. This study provides a temperature-responsive method for drought assessments, supporting the development of adaptive strategies that address snowmelt variability, seasonal water availability, and shifting drought patterns in cold regions. Full article

(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts (2nd Edition))

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17 pages, 7997 KiB

Open AccessArticle

Synergistic Effects of Multiple Monsoon Systems on Autumn Precipitation in West China

by Luchi Song, Lingli Fan, Chunqiao Lin, Jiahao Li and Jianjun Xu

Atmosphere 2025, 16(4), 481; https://doi.org/10.3390/atmos16040481 - 20 Apr 2025

Abstract

Multiple monsoon systems impact autumn precipitation in West China; however, their synergistic influence is unknown. Here, we employed statistical analysis of Global Precipitation Climatology Project Version 3.2 precipitation data, European Center for Medium-Range Weather Forecasts ERA5 reanalysis data, and Coupled Model Intercomparison Project [...] Read more.

Multiple monsoon systems impact autumn precipitation in West China; however, their synergistic influence is unknown. Here, we employed statistical analysis of Global Precipitation Climatology Project Version 3.2 precipitation data, European Center for Medium-Range Weather Forecasts ERA5 reanalysis data, and Coupled Model Intercomparison Project model data, and calculated four monsoon indices to analyze the features of the East Asian Monsoon, South Asian Monsoon, Asia Zonal Circulation, and Tibetan Plateau Monsoon, as well as their synergistic impacts on autumn precipitation in West China. The East Asian Monsoon negatively influences autumn precipitation in West China through closed high pressure over Northeast China. The South Asian Monsoon encloses West China between two areas of closed high pressure; strong high pressure to the north guides the abnormal transport of cold air in Northwest China, whereas strong western Pacific subtropical high pressure guides the transport of warm and wet air to West China, which is conducive to the formation of autumn precipitation in West China. During years of strong Asia Zonal Circulation, West China is controlled by an anomalous sinking airflow, which is not conducive to the occurrence of autumn rain. During strong Tibetan Plateau Monsoon, western and southwestern China are affected by plateau subsidence flow, resulting in less precipitation. Based on the CMIP6 model data, the study found that under the SSP5-8.5 emission scenario, the future trends of the four monsoon systems will show significant differences, and the amplitude of autumn and interannual precipitation oscillations in west China will increase. Full article

(This article belongs to the Section Climatology)

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33 pages, 15492 KiB

Open AccessArticle

Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes

by Philippe Ear, Elena Di Bernardino, Thomas Laloë, Adrien Lambert and Magali Troin

Atmosphere 2025, 16(4), 480; https://doi.org/10.3390/atmos16040480 - 19 Apr 2025

Abstract

Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions [...] Read more.

Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them with either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk–Jones (BJ) statistical test, which allows for automatic and personalized splicing of parametric and non-parametric distributions, has been developed. This method, called the Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. Results show that a seasonal separation of data is necessary in order to account for intra-annual non-stationarity. Moreover, the Stitch-BJ distribution was able to consistently perform as well as or better than all the other considered models over the validation set, including the empirical distribution, which is often used due to its robustness. Finally, while methods for correcting dry day probabilities can be easily applied, their relevance can be discussed as temporal and spatial correlations are often neglected. Full article

(This article belongs to the Special Issue Weather and Climate Extremes: Observations, Modeling, and Impacts (2nd Edition))

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17 pages, 2582 KiB

Open AccessArticle

Atmospheric Pollution Particulate Matter Absorption Efficiency by Bryophytes in Laboratory Conditions

by Juta Karklina, Edgars Karklins, Lilita Abele, Jean-Baptiste Renard and Liga Strazdina

Atmosphere 2025, 16(4), 479; https://doi.org/10.3390/atmos16040479 - 19 Apr 2025

Abstract

The World Health Organization (WHO) has recognized Particulate Matter (PM) as the main threat to human health from air pollution. One of the solutions is Green Infrastructure (GI), which uses different plants to mitigate pollution. Among these plants are bryophytes (or more commonly [...] Read more.

The World Health Organization (WHO) has recognized Particulate Matter (PM) as the main threat to human health from air pollution. One of the solutions is Green Infrastructure (GI), which uses different plants to mitigate pollution. Among these plants are bryophytes (or more commonly used mosses), which have easier maintenance, lighter weight, and durability compared to vascular plants. However, currently, there is limited knowledge of its effectiveness in air pollution mitigation. By addressing this gap in current scientific knowledge, more effective deployment of GI could be introduced by municipalities for society’s health benefits. This study aimed to evaluate three species of mosses (Dicranum scoparium, Plagiomnium affine, and Hypnum cupressiforme) and one thuja (Thuja plicata) as a control species for a possible GI vertical barrier for local de-pollution. The objective was to assess different moss species’ effectiveness in air pollution PM_2.5 and PM₁₀ absorption in a laboratory setting. The practical experiment was conducted from June–July 2024 in the Laboratory of the Physics and Chemistry of Environment and Space in Orleans (LPC2E-CNRS), France. For the experiment, a unique air pollution chamber was engineered and built with a linear barrier of GI inside to measure pollution absorption before and after the barrier. With the obtained data from the sensors, the efficiency of the vegetation barrier was calculated. The total average efficiency of all 18 tests and tested moss species is 41% for PM_2.5 and 47% for PM₁₀ mass concentrations. Efficiency shows moss species’ maximum or optimal ability to absorb pollution PM_2.5 and PM₁₀ in laboratory environments, with the limitations indicated in this article. This research is an essential step towards further and more profound research on the effectiveness of GI barriers of mosses in urban environments. It significantly contributes to understanding GI effects on air pollution and presents the results for specific moss species and their capacity for PM_2.5 and PM₁₀ mitigation in the air. The novelty of the study lies in a particular application of the chosen moss species. Full article

(This article belongs to the Section Air Pollution Control)

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