Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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25 pages, 18044 KiB  
Article
Atmospheric Energetics of Three Contrasting West African Monsoon Seasons as Simulated by a Regional Climate Model
by Yves Ngueto, René Laprise and Oumarou Nikiéma
Atmosphere 2025, 16(4), 405; https://doi.org/10.3390/atmos16040405 - 31 Mar 2025
Viewed by 194
Abstract
The West African atmospheric energy budget is assessed for the first time across three contrasting monsoon seasons (dry, wet, and moderate) using the latest version of the Canadian Regional Climate Model (CRCM6/GEM5). The model is driven by ERA5 reanalysis from the European Centre [...] Read more.
The West African atmospheric energy budget is assessed for the first time across three contrasting monsoon seasons (dry, wet, and moderate) using the latest version of the Canadian Regional Climate Model (CRCM6/GEM5). The model is driven by ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). A formalism appropriate for regional climate energetics is employed to quantify the primary physical processes occurring during the West African Monsoon, with the aim of highlighting those that exhibit significant inter-seasonal variability. The atmospheric energy path shows that the time-mean available enthalpy (AM) reservoir, reflecting high surface temperatures and a lapse rate characteristic of a dry atmosphere, dominates other energy reservoirs. AM is converted into the time-mean kinetic energy (KM) and the time-variability available enthalpy (AE) reservoirs, which are converted into a time-variability kinetic energy reservoir (KE) through barotropic and baroclinic processes. AE is the lowest energy reservoir, confirming smaller temperature variations in the tropics compared to higher latitudes. Kinetic energy reservoirs KM and KE have the same order of magnitude, suggesting that mean flow is as important as eddy activities during the season. The atmospheric energy cycle computed for three contrasting rainy seasons shows that time-variability energy reservoirs (AE and KE) and main terms acting upon them, are proportional to the rainfall activity, being higher (lower) during rainy (dry) years. It also reveals that, while CA (conversion from AM to AE) and the generation term GE feed wave’s development, the frictional term DE counteracts the generation of KE to dampen the creation of transient eddies. These findings suggest that the atmospheric energetic formalism could be applied on West African seasonal forecasts and future climate simulations to implement adaptation strategies. Full article
(This article belongs to the Section Climatology)
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21 pages, 12701 KiB  
Article
An Overview of Air-Sea Heat Flux Products and CMIP6 HighResMIP Models in the Southern Ocean
by Regiane Moura, Fernanda Casagrande and Ronald Buss de Souza
Atmosphere 2025, 16(4), 402; https://doi.org/10.3390/atmos16040402 - 30 Mar 2025
Viewed by 332
Abstract
The Southern Ocean (SO) is crucial for global climate regulation by absorbing excess heat and anthropogenic CO2. However, representing air-sea heat fluxes in climate models remains a challenge, particularly in regions characterised by strong ocean–atmosphere–sea ice interactions. This study analysed air–sea [...] Read more.
The Southern Ocean (SO) is crucial for global climate regulation by absorbing excess heat and anthropogenic CO2. However, representing air-sea heat fluxes in climate models remains a challenge, particularly in regions characterised by strong ocean–atmosphere–sea ice interactions. This study analysed air–sea heat fluxes over the SO using four products and seven CMIP6 HighResMIP pairs, comparing the mean state and trends (1985–2014) of sensible and latent heat fluxes (SHF and LHF, respectively) and the impact of grid resolution refinement on their estimation. Our results revealed significant discrepancies across datasets and SO sectors, with LHF showing more consistent seasonal performance than SHF. High-resolution models better capture air–sea heat flux variability, particularly in eddy-rich regions, with climatological mean differences reaching ±20 W.m−2 and air–sea exchange variations spreading up to 30%. Most refined models exhibited enhanced spatial detail, amplifying trend magnitudes by 30–50%, with even higher values observed in some regions. Furthermore, the trend analysis showed significant regional differences, particularly in the Pacific sector, where air–sea heat fluxes showed heightened variability. Despite modelling advances, discrepancies between datasets revealed uncertainties in climate simulations, highlighting the critical need for continued improvements in climate modelling and observational strategies to accurately represent SO air–sea heat fluxes. Full article
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28 pages, 5005 KiB  
Review
Research Progress on Plasma-Assisted Catalytic Dry Reforming of Methane
by Tao Zhu, Chen Li, Xueli Zhang, Bo Yuan, Meidan Wang, Xinyue Zhang, Xudong Xu and Qian Sun
Atmosphere 2025, 16(4), 376; https://doi.org/10.3390/atmos16040376 - 26 Mar 2025
Viewed by 433
Abstract
With the significant consumption of traditional fossil fuels, emissions of greenhouse gases such as methane (CH4) and carbon dioxide (CO2) continue to rise, requiring effective treatment methods. The dry reforming of methane (DRM) offers a promising pathway for greenhouse [...] Read more.
With the significant consumption of traditional fossil fuels, emissions of greenhouse gases such as methane (CH4) and carbon dioxide (CO2) continue to rise, requiring effective treatment methods. The dry reforming of methane (DRM) offers a promising pathway for greenhouse gas mitigation by converting CH4 and CO2 into high-value syngas. However, traditional thermal catalysis is prone to catalyst deactivation due to high-temperature sintering and carbon deposition caused by side reactions. The introduction of non-thermal plasma (NTP) provides a mild reaction environment, effectively mitigating catalyst sintering and carbon deposition, extending catalyst lifespan, reducing energy consumption, and significantly enhancing reaction performance and energy efficiency. This paper reviews recent progress in plasma-assisted DRM, focusing on different plasma discharge types and catalyst materials. The synergistic effects between plasma and catalysts and the challenges and prospects of plasma-assisted DRM technology are discussed. Full article
(This article belongs to the Section Air Pollution Control)
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23 pages, 5966 KiB  
Article
Using an Artificial Neural Network to Assess Several Rainfall Estimation Algorithms Based on X-Band Polarimetric Variables in West Africa
by Fulgence Payot Akponi, Sounmaïla Moumouni, Eric-Pascal Zahiri, Modeste Kacou and Marielle Gosset
Atmosphere 2025, 16(4), 371; https://doi.org/10.3390/atmos16040371 - 25 Mar 2025
Viewed by 195
Abstract
Quantitative precipitation estimation using polarimetric radar in attenuation-prone frequency (X-band) in tropical regions characterized by convective rain systems with high intensities is a major challenge due to strong attenuations that can lead to total signal extinction over short distances. However, some authors have [...] Read more.
Quantitative precipitation estimation using polarimetric radar in attenuation-prone frequency (X-band) in tropical regions characterized by convective rain systems with high intensities is a major challenge due to strong attenuations that can lead to total signal extinction over short distances. However, some authors have addressed this issue in Benin since 2006 in the framework of the African Monsoon Multidisciplinary Analysis program. Thus, with an experimental setup consisting of an X-band polarimetric weather radar (Xport) and a network of rain gauges, investigations have started on the subject with the aim of improving rainfall estimates. Based on simulated polarimetric variables and using a Multilayer Perceptron artificial neural network, several bi-variable and tri-variable algorithms were assessed in this study. The data used in this study are of two categories: (i) simulated polarimetric variables (Rayleigh reflectivity Z, horizontal attenuation Ah, horizontal reflectivity Zh, differential reflectivity Zdr, and specific differential phase Kdp) and rainfall intensity (R) obtained from Rain Drop Size Distribution (DSD) measurements used for algorithm evaluation (training and testing); (ii) polarimetric variables measured by the Xport radar and rainfall intensity measured by rain gauges used for algorithm validation. The simulations are performed using the T-matrix code, which leverages the scattering properties of spheroidal particles. The DSD measurements taken in northwest Benin were used as input for this code. For each spectrum, the T-matrix code simulates multiple variables. The simulated data (first category) were divided into two parts: one for training and one for testing. Subsequently, the best algorithms were validated with the second category of data. The performance of the algorithms during training, testing, and validation was evaluated using metrics. The best selected algorithms are A1:R(Z,Kdp) and A12:R(Zdr,Kdp) (among the bi-variable); B2:R(Zh,Zdr,Kdp) and B3:R(Ah,Zdr,Kdp) (among the tri-variable). Tri-variable algorithms outperform bi-variable algorithms. Validation with observation data (Xport measurements and rain gauge network) showed that the algorithm B3:R(Ah,Zdr,Kdp) performs better than B2:R(Zh,Zdr,Kdp). Full article
(This article belongs to the Special Issue Applications of Meteorological Radars in the Atmosphere)
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10 pages, 4201 KiB  
Article
Reduction in Anthropogenic CO2 Emissions Detected Through Two Decades of Observation in the Tokyo Metropolitan Area
by Toshimasa Ohara, Yosuke Muto, Junichi Kurokawa, Tomohide Shimada and Mitsuo Uematsu
Atmosphere 2025, 16(4), 364; https://doi.org/10.3390/atmos16040364 - 24 Mar 2025
Viewed by 216
Abstract
Reducing CO2 emissions is a global goal aimed at mitigating climate change, but such reductions must be scientifically tracked and verified based on long-term observational data. We analyzed the long-term trend in CO2 concentration observed for a period of 19 years [...] Read more.
Reducing CO2 emissions is a global goal aimed at mitigating climate change, but such reductions must be scientifically tracked and verified based on long-term observational data. We analyzed the long-term trend in CO2 concentration observed for a period of 19 years from 2002 to 2020 at two stations in the vicinity of Tokyo, one near a mountain summit and the other suburban. The CO2 concentration was higher at the suburban station than at the mountain station, while the annual rate of increase was lower at the suburban station than at the mountain station. The difference between the CO2 concentrations at the suburban and mountain stations (ΔCO2*) showed a significant decreasing trend over the two decades. The long-term trends (−1.39 ± 0.24% yr−1) of winter-nighttime ΔCO2* closely matched the trends (−1.54 ± 0.11% yr−1) of anthropogenic CO2 emissions in the region around the two stations. Based on this similarity, we conclude that the decreasing trend in ΔCO2* corresponds to a reduction in anthropogenic CO2 emissions around the Tokyo Metropolitan Area. This is the first evidence of two-decade-scale reductions in urban CO2 emissions from long-term continuous CO2 concentration monitoring. Full article
(This article belongs to the Section Air Quality)
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23 pages, 8567 KiB  
Article
Consistency of Changes in the Ascending and Descending Positions of the Hadley Circulation Using Different Methods
by Qianye Su, Chunlei Liu, Yu Zhang, Juliao Qiu, Jiandong Li, Yufeng Xue, Ning Cao, Xiaoqing Liao, Ke Yang, Rong Zheng, Zhiting Liang, Liang Jin, Kejia Huang, Ke Jin and Nankai Zhou
Atmosphere 2025, 16(4), 367; https://doi.org/10.3390/atmos16040367 - 24 Mar 2025
Viewed by 240
Abstract
The shift in the intertropical convergence zone (ITCZ) and the poleward expansion of the Hadley circulation termini have attracted many investigations, since they affect the hydrological cycle and hence the societies and ecosystems in the tropical and subtropical areas. Using the observed precipitation [...] Read more.
The shift in the intertropical convergence zone (ITCZ) and the poleward expansion of the Hadley circulation termini have attracted many investigations, since they affect the hydrological cycle and hence the societies and ecosystems in the tropical and subtropical areas. Using the observed precipitation and three atmospheric reanalysis data sets, different methods have been employed to quantify the changes in the ITCZ position, the Hadley circulation width, terminus position, and center intensity in both hemispheres over the global and seven longitudinal sections. It is found that the ITCZ position from the centroid method is closer to the equator over the global and ocean sections than that from the maximum precipitation method and the mass streamfunction, but the variability between different methods and data sets has significant correlations. The large spread of the ITCZ latitude is mainly from the different methods used. The ITCZ position has shifted away from the equator over 1983–2023, which is consistent across data sets, and the multi-method mean trend from five significant trends is 0.22 ± 0.12°/decade over this period. The south HC branch terminus is expanding poleward; this shift, computed using different methods and data sets, is consistent, and five out of seven are significant. The terminus position shift in the north branch is mixed, and most trends are insignificant except that from P-E. The global mean south branch circulation width has a significant increasing trend, contributed mainly by the northward shift in the ITCZ position; meanwhile, the north circulation width is shrinking insignificantly over 1983–2023. The cross-equatorial atmospheric energy transport AHT and the ITCZ position θITCZ from ERA5 are generally anti-correlated, and the correlation coefficients between AHT and θITCZ from different methods are all significant. The multi-method mean northward shift of θITCZ is 3.48 °PW−1. Full article
(This article belongs to the Section Meteorology)
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19 pages, 1743 KiB  
Review
Some Recent Key Aspects of the DC Global Electric Circuit
by Michael J. Rycroft
Atmosphere 2025, 16(3), 348; https://doi.org/10.3390/atmos16030348 - 20 Mar 2025
Viewed by 614
Abstract
The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect [...] Read more.
The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect to the Earth’s surface. The circuit is formed by the current I, flowing through the ionosphere all around the world, down through the atmosphere remote from the current sources (J ~2 pA/m2 through a resistance R ~250 Ω), through the land and sea surface, and up to the thunderstorms as point discharge currents. This maintains a downward electric field E of magnitude ~130 V/m at the Earth’s surface away from thunderstorms and a charge Q ~−6.105 C on the Earth’s surface. The theoretical modelling of ionospheric currents and the miniscule geomagnetic field perturbations (ΔB ~0.1 nT) which they cause, as derived by Denisenko and colleagues in recent years, are reviewed. The time constant of the GEC, τ = RC, where C is the capacitance of the global circuit capacitor, is estimated via three different methods to be ~7 to 12 min. The influence of stratus clouds in determining the value of τ is shown to be significant. Sudden excitations of the GEC by volcanic lightning in Iceland in 2011 and near the Tonga eruption in 2022 enable τ to be determined, from experimental observations, as ~10 min and 8 min, respectively. It has been suggested that seismic activity, or earthquake precursors, could produce large enough electric fields in the ionosphere to cause detectable effects, either by enhanced radon emission or by enhanced thermal emission from the earthquake region; a review of the quantitative estimates of these mechanisms shows that they are unlikely to produce sufficiently large effects to be detectable. Finally, some possible links between the topics discussed and human health are considered briefly. Full article
(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))
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22 pages, 4257 KiB  
Article
Impacts of Low-Carbon Policies on Air Quality in China’s Metropolitan Areas: Evidence from a Difference-in-Differences Study
by Xuejiao Niu and Ying Liu
Atmosphere 2025, 16(3), 339; https://doi.org/10.3390/atmos16030339 - 17 Mar 2025
Viewed by 269
Abstract
Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon [...] Read more.
Climate change and air pollution are intrinsically interconnected as carbon dioxide and air pollutants are co-emitted during fossil fuel combustion. Low-carbon policies, aimed at mitigating carbon emissions, are also anticipated to yield co-benefits for air quality; however, the extent to which regional low-carbon policies can effectively achieve significant reductions in air pollutant levels remains uncertain. In China, the implementation of the low-carbon city pilot (LCCP) policy has reduced carbon emissions, but further research is needed to examine its effectiveness regarding achieving air quality co-benefits. Adopting a difference-in-differences model with a 19-year national database of air quality, this study examines whether the LCCP policy improves air quality in China’s metropolitan areas and explores how these policy initiatives address their air pollution challenges. The results indicate that, following the implementation of the LCCP policy, the mean, maximum, and standard deviation of the AQI in pilot cities decreased significantly by 9.3%, 20.8%, and 19.8%, respectively, compared to non-pilot cities. These results suggest that the LCCP policy significantly improves air quality and provide evidence that this improvement is facilitated by advancements in green technology, industrial restructuring, and the optimization of urban planning and landscape design. Full article
(This article belongs to the Special Issue Air Quality in Metropolitan Areas and Megacities (Second Edition))
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20 pages, 2997 KiB  
Article
A Case Study of Ozone Pollution in a Typical Yangtze River Delta City During Typhoon: Identifying Precursors, Assessing Health Risks, and Informing Local Governance
by Mei Wan, Xinglong Pang, Xiaoxia Yang, Kai Xu, Jianting Chen, Yinglong Zhang, Junyue Wu and Yushang Wang
Atmosphere 2025, 16(3), 330; https://doi.org/10.3390/atmos16030330 - 14 Mar 2025
Viewed by 363
Abstract
Ozone (O3) is a crucial atmospheric component that significantly affects air quality and poses considerable health risks to humans. In the coastal areas of the Yangtze River Delta, typhoons, influenced by the subtropical high-pressure system, can lead to complex ozone pollution [...] Read more.
Ozone (O3) is a crucial atmospheric component that significantly affects air quality and poses considerable health risks to humans. In the coastal areas of the Yangtze River Delta, typhoons, influenced by the subtropical high-pressure system, can lead to complex ozone pollution situations. This study aimed to explore the causes, sources, and health risks of O3 pollution during such events. Ground-based data from Jiaxing City’s key ozone precursor (VOCs) composition observations, ERA5 reanalysis data, and models CMAQ-ISAM and PMF were employed. Focusing on the severe ozone pollution event in Jiaxing from 3 to 11 September 2022, the results showed that local ozone production was the main contributor (60.8–81.4%, with an average of 72.3%), while external regional transport was secondary. Concentrations of olefins and aromatic hydrocarbons increased remarkably, playing a vital role in ozone formation. Meteorological conditions, such as reduced cloud cover during typhoon periphery transit, promoted ozone accumulation. By considering the unique respiratory exposure habits of the Chinese population, refined health risk assessments were conducted. Acrolein was found to be the main cause of chronic non-carcinogenic risks (NCRs), with NCR values reaching 1.74 and 2.02 during and after pollution. In lifetime carcinogenic risk (LCR) assessment, the mid-pollution LCR was 1.73 times higher, mainly due to 1,2-dichloroethane and benzene. This study presents a methodology that is readily adaptable to analogous pollution incidents, thereby providing a pragmatic framework to guide actionable local government policy-making aimed at safeguarding public health and mitigating urban ozone pollution. Full article
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21 pages, 2607 KiB  
Article
Cross-Examination of Reanalysis Datasets on Elevation-Dependent Climate Change in the Third Pole Region
by Arathi Rameshan, Prashant Singh and Bodo Ahrens
Atmosphere 2025, 16(3), 327; https://doi.org/10.3390/atmos16030327 - 13 Mar 2025
Viewed by 473
Abstract
The scarcity of in situ observation stations and the unreliability of long-term satellite data necessitate the use of reanalysis datasets to study elevation-dependent climate change (EDCC) in the third pole (TP) region. We analyzed elevation-dependent temperature and precipitation patterns over TP using the [...] Read more.
The scarcity of in situ observation stations and the unreliability of long-term satellite data necessitate the use of reanalysis datasets to study elevation-dependent climate change (EDCC) in the third pole (TP) region. We analyzed elevation-dependent temperature and precipitation patterns over TP using the ECMWF Atmospheric Reanalysis Fifth Generation (ERA5), a global reanalysis product with coarse resolution, along with three high-resolution regional reanalysis datasets that cover our study domain: Indian Monsoon Data Assimilation and Analysis (IMDAA), High Asia Refined Analysis—Version 2 (HAR-v2), and Tibetan Plateau Regional Reanalysis (TPRR). Comparing the performance of the four reanalysis datasets in capturing EDCC over TP is crucial, as these datasets provide spatially and temporally consistent data at an optimum resolution that greatly aids EDCC research. Our study results reveal the following: (1) A positive elevation-dependent warming trend is observed across all four datasets in winter and autumn, with varying magnitudes of warming across the datasets. (2) All four datasets exhibit positive elevation-dependent wetting trends in all seasons, except autumn. These are primarily driven by pronounced drying trends at lower elevations and relatively minimal changes in precipitation trends at higher elevations. (3) ERA5 and IMDAA exhibit similar results in capturing elevation-dependent climate change, whereas the TPRR dataset reveals more extreme and unique features in temperature trends compared to the other three datasets. HAR-v2 shows smaller variations in temperature and precipitation trends across different elevations and seasons, in contrast to the other three datasets. While all reanalysis datasets indicate EDCC in the TP, their varying degrees of seasonal and spatial differences underscore the need for a careful evaluation before using them as reference data. Comparison of reanalysis datasets with available observational records, such as in situ measurements and satellite data, over overlapping spatial and temporal domains is essential to assess their quality. This evaluation can help identify the most suitable reanalysis dataset, or combination of datasets, to serve as reliable a reference even in regions or periods without observational data. Full article
(This article belongs to the Section Climatology)
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19 pages, 2442 KiB  
Article
Assessing the Impact of Climatic Factors and Air Pollutants on Cardiovascular Mortality in the Eastern Mediterranean Using Machine Learning Models
by Kyriaki Psistaki, Damhan Richardson, Souzana Achilleos, Mark Roantree and Anastasia K. Paschalidou
Atmosphere 2025, 16(3), 325; https://doi.org/10.3390/atmos16030325 - 12 Mar 2025
Cited by 1 | Viewed by 1073
Abstract
Cardiovascular diseases are the most common cause of death worldwide, with atmospheric pollution, and primarily particulate matter, standing out as the most hazardous environmental factor. To explore the exposure–response curves, traditional epidemiological studies rely on generalised additive or linear models and numerous works [...] Read more.
Cardiovascular diseases are the most common cause of death worldwide, with atmospheric pollution, and primarily particulate matter, standing out as the most hazardous environmental factor. To explore the exposure–response curves, traditional epidemiological studies rely on generalised additive or linear models and numerous works have demonstrated the relative risk and the attributable fraction of mortality/morbidity associated with exposure to increased levels of particulate matter. An alternative, probably more effective, procedure to address the above issue is using machine learning models, which are flexible and often outperform traditional methods due to their ability to handle both structured and unstructured data, as well as having the capacity to capture non-linear, complex associations and interactions between multiple variables. This study uses five advanced machine learning techniques to examine the contribution of several climatic factors and air pollutants to cardiovascular mortality in the Eastern Mediterranean region, focusing on Thessaloniki, Greece, and Limassol, Cyprus, covering the periods 1999–2016 and 2005–2019, respectively. Our findings highlight that temperature fluctuations and major air pollutants significantly affect cardiovascular mortality and confirm the higher health impact of temperature and finer particles. The lag analysis performed suggests a delayed effect of temperature and air pollution, showing a temporal delay in health effects following exposure to air pollution and climatic fluctuations, while the seasonal analysis suggests that environmental factors may explain greater variability in cardiovascular mortality during the warm season. Overall, it was concluded that both air quality improvements and adaptive measures to temperature extremes are critical for mitigating cardiovascular risks in the Eastern Mediterranean. Full article
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17 pages, 7826 KiB  
Article
Evaluating the Spatial Coverage of Air Quality Monitoring Stations Using Computational Fluid Dynamics
by Giannis Ioannidis, Paul Tremper, Chaofan Li, Till Riedel, Nikolaos Rapkos, Christos Boikos and Leonidas Ntziachristos
Atmosphere 2025, 16(3), 326; https://doi.org/10.3390/atmos16030326 - 12 Mar 2025
Viewed by 539
Abstract
Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant [...] Read more.
Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations. Full article
(This article belongs to the Special Issue Emerging Technologies for Observation of Air Pollution (2nd Edition))
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19 pages, 6933 KiB  
Article
Role of Position of Pacific Subtropical High in Deciding Path of Tropical Storms
by Ravi Shankar Pandey
Atmosphere 2025, 16(3), 322; https://doi.org/10.3390/atmos16030322 - 11 Mar 2025
Viewed by 399
Abstract
The Pacific Subtropical High (PSH) predominantly develops during the boreal summer (June–August) over the Northwest Pacific (NWP) basin, with August accounting for the highest tropical storm (TS) frequency (46.9%). This study examines the critical influence of the PSH’s position on TS trajectories and [...] Read more.
The Pacific Subtropical High (PSH) predominantly develops during the boreal summer (June–August) over the Northwest Pacific (NWP) basin, with August accounting for the highest tropical storm (TS) frequency (46.9%). This study examines the critical influence of the PSH’s position on TS trajectories and the consequent exposure of affected countries, utilizing four decades (1977–2016) of August TS data from the NWP. A total of 55 TSs, unaffected by other environmental factors, were analyzed. The PSH’s observed position during each TS’s turning point was delineated using a geopotential height of 500 hPa, while track sinuosity was quantified using a validated sinuosity index (SI). Three distinct TS paths were identified: an eastward PSH position leads to highly sinuous tracks, directing TSs toward Japan; a westward PSH position results in straighter tracks, steering TSs toward the South China Sea (SCS) below Taiwan; and a mid-position guides TSs toward Taiwan. These findings underscore the PSH’s pivotal role in modulating TS behavior and provide valuable insights for disaster risk management agencies to mitigate TS impacts in the NWP basin, the world’s most active TS region, responsible for one-third of global tropical cyclones. Full article
(This article belongs to the Section Meteorology)
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21 pages, 4768 KiB  
Article
Evaluation of the Spatio-Temporal Variation of Extreme Cold Events in Southeastern Europe Using an Intensity–Duration Model and Excess Cold Factor Severity Index
by Krastina Malcheva, Neyko Neykov, Lilia Bocheva, Anastasiya Stoycheva and Nadya Neykova
Atmosphere 2025, 16(3), 313; https://doi.org/10.3390/atmos16030313 - 9 Mar 2025
Viewed by 733
Abstract
Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is [...] Read more.
Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is expected to have more significant socioeconomic impacts than cold extremes, the latter contributes to a wide range of adverse effects on the environment, various economic sectors and human health. The present research aims to evaluate the contemporary spatio-temporal variations of extreme cold events in Southeastern Europe through the intensity–duration cold spell model developed for quantitative assessment of cold weather in Bulgaria. We defined and analyzed the suitability of three indicators, based on minimum temperature thresholds, for evaluating the severity of extreme cold in the period 1961–2020 across the Köppen–Geiger climate zones, using daily temperature data from 70 selected meteorological stations. All indicators show a statistically significant decreasing trend for the Cfb and Dfb climate zones. The proposed intensity–duration model demonstrated good spatio-temporal conformity with the Excess Cold Factor (ECF) severity index in classifying and estimating the severity of extreme cold events on a yearly basis. Full article
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16 pages, 4285 KiB  
Article
Jazan Rainfall’s Seasonal Shift in Saudi Arabia: Evidence of a Changing Regional Climate
by M. Nazrul Islam, Arjan O. Zamreeq, Muhammad Ismail, Turki M. A. Habeebullah and Ayman S. Ghulam
Atmosphere 2025, 16(3), 300; https://doi.org/10.3390/atmos16030300 - 4 Mar 2025
Viewed by 705
Abstract
In recent years, rainfall in the Jazan region of southwest Saudi Arabia has significantly increased, setting new records for monthly and daily rainfall in 2024 and leading to natural disasters. The distribution of monthly rainfall in Jazan and its variations over recent decades [...] Read more.
In recent years, rainfall in the Jazan region of southwest Saudi Arabia has significantly increased, setting new records for monthly and daily rainfall in 2024 and leading to natural disasters. The distribution of monthly rainfall in Jazan and its variations over recent decades have not been analyzed yet. This study examines the changes in seasonal rainfall patterns in the Jazan region utilizing observational and reanalysis datasets from 1978 to 2024. The rescaled adjusted partial sums technique is used to detect breaks in the rainfall time series, while statistical methods are applied to analyze rainfall extremes and their trends. The average annual rainfall for the period 1978–2024 is 149.4 mm, which has increased from 131.9 mm during the earlier decades (1978–2000) to 166.2 mm in recent decades (2001–2024), reflecting an increase of 34.3 mm. The annual rainfall has been increasing significantly at a rate of 92.9 mm/decade in recent decades, compared to 74.3 mm/decade in the previous decades. There has been a marked shift in the peak rainfall season from autumn to summer, in particular moving from October to August in recent decades. The highest monthly rainfall recorded in August, reached 54.9 mm in recent decades, compared to just 15.4 mm in earlier decades. In contrast, the peak rainfall in October was 19.9 mm in previous decades, which decreased to 18.7 mm in recent decades. Notably, August 2024 marked a record-breaking rainfall of 414.8 mm, surpassing the previous high of 157.5 mm set in October 1997. These data show clear evidence of the changing climate in the region. Moreover, the number of heavy rainfall days has risen, with a total of 608 wet days documented throughout the entire period, alongside a significant increase in light, heavy, and extremely heavy rainfall days in recent decades compared to earlier ones. Hence, the region has seen a rise in heavy to extremely heavy rainfall days, including a daily record of 113.7 mm on 23 August 2024, compared to 90.0 mm on 22 October 1997. Additionally, there has been a rise in the maximum consecutive 5-day rainfall compared to the maximum 1-day rainfall. Overall, these findings show substantial changes in rainfall patterns in the Jazan region, suggesting notable climatic shifts that warrant further investigation using the automatic weather stations, radar and satellite data, as well as climate model simulations. Full article
(This article belongs to the Section Climatology)
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19 pages, 8506 KiB  
Article
Rapid Intensification of Typhoon Rammasun (2014) with Strong Vertical Wind Shear
by Weiyu Lu and X. San Liang
Atmosphere 2025, 16(3), 297; https://doi.org/10.3390/atmos16030297 - 2 Mar 2025
Viewed by 484
Abstract
From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study [...] Read more.
From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study investigates the counterintuive phenomenon, using the multiscale window transform (MWT) and the theory of canonical transfer. For the first time, the diagnostic results show that the strong VWS provided additional available potential energy (APE) to the mid-to-upper troposphere through baroclinic instability. This APE was converted into kinetic energy (KE) via buoyancy conversion and transported to the lower troposphere by pressure gradient, increasing the lower-troposphere wind speed. The strong VWS facilitated the RI in two main ways. First, it was via baroclinic instability. Strong VWS facilitated the transfer of APE from the background flow window to the typhoon scale window, supplying additional APE to the mid-to-upper troposphere, hence enhancing the warm-core structure. Second, the VWS direction shifted from an east-west orientation to a north-south orientation. This directional change put the typhoon’s vertical alignment from a westward tilt back to a straighter one. This effectively suppressed the destructive effects of the asymmetric circulation, and promoted the conversion of APE into KE via buoyancy conversion, hence contributed to the RI. Full article
(This article belongs to the Section Meteorology)
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15 pages, 15327 KiB  
Technical Note
Establishment and Operation of an Early Warning Service for Agrometeorological Disasters Customized for Farmers and Extension Workers at Metropolitan-Scale
by Yong-Soon Shin, Hee-Ae Lee, Sang-Hyun Park, Yong-Kyu Han, Kyo-Moon Shim and Se-Jin Han
Atmosphere 2025, 16(3), 291; https://doi.org/10.3390/atmos16030291 - 28 Feb 2025
Viewed by 342
Abstract
A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For [...] Read more.
A farm-specific early warning system has been developed to mitigate agricultural damage caused by climate change. This system utilizes weather data at the farm level to predict crop growth, forecast weather disaster risks, and provide risk alerts to farmers and local governments. For effective implementation, local governments must lead operating early warning services that reflect regional agricultural characteristics and farmers’ needs, while the central government provides foundational data. The system connects data from each region to the cloud, enabling the establishment of a nationwide integrated service operation framework that includes the central government, metropolitan cities, municipalities, and farmers. Full article
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15 pages, 5421 KiB  
Article
Indoor Radon Monitoring in Residential Areas in the Vicinity of Coal Mining Operations in the Mpumalanga Province, South Africa
by Paballo M. Moshupya, Seeke C. Mohuba, Tamiru A. Abiye, Ian Korir and Sifiso Nhleko
Atmosphere 2025, 16(3), 290; https://doi.org/10.3390/atmos16030290 - 28 Feb 2025
Viewed by 443
Abstract
Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and [...] Read more.
Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and for heating, predominantly in informal settlements and township communities. Most of the existing coal-fired power plants are found in the Mpumalanga province. This paper presents long-term radon (222Rn) measurements in dwellings surrounding coal mining centres in the Mpumalanga province and evaluates their contributions to indoor radon exposures. The indoor radon measurements were conducted using solid-state nuclear track detectors and were performed during warm and cold seasons. It was found that the overall indoor radon activity concentrations ranged between 21 Bq/m3 and 145 Bq/m3, with a mean value of 40 Bq/m3. In all the measured dwellings, the levels were below the WHO reference level of 100 Bq/m3 and 300 Bq/m3 reference level recommended by the IAEA and ICRP, with the exception of one dwelling that was poorly ventilated. The results reveal that individuals residing in the surveyed homes are not exposed to radon levels higher than the WHO, ICRP, and IAEA reference levels. The main source influencing indoor radon activity concentrations was found to be primarily the concentration of uranium found in the geological formations in the area, with ventilation being an additional contributing factor of radon levels in dwellings. To maintain good air quality in homes, it is recommended that household occupants should keep their dwellings well ventilated to keep indoor radon levels as low as possible. Full article
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17 pages, 3397 KiB  
Article
A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism
by Bixiong Luo, Peng Zuo, Lijun Zhu and Wei Hua
Atmosphere 2025, 16(3), 266; https://doi.org/10.3390/atmos16030266 - 25 Feb 2025
Viewed by 252
Abstract
Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid [...] Read more.
Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. This paper proposes a novel WPD forecasting model based on RF-DBO-VMD feature selection and BiGRU optimized by an attention mechanism. The proposed model consists of three main stages. First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. The experimental results demonstrate the model’s high predictive accuracy, evidenced by an R2 value of 0.9754. Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. These results highlight the model’s potential to provide effective forecasting insights for future applications, such as energy trading and power system management, which will be further explored in real-world scenarios. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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17 pages, 6128 KiB  
Article
Spatiotemporal Characteristics of Mesoscale Convective Systems in the Yangtze River Delta Urban Agglomeration and Their Response to Urbanization
by Xinguan Du, Tianwen Sun and Kyaw Than Oo
Atmosphere 2025, 16(3), 245; https://doi.org/10.3390/atmos16030245 - 21 Feb 2025
Viewed by 389
Abstract
Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of [...] Read more.
Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of MCSs in the Yangtze River Delta (YRD) urban agglomeration and assess their response to urbanization. Our analysis reveals significant spatial and temporal differences in MCS activities during the warm season (April to September), including initiation, movement, and lifespan, with notable trends observed over the study period. MCSs are found to contribute substantially to hourly extreme precipitation, accounting for approximately 60%, which exceeds their contribution to total precipitation. Furthermore, the role of MCSs in extreme precipitation has also increased, driven by the intensification of MCS-induced extreme rainfall. Additionally, MCS characteristics exhibit significant regional differences. Urban areas experience more pronounced changes in MCS activity and precipitation compared to the surrounding rural regions. Specifically, urbanization contributes approximately 16% to MCS-related precipitation and 19% to MCS initiation, highlighting its substantial role in enhancing these processes. Moreover, mountainous areas and water bodies surrounding cities show stronger trends in certain MCS characteristics than urban and rural plains. This may be attributed to climatological conditions that favor MCS activity in these regions, as well as the complex interactions between urbanization, topography, and land–sea contrasts. These complicated dynamics warrant further investigation to better understand their implications. Full article
(This article belongs to the Section Meteorology)
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15 pages, 7070 KiB  
Article
Assessment of Fire Dynamics in the Amazon Basin Through Satellite Data
by Humberto Alves Barbosa, Catarina Oliveira Buriti and Tumuluru Venkata Lakshmi Kumar
Atmosphere 2025, 16(2), 228; https://doi.org/10.3390/atmos16020228 - 18 Feb 2025
Cited by 1 | Viewed by 861
Abstract
The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse [...] Read more.
The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO2) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO2 emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r2 = 0.30) due to the complex dynamics of plant moisture and associated CO and NO2 emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r2~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article
(This article belongs to the Section Air Quality)
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36 pages, 9488 KiB  
Article
New Challenges for Tropical Cyclone Track and Intensity Forecasting in Unfavorable External Environment in Western North Pacific. Part I. Formations South of 20° N
by Russell L. Elsberry, Hsiao-Chung Tsai, Wen-Hsin Huang and Timothy P. Marchok
Atmosphere 2025, 16(2), 226; https://doi.org/10.3390/atmos16020226 - 18 Feb 2025
Viewed by 550
Abstract
A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As [...] Read more.
A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article
(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))
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30 pages, 32662 KiB  
Article
Air Pollution Trends and Predictive Modeling for Three Cities with Different Characteristics Using Sentinel-5 Satellite Data and Deep Learning
by Salma Alkayal, Hind Almisbahi, Souad Baowidan and Entisar Alkayal
Atmosphere 2025, 16(2), 211; https://doi.org/10.3390/atmos16020211 - 13 Feb 2025
Viewed by 782
Abstract
Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with [...] Read more.
Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with high industrial activity, a city with a high population density and urbanization, and an agricultural city. The air pollution data were collected using the Sentinel-5P satellite and Google Earth Engine to apply descriptive analysis and comparison of two years, 2022 and 2023. The studied cities were Al Riyadh (high population), Al Jubail (industrial), and Najran (agricultural) in Saudi Arabia. The selected pollutants were SO2, NO2, CO, O3, and HCHO. In addition, this study investigated the variations observed in all the pollutants during the months of the year, the correlations between the contaminants, and the correlation between NO2 and the meteorological data. Based on our findings, Al Jubail had the highest level of all the pollutants during the two years, except for NO2, for which the highest level was observed in Al Riyadh, which has witnessed notable urbanization and development recently. Moreover, this study developed a forecasting model for the concentration of NO2 based on weather data and the previous values of NO2 using Long Short-Term Memory (LSTM) and Time2Vec. The modeling proved that any model that is trained on data collected from a specific city is not suitable for predicting the pollution level in another city and the level of another pollutant, as the three cities have different correlations with the pollutants and the weather data. The proposed model demonstrated a superior accuracy in predicting NO2 concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors, which contributes to ongoing efforts to understand the dynamics of air pollution based on cities’ characteristics and the period of the year. Full article
(This article belongs to the Special Issue Dispersion and Mitigation of Atmospheric Pollutants)
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22 pages, 11030 KiB  
Article
Adjusting Soil Temperatures with a Physics-Informed Deep Learning Model for a High-Resolution Numerical Weather Prediction System
by Qiufan Wang, Yubao Liu, Yueqin Shi and Shaofeng Hua
Atmosphere 2025, 16(2), 207; https://doi.org/10.3390/atmos16020207 - 12 Feb 2025
Viewed by 623
Abstract
Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to [...] Read more.
Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article
(This article belongs to the Section Meteorology)
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19 pages, 4707 KiB  
Article
The Impact of Lightning Rods on the Differential Reflectivity of X-Band Radar
by Hui Wang, Haifeng Yu, Hao Wen and Zhifeng Shu
Atmosphere 2025, 16(2), 204; https://doi.org/10.3390/atmos16020204 - 11 Feb 2025
Viewed by 420
Abstract
Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on [...] Read more.
Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on radar data quality have become particularly important. In this study, lightning rods of two different sizes were configured on Beijing’s Fangshan X-band radar to perform antenna far-field tests and precipitation process comparative observation tests, and to conduct a quantitative impact assessment of the antenna electrical performance parameters and radar differential reflectivity. First, far-field tests were conducted on the impact of small- and original-diameter lightning rods on the Fangshan X-band radar. The results showed that the horizontal polarisation beam width was reduced by 0.081 and 0.08°, while the vertical polarisation beam width was reduced by 0.02 and 0.11°, respectively. Second, light rain or snowfall with a signal-to-noise ratio greater than 15 dB, and a correlation coefficient greater than 0.985, were selected for comparative observation. When other environmental influences could not be isolated, the original lightning rod showed a maximum ZDR value of 1.32 dB and a maximum azimuth span of 35°. The maximum ZDR value of the small-diameter lightning rod was 0.18 dB and the maximum azimuth span was 20°; however, its deviation from the theoretical maximum value is only 0.05 dB. Therefore, once the system configuration is determined, the design of an appropriate lightning rod scheme can effectively improve radar data quality. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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29 pages, 3568 KiB  
Article
Threshold Effects of the Interaction Between Urban Development and Atmospheric Pollution
by Xiaoling Yuan, Hanyu Geng and Zhaopeng Li
Atmosphere 2025, 16(2), 201; https://doi.org/10.3390/atmos16020201 - 10 Feb 2025
Viewed by 617
Abstract
Over the past 70 years since the founding of the People’s Republic of China, urban development has achieved remarkable progress but also encountered severe atmospheric pollution, which has become a significant obstacle to high-quality urban development. Understanding the interaction mechanisms between urban development [...] Read more.
Over the past 70 years since the founding of the People’s Republic of China, urban development has achieved remarkable progress but also encountered severe atmospheric pollution, which has become a significant obstacle to high-quality urban development. Understanding the interaction mechanisms between urban development and atmospheric pollution is thus crucial for promoting sustainable urban construction. This paper explores these mechanisms by analyzing the interplay between urban population, industry, space, social development, and pollution through a theoretical framework. Using a simultaneous equations model and the Three-Stage Least Squares (3SLS) method, it examines these relationships and further investigates threshold effects. The findings reveal a nonlinear relationship with significant thresholds: (1) High levels of PM2.5, population size, and industrial agglomeration can shift from exacerbating pollution to enabling governance, though excessive thresholds reverse this trend. (2) PM2.5 mediates the impact of spatial sprawl, environmental regulation, and population dynamics, oscillating between governance and pollution effects. (3) Industrial agglomeration and spatial sprawl show variable impacts on pollution mitigation depending on pollution intensity and urban thresholds. These findings provide critical insights into the intricate dynamics between urban development and atmospheric pollution, emphasizing the importance of adopting differentiated strategies based on specific urban thresholds. Ultimately, this research contributes to the broader goal of harmonizing economic growth, social development, and environmental sustainability in urban areas, serving as a valuable reference for cities worldwide facing similar challenges. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
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18 pages, 5015 KiB  
Article
Dissipation Scaling with a Variable Cϵ Coefficient in the Stable Atmospheric Boundary Layer
by Marta Wacławczyk, Jackson Nzotungishaka, Paweł Jędrejko, Joydeep Sarkar and Szymon P. Malinowski
Atmosphere 2025, 16(2), 188; https://doi.org/10.3390/atmos16020188 - 7 Feb 2025
Viewed by 484
Abstract
This work concerns the Taylor formula for the turbulence kinetic energy dissipation rate in the stable atmospheric boundary layer. The formula relates the turbulence kinetic energy dissipation rate to statistics at large scales, namely, the turbulence kinetic energy and the integral length scale. [...] Read more.
This work concerns the Taylor formula for the turbulence kinetic energy dissipation rate in the stable atmospheric boundary layer. The formula relates the turbulence kinetic energy dissipation rate to statistics at large scales, namely, the turbulence kinetic energy and the integral length scale. In parameterization schemes for atmospheric turbulence, it is usually assumed that the dissipation coefficient Cϵ in the Taylor formula is constant. However, a series of recent theoretical works and laboratory experiments showed that Cϵ depends on the local Reynolds number. We calculate turbulence statistics, including the dissipation rate, the standard deviation of fluctuating velocities and integral length scales, using observational data from the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition. We show that the dissipation coefficient Cϵ varies considerably and is a function of the Reynolds number, however, the functional form of this dependency in the stably stratified atmospheric boundary layer is different than in previous studies. Full article
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20 pages, 5287 KiB  
Article
Research on NOx Emissions Testing and Optimization Strategies for Diesel Engines Under Low-Load Cycles
by Fengbin Wang, Jianfu Zhao, Tengteng Li, Peng Guan, Shuangxi Liu, Haiqiao Wei and Lei Zhou
Atmosphere 2025, 16(2), 190; https://doi.org/10.3390/atmos16020190 - 7 Feb 2025
Viewed by 660
Abstract
Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was [...] Read more.
Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was studied based on a dual selective catalytic reduction (SCR) after-treatment system. The results indicate that emissions at load rates below 30% during LLCs account for more than 67.5% of the total cycle emissions, particularly under idling and start-stop conditions. Moreover, it was found that NOx emissions decrease significantly by using a pre-positioned dual SCR after-treatment system. And, the closer the SCR is to the engine, the higher the NOx conversion efficiency becomes. As the SCR’s position is adjusted, the rate of NOx removal stabilizes, achieving a maximum reduction in NOx concentration of up to 60.1%. Full article
(This article belongs to the Section Air Pollution Control)
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16 pages, 2878 KiB  
Article
Exploring the Holiday Effect on Elevated Traffic-Related Air Pollution with Hyperlocal Measurements in Chengdu, China
by Sheng Xiang, Jiaojiao Yu, Yu Ting Yu, Pengbo Zhao, Tie Zheng, Jingsong Yue, Yuanyuan Yang and Haobing Liu
Atmosphere 2025, 16(2), 171; https://doi.org/10.3390/atmos16020171 - 2 Feb 2025
Viewed by 941
Abstract
Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in [...] Read more.
Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 105 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NOx) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NOx emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies. Full article
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23 pages, 11007 KiB  
Article
Research on the Detection Model of Kernel Anomalies in Ionospheric Space Electric Fields
by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Yumeng Huo, Junjie Song and Bo Hao
Atmosphere 2025, 16(2), 160; https://doi.org/10.3390/atmos16020160 - 31 Jan 2025
Viewed by 583
Abstract
Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and [...] Read more.
Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and U-net networks. The model embeds the KAN-Conv convolutional module based on KANs in the encoder section, introduces the feature pyramid attention module (FPA) at the junction of the encoder and decoder, and introduces the CBAM attention mechanism module in the decoder section. The experimental results show that the improved KANs-Unet model has a mIoU improvement of about 10% compared to the PSPNet algorithm and an improvement of about 7.8% compared to the PAN algorithm. It has better detection performance than the currently popular semantic segmentation algorithms. A higher evaluation index represents that the detected abnormal area is closer to the label value (i.e., the detected abnormal area is more complete), indicating better detection performance. To further investigate the characteristics of kernel anomaly areas and the differences in features during magnetic storms, the author studied the characteristics of kernel anomaly areas during two different intensities of magnetic storms: from November 2021 to October 2022 and from 1 May 2024 to 13 May 2024 (large magnetic storm), and from 11 October 2023 to 23 October 2023 (moderate magnetic storm). During a major geomagnetic storm, the overall distribution of kernel anomaly areas shows a parallel trend with a band-like distribution. The spatial distribution of magnetic latitudes is relatively scattered, especially in the southern hemisphere, where the magnetic latitudes are wider. Additionally, the number of orbits with kernel anomaly areas during ascending increases, especially during peak periods of major geomagnetic storms. The overall spatial distribution of moderate geomagnetic storms does not change significantly, but the global magnetic latitude distribution is relatively concentrated. Full article
(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)
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18 pages, 2034 KiB  
Article
Comparison of Identified Ice Supersaturated Regions for Contrail Avoidance Using Three Standard Weather Forecast Databases
by Amy Tal Rose-Tejwani, Lance Sherry and Kayla Ebright
Atmosphere 2025, 16(2), 149; https://doi.org/10.3390/atmos16020149 - 29 Jan 2025
Viewed by 591
Abstract
Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to contrail formation in Ice Supersaturated Regions (ISSRs). Contrails are estimated to contribute approximately 2% to total anthropogenic global warming. Some researchers have developed [...] Read more.
Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to contrail formation in Ice Supersaturated Regions (ISSRs). Contrails are estimated to contribute approximately 2% to total anthropogenic global warming. Some researchers have developed simulation models to estimate the frequency, duration, and spatial distribution of contrails. Other researchers have identified issues with the accuracy of the data for predicting the timing and precise geographic positioning of ISSRs. This study presents a systematic review of 22 peer-reviewed articles that included detailed models of ISSR identification, identifying three atmospheric data sources, four parameters, and two equations for calculating the parameters derived. A further analysis revealed differences in the temperature and RHW readings across the three databases, resulting in differences in the RHI calculations and the identification of ISSRs. Over an 18-month period in Sterling, Virginia, USA, the radiosonde data and two atmospheric forecast databases identified the ISSR conditions on 44%, 47%, and 77% of days, respectively. Broken down by a flight level between 30,000 and 39,999 feet in altitude, these differences are highlighted further. The forecast databases overestimated the presence of ISSRs compared to the radiosonde data. These findings underscore the variability inherent in atmospheric datasets and the conversion methods, highlighting potential areas for refinement in ISSR prediction, notably in the development of ensemble forecasts based on several atmospheric databases. The implications of these results, the limitations of this study, and future work are discussed. Full article
(This article belongs to the Special Issue Advance in Transportation Meteorology (2nd Edition))
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73 pages, 10996 KiB  
Review
The Fluid Ionosphere
by Massimo Materassi
Atmosphere 2025, 16(2), 147; https://doi.org/10.3390/atmos16020147 - 29 Jan 2025
Viewed by 671
Abstract
In this review paper, the equations of motion describing the fluid dynamics of the ionosphere are constructed step by step, so that any master or post-graduate student may get familiar with the general theory of the “traditional” approach to Ionospheric Physics, in which [...] Read more.
In this review paper, the equations of motion describing the fluid dynamics of the ionosphere are constructed step by step, so that any master or post-graduate student may get familiar with the general theory of the “traditional” approach to Ionospheric Physics, in which chemicals forming the Earth’s upper atmosphere are represented as fluids in mutual interaction. The hypotheses on which the smooth-field fluid representation is based are discussed in terms of microscopic dynamics of the gas particles; this discussion is oriented to prepare the reader for the post-fluid approaches to the physics of turbulence. The fluid-dynamical picture of the ionosphere is the most classical and conceptually simple one, and this makes it extremely widespread in terms of theoretical models and applications. The advantages and achievements of this theory are highlighted, its limits are discussed, and perspectives to go beyond it sketched. Full article
(This article belongs to the Section Upper Atmosphere)
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16 pages, 4222 KiB  
Article
Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change
by Mahin Razi Ghalavand, Manuchehr Farajzadeh and Yousef Ghavidel Rahimi
Atmosphere 2025, 16(2), 136; https://doi.org/10.3390/atmos16020136 - 27 Jan 2025
Viewed by 769
Abstract
Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We [...] Read more.
Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We applied stationary and non-stationary Generalized Extreme Value (GEV) models within a Bayesian framework to estimate return levels for rainfall extremes, along with 90% confidence intervals. Our findings indicate that non-stationary models are not prominently evident based on AIC at most stations; however, non-stationary Generalized Extreme Value (GEV) models outperform stationary models based on RMSE and NSE evaluation criteria that sufficiently capture variations in extremes. Furthermore, most observed changes in extreme events exhibit a non-stationary pattern. Non-stationary analysis indicates that the frequency and severity of rainfall extremes have shown both increasing and decreasing trends, characterized by inconsistent spatial patterns. Full article
(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)
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12 pages, 4383 KiB  
Article
Decadal Regime Shifts in Sea Fog Frequency over the Northwestern Pacific: The Influence of the Pacific Decadal Oscillation and Sea Surface Temperature Warming
by Shihan Zhang, Liguo Han, Jingchao Long, Lingyu Dong, Pengzhi Hong and Feng Xu
Atmosphere 2025, 16(2), 130; https://doi.org/10.3390/atmos16020130 - 26 Jan 2025
Viewed by 489
Abstract
Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving [...] Read more.
Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving these changes. The results show that SFF experienced a significant and sudden decadal increase around 1978 (up by 12.9%) and a prominent decadal decrease around 1999 (down by 7.8%). The sudden increase in SFF around 1978 was closely related to the PDO. A positive PDO phase induced unusual anticyclonic circulation and southerly winds over the northwestern Pacific, enhancing low-level atmospheric stability and moisture supply, thus facilitating sea fog formation. Nevertheless, the decrease in SFF around 1999 was related to SST warming in the north Pacific. The rise in sea temperatures weakened the SST front south of the foggy region, reducing the cooling and condensation of warm air necessary for sea fog formation. This study enhances the understanding of the decadal variability mechanism of SFF over the northwestern Pacific regulated by large-scale circulation systems and provides a reference for future sea fog forecasting work. Full article
(This article belongs to the Section Meteorology)
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16 pages, 4518 KiB  
Article
Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm
by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang and Lingbin Kong
Atmosphere 2025, 16(2), 114; https://doi.org/10.3390/atmos16020114 - 21 Jan 2025
Viewed by 854
Abstract
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the [...] Read more.
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m−3, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m−3, 7 μg·m−3, 8 μg·m−3, and 15 μg·m−3 for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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21 pages, 16278 KiB  
Article
Synoptic and Mesoscale Atmospheric Patterns That Triggered the Natural Disasters in the Metropolitan Region of Belo Horizonte, Brazil, in January 2020
by Thaís Aparecida Cortez Pinto, Enrique Vieira Mattos, Michelle Simões Reboita, Diego Oliveira de Souza, Paula S. S. Oda, Fabrina Bolzan Martins, Thiago Souza Biscaro and Glauber Willian de Souza Ferreira
Atmosphere 2025, 16(1), 102; https://doi.org/10.3390/atmos16010102 - 18 Jan 2025
Viewed by 716
Abstract
Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic [...] Read more.
Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day−1 and 48 mm h−1, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters. Full article
(This article belongs to the Special Issue Prediction and Modeling of Extreme Weather Events)
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56 pages, 48151 KiB  
Article
Excitation of ULF, ELF, and VLF Resonator and Waveguide Oscillations in the Earth–Atmosphere–Ionosphere System by Lightning Current Sources Connected with Hunga Tonga Volcano Eruption
by Yuriy G. Rapoport, Volodymyr V. Grimalsky, Andrzej Krankowski, Asen Grytsai, Sergei S. Petrishchevskii, Leszek Błaszkiewicz and Chieh-Hung Chen
Atmosphere 2025, 16(1), 97; https://doi.org/10.3390/atmos16010097 - 16 Jan 2025
Viewed by 797
Abstract
The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, [...] Read more.
The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, including ultra low frequency (ULF), extremely low frequency (ELF), and very low frequency (VLF) ranges. The electric current source due to lightning near the location of the Hunga Tonga volcano eruption has a wide-band frequency spectrum determined in this paper based on a data-driven approach. The spectrum is monotonous in the VLF range but has many significant details at the lower frequencies (ULF, ELF). The decreasing amplitude tendency is maintained at frequencies exceeding 0.1 Hz. The density of effective lightning current in the ULF range reaches the value of the order of 10−7 A/m2. A combined dynamic/quasi-stationary method has been developed to simulate ULF penetration through the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system. This method is suitable for the ULF range down to 10−4 Hz. The electromagnetic field is determined from the dynamics in the ionosphere and from a quasi-stationary approach in the atmosphere, considering not only the electric component but also the magnetic one. An analytical/numerical method has been developed to investigate the excitation of the global Schumann resonator and the eigenmodes of the coupled Schumann and ionospheric Alfvén resonators in the ELF range and the eigenmodes of the Earth–ionosphere waveguide in the VLF range. A complex dispersion equation for the corresponding disturbances is derived. It is shown that oscillations at the first resonance frequency in the Schumann resonator can simultaneously cause noticeable excitation of the local ionospheric Alfvén resonator, whose parameters depend on the angle between the geomagnetic field and the vertical direction. VLF propagation is possible over distances of 3000–10,000 km in the waveguide Earth–ionosphere. The results of simulations are compared with the published experimental data. Full article
(This article belongs to the Special Issue Feature Papers in Upper Atmosphere (2nd Edition))
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18 pages, 7881 KiB  
Article
Effect of Multiple Injection Strategy Under High Ammonia Ratio on Combustion and Emissions of Liquid Ammonia/Diesel Dual DI Engine
by Zhenbin Chen, Yudong Wan, Omar I. Awad and Zhiqiang Pan
Atmosphere 2025, 16(1), 94; https://doi.org/10.3390/atmos16010094 - 16 Jan 2025
Viewed by 750
Abstract
With the increasingly prominent environmental and energy issues, emission regulations are becoming more stringent. Ammonia diesel dual fuel (ADDF) engine is one of the effective ways to reduce carbon emissions. This study investigated the effect of multiple injection strategy on the combustion and [...] Read more.
With the increasingly prominent environmental and energy issues, emission regulations are becoming more stringent. Ammonia diesel dual fuel (ADDF) engine is one of the effective ways to reduce carbon emissions. This study investigated the effect of multiple injection strategy on the combustion and emission characteristics of liquid ammonia/diesel dual direct injection (DI) engines through numerical simulation. The results showed that under the condition of maintaining the same pre injection diesel fuel and high ammonia energy ratio (80%), with the introduction of multiple injection, the peak cylinder pressure decreased and the peak phase advanced, the combustion start angle (CA10) advanced, the heat release showed a multi-stage pattern. The times of injection (TSOI) has a significant effect on combustion and emissions. As TSOI increased, ignition delay decreased, the combustion duration is shortened, and the combustion is accelerated. Notably, overall emissions of NOx and N2O have decreased, but the emissions of unburned NH3 have increased. Optimized the state of ammonia injection (SOAI) timing and ammonia injection pressure (AIP), showed that advancing SOAI timing and increasing AIP improved combustion. Advanced the SOAI timing to −8 °CA ATDC, resulted in a significant NOx emissions decrease with an increase in TSOI, reaching over 50%. Although increasing injection pressure can improve combustion, it also results in higher N2O emissions. Full article
(This article belongs to the Special Issue Renewable Strategies for Emission Reduction: A Multisectoral Approach)
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21 pages, 5166 KiB  
Article
Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses
by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis and Yasuhide Hobara
Atmosphere 2025, 16(1), 88; https://doi.org/10.3390/atmos16010088 - 15 Jan 2025
Viewed by 734
Abstract
The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological [...] Read more.
The purpose of this paper is to discuss the effect of earthquake (EQ) preparation on changes in meteorological parameters. The two physical quantities of temperature (T)/relative humidity (Hum) and atmospheric chemical potential (ACP) have been investigated with the use of the Japanese meteorological “open” data of AMeDAS (Automated Meteorological Data Acquisition System), which is a very dense “ground-based” network of meteorological stations with higher temporal and spatial resolutions than the satellite remote sensing open data. In order to obtain a clearer identification of any seismogenic effect, we have used the AMeDAS station data at local midnight (LT = 01 h) and our initial target EQ was chosen to be the famous 1995 Kobe EQ of 17 January 1995 (M = 7.3). Initially, we performed conventional statistical analysis with confidence bounds and it was found that the Kobe station (very close to the EQ epicenter) exhibited conspicuous anomalies in both physical parameters on 10 January 1995, just one week before the EQ, exceeding m (mean) + 3σ (standard deviation) in T/Hum and well above m + 2σ in ACP within the short-term window of one month before and two weeks after an EQ. When looking at the whole period of over one year including the day of the EQ, in the case of T/Hum only we detected three additional extreme anomalies, except in winter, but with unknown origins. On the other hand, the anomalous peak on 10 January 1995 was the largest for ACP. Further, the spatial distributions of the anomaly intensity of the two quantities have been presented using about 40 stations to provide a further support to the close relationship of this peak with the EQ. The above statistical analysis has been compared with an analysis with recent machine/deep learning methods. We have utilized a combinational use of NARX (Nonlinear Autoregressive model with eXogenous inputs) and Long Short-Term Memory (LSTM) models, which was successful in objectively re-confirming the anomalies in both parameters on the same day prior to the EQ. The combination of these analysis results elucidates that the meteorological anomalies on 10 January 1995 are considered to be a notable precursor to the EQ. Finally, we suggest a joint examination of our two meteorological quantities for their potential use in real short-term EQ prediction, as well as in the future lithosphere–atmosphere–ionosphere coupling (LAIC) studies as the information from the bottom part of LAIC. Full article
(This article belongs to the Section Meteorology)
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34 pages, 773 KiB  
Review
Machine Learning Methods for Weather Forecasting: A Survey
by Huijun Zhang, Yaxin Liu, Chongyu Zhang and Ningyun Li
Atmosphere 2025, 16(1), 82; https://doi.org/10.3390/atmos16010082 - 14 Jan 2025
Cited by 3 | Viewed by 7468
Abstract
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning [...] Read more.
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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19 pages, 1296 KiB  
Article
MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting
by Shaohan Li, Min Chen, Lu Yi, Qifeng Lu and Hao Yang
Atmosphere 2025, 16(1), 67; https://doi.org/10.3390/atmos16010067 - 9 Jan 2025
Viewed by 541
Abstract
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as [...] Read more.
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)
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31 pages, 3113 KiB  
Article
Automatic Threshold Selection for Generalized Pareto and Pareto–Poisson Distributions in Rainfall Analysis: A Case Study Using the NOAA NCDC Daily Rainfall Database
by Roberto Mínguez
Atmosphere 2025, 16(1), 61; https://doi.org/10.3390/atmos16010061 - 8 Jan 2025
Viewed by 875
Abstract
Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or [...] Read more.
Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or Pareto–Poisson distributions for extreme-value analysis. Recent analyses of threshold selection methods proposed in the technical literature, particularly when applied to rainfall records with high quantization levels, have shown that nonparametric methods are often unreliable. Additionally, methods relying on the asymptotic properties of the GP distribution tend to produce unrealistically high threshold estimates. In contrast, graphical methods and goodness-of-fit (GoF) metrics that account for the pre-asymptotic behavior of the GP distribution have demonstrated better performance. Despite these improvements, there remains no automatic and statistically robust methodology for threshold selection. This study develops an automatic, statistically sound procedure for optimal threshold selection, leveraging weighted mean square errors and internally studentized residuals. The proposed method outperforms existing approaches in terms of accuracy, as demonstrated through numerical experiments and its application to real-world data from the NOAA NCDC Daily Rainfall Database. Results indicate that the method not only improves threshold estimation precision but also enhances the reliability of extreme-value analysis for precipitation records, making it a valuable tool for hydrological applications. The findings emphasize the practical implications of the method for analyzing extreme rainfall events and its potential for broader climatological studies. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))
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20 pages, 3405 KiB  
Article
ICOS Potenza (Italy) Atmospheric Station: A New Spot for the Observation of Greenhouse Gases in the Mediterranean Basin
by Emilio Lapenna, Antonella Buono, Alessandro Mauceri, Isabella Zaccardo, Francesco Cardellicchio, Francesco D’Amico, Teresa Laurita, Davide Amodio, Canio Colangelo, Gianluca Di Fiore, Antonella Gorga, Ermann Ripepi, Francesco De Benedictis, Silvana Pirelli, Liborio Capozzo, Vincenzo Lapenna, Gelsomina Pappalardo, Serena Trippetta and Lucia Mona
Atmosphere 2025, 16(1), 57; https://doi.org/10.3390/atmos16010057 - 8 Jan 2025
Cited by 3 | Viewed by 1338
Abstract
The Integrated Carbon Observation System (ICOS) is the reference Research Infrastructure (RI) for the observation of greenhouse gases (GHGs) across Europe, providing standardised, long-term and high-precision measurements of the most relevant species (CO2, CH4, CO, etc.). The ICOS Atmosphere [...] Read more.
The Integrated Carbon Observation System (ICOS) is the reference Research Infrastructure (RI) for the observation of greenhouse gases (GHGs) across Europe, providing standardised, long-term and high-precision measurements of the most relevant species (CO2, CH4, CO, etc.). The ICOS Atmosphere network currently extends throughout the continent, although the density of stations in the Mediterranean area is still low compared to Central and Northern Europe. In this context, the recently implemented class 1 continental station near Potenza in Basilicata, Italy—station code: POT—represents an important step forward in the extension of the ICOS atmosphere domain across the South, reducing the large spatial gaps existing between ICOS sites within the Mediterranean basin. Herein, we provide a description of the new ICOS POT station and the site where it operates, focusing mostly on the technical setup of the sampling system which plays a key role in GHG measurements. With a strong technical connotation, the present paper aims to be beneficial for the ICOS atmosphere community and those stations that intend to join the network in the future, providing an accurate description of the station at the level of single components. Moreover, a brief overview of the peculiarities of the site and the scientific perspectives to be pursued, together with very preliminary data collected at the new ICOS station, are presented. Preliminary data collected during a short campaign are compared with STILT (Stochastic Time-Inverted Lagrangian Transport) model results as a first test of the measurements and to provide a first insight of the specific Potenza situation in terms of GHG concentrations. Full article
(This article belongs to the Section Air Quality)
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22 pages, 10003 KiB  
Article
Spatial Downscaling of Daily Temperature Minima Using Machine Learning Methods and Application to Frost Forecasting in Two Alpine Valleys
by Sudheer Bhakare, Michael Matiu, Alice Crespi and Dino Zardi
Atmosphere 2025, 16(1), 38; https://doi.org/10.3390/atmos16010038 - 1 Jan 2025
Viewed by 1082
Abstract
This study examines the performance of three machine learning models—namely, Artificial Neural Network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN)—for spatial downscaling of seasonal forecasts of daily minimum temperature from 12 km to 250 m horizontal resolution. Downscaling is carried out [...] Read more.
This study examines the performance of three machine learning models—namely, Artificial Neural Network (ANN), Random Forest (RF), and Convolutional Neural Network (CNN)—for spatial downscaling of seasonal forecasts of daily minimum temperature from 12 km to 250 m horizontal resolution. Downscaling is carried out with a one-month lead time, with analysis split into short-term (1 to 8 days) and extended (9 to 28 days) forecast periods, allowing a detailed assessment of the performance of models over time. Results suggest that CNN outperforms ANN and RF, achieving lower Root Mean Square Error (ranging from 2.04 °C to 2.66 °C) and Mean Absolute Error (1.59 °C to 2.03 °C) along with higher correlation (0.75 to 0.88) and reduced bias (−0.38 °C to −0.68) across all seasons, for the short term. The CNN model also exhibits superior performance in frost prediction, with the highest F1 score (0.78) and lowest False Discovery Rate (0.30) in predicting frost events, particularly in early spring for the short-term forecast period over 2010–2018. However, errors increase in transitional months, like April, and in the extended forecast period, confirming the intrinsic challenges inherent to predicting frost events in these months. Despite the decreased skills for extended forecast periods, results suggest that the CNN model’s effectiveness for spatial downscaling of minimum temperature and frost forecasting over complex terrain provides a valuable tool for frost risk management. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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24 pages, 5694 KiB  
Article
Investigating the Temporal and Spatial Characteristics of Lower Atmospheric Ducts in the Arctic via Long-Term Numerical Simulations
by Jinyue Wang, Xiaofeng Zhao, Jing Zou, Pinglv Yang, Bo Wang, Shuai Yang, Zhijin Qiu, Zhiqian Li, Tong Hu and Miaomiao Song
Atmosphere 2025, 16(1), 11; https://doi.org/10.3390/atmos16010011 - 26 Dec 2024
Viewed by 576
Abstract
In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The [...] Read more.
In this study, a diagnostic model for lower atmospheric ducts was developed using the polar weather research and forecasting model. A five-year simulation was then conducted across the entire Arctic region to investigate the temporal and spatial characteristics of lower atmospheric ducts. The model demonstrated excellent performance in simulating modified atmospheric refractivity, with root mean square errors ranging from 0 M to 5 M. The five-year simulation results revealed that duct occurrence rates across the Arctic region were all below 1% and exhibited a negative relationship with latitude. Regarding the difference between surface ducts and elevated ducts, a higher frequency of surface ducts was detected in the Arctic region. The height and thickness of surface ducts were generally lower than those of elevated ducts, but the strength of surface ducts was slightly greater. Regionally, surface ducts mainly occurred in the land areas surrounding the Arctic Ocean, while more elevated ducts were found in the North Atlantic Sea area. Additionally, a negative correlation was observed between the polar vortex indices and the characteristics of ducts, particularly for surface ducts. The ducts in Greenland were notably influenced by polar vortex activity, whereas the ducts in other regions, such as the Norwegian Sea and Kara Sea, were less affected. Full article
(This article belongs to the Special Issue Advances in Understanding Extreme Weather Events in the Anthropocene)
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12 pages, 3372 KiB  
Article
Lightning Current Distribution of the First and Subsequent Strokes Based on the Lightning Location System: Survey in Yunnan Power Grid
by Yutang Ma, Hongchun Shu, Changxin Xiao, Gaohui Yang, Chengwei Xie, Mengmeng Zhu and Pulin Cao
Atmosphere 2025, 16(1), 15; https://doi.org/10.3390/atmos16010015 - 26 Dec 2024
Viewed by 766
Abstract
Lightning is an electrical discharge phenomenon in the atmosphere caused by charge separation in clouds, which is divided into cloud-to-ground (CG) and cloud-to-cloud (CC) lightning. In recent years, research on the characteristics of multiple-stroke ground lightning and the amplitude of lightning currents has [...] Read more.
Lightning is an electrical discharge phenomenon in the atmosphere caused by charge separation in clouds, which is divided into cloud-to-ground (CG) and cloud-to-cloud (CC) lightning. In recent years, research on the characteristics of multiple-stroke ground lightning and the amplitude of lightning currents has attracted significant attention. The amplitude of lightning currents serves as fundamental data for lightning protection in power systems. Its accurate measurement is crucial for designing and safeguarding power systems. This paper obtains data from a lightning location system and analyzes the probability density distribution of lightning current amplitudes. It is found that the median of lightning currents gradually decreases with an increasing number of multiple strokes, and there is a trend in the change of lightning current steepness. As the number of strokes increases, the median value of amplitude distribution gradually decreases, while the steepness coefficient shows an increasing trend. These research findings contribute to a deeper understanding of the characteristics of lightning and provide important references for lightning prevention and disaster reduction. Full article
(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))
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13 pages, 3943 KiB  
Article
Investigating the Applicability of the Peak Density Thickness Parameter over the Equatorial Region
by Mohamed O. Shammat, Bodo W. Reinisch, Ivan Galkin, Philip J. Erickson, Jay A. Weitzen and William C. Rideout
Atmosphere 2025, 16(1), 10; https://doi.org/10.3390/atmos16010010 - 26 Dec 2024
Viewed by 525
Abstract
The Peak Density Thickness (PDT) refers to a vertical region in the ionosphere encompassing the F2 peak, where electron density is at its maximum, and extending upward—maintaining a constant density—for a fixed altitude beyond this peak. This study builds on the previously established [...] Read more.
The Peak Density Thickness (PDT) refers to a vertical region in the ionosphere encompassing the F2 peak, where electron density is at its maximum, and extending upward—maintaining a constant density—for a fixed altitude beyond this peak. This study builds on the previously established PDT concept, initially explored at midlatitudes using data from Millstone Hill, by evaluating its applicability and effectiveness over equatorial latitudes using data from the Jicamarca Incoherent Scatter Radar (ISR) in Lima, Peru. A comprehensive analysis of electron density profiles measured by the Jicamarca ISR, spanning 1997 to 2020, was conducted using the Madrigal database to extract the PDT parameter for the F2 layer. Findings from the Jicamarca ISR indicate that the PDT parameter peaks around solar noon, aligning with observations from Millstone Hill. For selected case studies, the Vary-Chap topside model was employed to reconstruct the ionospheric profile above the F2 peak and PDT, demonstrating the model’s enhanced effectiveness when incorporating the PDT parameter over equatorial regions. This research confirms the presence of PDT in equatorial regions, consistent with its behavior at midlatitudes, and underscores the importance of PDT in refining predictive ionospheric models across different latitudes. Full article
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16 pages, 6328 KiB  
Article
Gas Transport Arising from the Decomposition of Methane Hydrates in the Sediments of the Arctic Shelf to the Atmosphere: Numerical Modeling
by Mariia Trimonova, Nikolay Baryshnikov and Sergey Turuntaev
Atmosphere 2025, 16(1), 9; https://doi.org/10.3390/atmos16010009 - 26 Dec 2024
Viewed by 638
Abstract
This study investigates the transport of methane released from gas hydrate decomposition through sedimentary layers to quantify its flux into the atmosphere, a critical process given methane’s role as a major greenhouse gas. A novel methodology was developed to model two-phase, unsteady gas [...] Read more.
This study investigates the transport of methane released from gas hydrate decomposition through sedimentary layers to quantify its flux into the atmosphere, a critical process given methane’s role as a major greenhouse gas. A novel methodology was developed to model two-phase, unsteady gas flow in regions of hydrate decomposition, incorporating key factors such as relative permeability curves, capillary pressure, hydrostatics, and gas diffusion. Numerical simulations revealed that to achieve a gas front rise rate of 7 m/year, the gas accumulation rate must not exceed 10−8 kg/m3·s. At higher accumulation rates (10−6 kg/m3·s), gas diffusion has minimal impact on the saturation front movement, whereas at lower rates (10−8 kg/m3·s), diffusion significantly affects the front’s behavior. The study also established that the critical gas accumulation rate required to trigger sediment blowout in the hydrate decomposition zone is approximately 10−6 kg/m3·s, several orders of magnitude greater than typical bubble gas fluxes observed at the ocean surface. The proposed model improves the ability to predict the contribution of Arctic shelf methane hydrate decomposition to atmospheric methane concentrations. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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19 pages, 6206 KiB  
Article
Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios
by Jukyeong Choi and Heemun Chae
Atmosphere 2025, 16(1), 5; https://doi.org/10.3390/atmos16010005 - 25 Dec 2024
Viewed by 1412
Abstract
For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under [...] Read more.
For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p > 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires. Full article
(This article belongs to the Section Climatology)
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15 pages, 1622 KiB  
Article
Trends in the Occurrence of Compound Extremes of Temperature and Precipitation in Côte d’Ivoire
by Elisée Yapo Akobé, Adama Diawara, Fidèle Yoroba, Benjamin K. Kouassi, Assi Louis Martial Yapo, Ibrahima Diba, Kouakou Kouadio, Dro T. Tiémoko, Dianikoura Ibrahim Koné and Arona Diedhiou
Atmosphere 2025, 16(1), 3; https://doi.org/10.3390/atmos16010003 - 24 Dec 2024
Viewed by 636
Abstract
The aim of this study is to characterize the compound extremes of rainfall and temperature in Côte d’Ivoire. For this purpose, we analyzed the outputs of fourteen (14) climate models from the CORDEX-Africa project. Results show an increase (approximately 4.3 °C) in the [...] Read more.
The aim of this study is to characterize the compound extremes of rainfall and temperature in Côte d’Ivoire. For this purpose, we analyzed the outputs of fourteen (14) climate models from the CORDEX-Africa project. Results show an increase (approximately 4.3 °C) in the surface temperature and a decrease (5.90%) of the mean rainfall in the near (2036–2065) and far futures (2071–2100) over Côte d’Ivoire during the January–February–March (JFM) period. The analysis of the compound extremes of the wet/warm type highlights an increase in the frequency of this climatic hazard in the northern and central parts of the country during the January–March (JFM) season in the near and far futures. The dry/warm mode will increase in the central and southern parts of the country in the near future and in the whole country in the far future. These increases in compound extremes could lead to an increase in droughts and natural disasters across the country and could have a negative impact on socio-economic activities, such as transportation and agricultural production. This work could provide decision support for political decision-makers in formulating future public policies for managing agricultural production, food security, and natural disasters. Full article
(This article belongs to the Section Meteorology)
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