Journal Description
Atmosphere
Atmosphere
is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about the Atmosphere.
- Companion journal: Meteorology.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Lightning-Ignited Wildfires and Associated Meteorological Conditions in Western Siberia for 2016–2021
Atmosphere 2024, 15(1), 106; https://doi.org/10.3390/atmos15010106 - 15 Jan 2024
Abstract
The analysis of the spatio-temporal variability of lightning-ignited wildfires and meteorological conditions preceding their occurrence from both dry lightning and lightning with precipitation in Western Siberia for the warm seasons (May–September) of 2016–2021 was carried out. In the Arctic zone, fires from lightnings
[...] Read more.
The analysis of the spatio-temporal variability of lightning-ignited wildfires and meteorological conditions preceding their occurrence from both dry lightning and lightning with precipitation in Western Siberia for the warm seasons (May–September) of 2016–2021 was carried out. In the Arctic zone, fires from lightnings occur in most cases (83%) almost without precipitation (<2.5 mm/day), whereas in the forest and steppe zones the number of cases is less (81% and 74%, respectively). The most significant changes in meteorological conditions before the ignition were also revealed in the northern part 3–4 days before. Among all considered parameters, the most important role in the occurrence of dry lightning-ignited wildfires belongs to mid-tropospheric instability, lower-tropospheric dryness, and the moisture content of the top soil and surface floor layer. Moreover, in the Arctic zone of Western Siberia, more extreme (hotter and drier) meteorological conditions should be observed for the occurrence of ignition from lightning. The threshold values for the considered meteorological parameters were derived for our region for the first time. Obtained results can be used in the development of models for potential fire hazards prediction in various landscapes, which will have a practical application in various spheres of the national economy.
Full article
(This article belongs to the Special Issue Extreme Weather Events in Siberia)
►
Show Figures
Open AccessArticle
Seasonal Variations in Anthropogenic and Natural Particles Induced by Rising CO2 Levels
Atmosphere 2024, 15(1), 105; https://doi.org/10.3390/atmos15010105 - 15 Jan 2024
Abstract
Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO2-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to
[...] Read more.
Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO2-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to the seasonal variations of aerosol size modes. With rising CO2 levels, surface warming, and changes in atmospheric circulations and hydrologic cycles are found during both summer (JJA) and winter (DJF). For anthropogenic particles, changes in fine anthropogenic particulate matter (PM2.5, particles with diameters smaller than 2.5 μm) decrease over high-latitude regions and increase over the tropics in both DJF and JJA. Global mean column concentrations of PM2.5 decrease by approximately 0.19 mg m−2, and concentrations of coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase by 0.005 mg m−2 in JJA. Changes in anthropogenic particles in DJF are similar to those in JJA, but the magnitudes of maximum regional changes are much smaller than those in JJA. The coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase over northern Africa and the Arabian Peninsula during JJA, whereas changes in anthropogenic CPM during DJF are minimal. During both JJA and DJF, changes in anthropogenic CPM are about two orders of magnitude smaller than those of anthropogenic PM2.5. Enhanced wet deposition by large-scale precipitation under rising CO2-induced surface warming is the critical factor affecting changes in anthropogenic particles. For natural particles, the distribution of change in the natural PM2.5 burden is similar to that of natural CPM, but much larger than natural CPM during each season. Both natural PM2.5 and CPM burdens increase over northern Africa and the Arabian Peninsula during JJA, but decrease over most of the continental regions during DJF. Changes in surface wind speed, divergence/convergence of surface wind, and precipitation are primary reasons for the variation of natural particles.
Full article
(This article belongs to the Special Issue Ozone Pollution and Effects in China)
►▼
Show Figures
Figure 1
Open AccessArticle
A Radar Echo Extrapolation Model Based on a Dual-Branch Encoder–Decoder and Spatiotemporal GRU
Atmosphere 2024, 15(1), 104; https://doi.org/10.3390/atmos15010104 - 14 Jan 2024
Abstract
Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of
[...] Read more.
Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model’s ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects.
Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation
Atmosphere 2024, 15(1), 103; https://doi.org/10.3390/atmos15010103 - 14 Jan 2024
Abstract
Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather
[...] Read more.
Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.
Full article
(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)
►▼
Show Figures
Figure 1
Open AccessArticle
Assessment of Atmospheric Pollution by Selected Elements and PAHs during 12-Month Active Biomonitoring of Terrestrial Mosses
by
, , , , and
Atmosphere 2024, 15(1), 102; https://doi.org/10.3390/atmos15010102 - 14 Jan 2024
Abstract
Biomonitoring studies are most often used in short-term study periods to quickly obtain information on the state/quality of the environment and its pollution levels. Performing long-term surveys involves a prolonged wait for the result and is therefore not often used and is rather
[...] Read more.
Biomonitoring studies are most often used in short-term study periods to quickly obtain information on the state/quality of the environment and its pollution levels. Performing long-term surveys involves a prolonged wait for the result and is therefore not often used and is rather associated with classical air quality monitoring. The aim of this study was to evaluate atmospheric air pollution by selecting 16 elements and 16 polycyclic aromatic hydrocarbons conducted as part of a 12-month ‘moss-bag’ technique of an active biomonitoring method with the use of three moss species: Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum. All analytes were determined by inductively coupled plasma mass spectrometry (ICP-MS) and gas chromatography–mass spectrometry (GC-MS). As a result of the experiment, it was found that the concentrations of all elements increased with time of exposure. The total sum of them in D. polysetum moss was 30% and 60% more than in P. schreberi and S. fallax, respectively, which allows us to consider this species’ broader use in active biomonitoring. For PAHs analysis, the best biomonitor in time was P. schreberi, which accumulated 25% and 55% more than S. fallax and D. polysetum, respectively. In this one-year study, most organic compounds accumulated between 5 and 6 months of exposure, depending on the species. Given the low-cost nature of active biomonitoring, it should be concluded that mosses could be used in long-term monitoring of the quality of the atmospheric aerosol in terms of element and organic compound concentration in air.
Full article
(This article belongs to the Special Issue Biomonitoring—An Effective Tool for Air Pollution Assessment (2nd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
Simulating the Wind Energy Distribution in the Coastal Hilly Area of the Jiaodong Peninsula Using the Weather Research and Forecasting Model
Atmosphere 2024, 15(1), 101; https://doi.org/10.3390/atmos15010101 - 13 Jan 2024
Abstract
This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that
[...] Read more.
This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer.
Full article
(This article belongs to the Special Issue Land Surface Processes: Modeling and Observation)
►▼
Show Figures
Figure 1
Open AccessArticle
Quantifying the Impact of Urban Growth on Urban Surface Heat Islands in the Bangkok Metropolitan Region, Thailand
Atmosphere 2024, 15(1), 100; https://doi.org/10.3390/atmos15010100 - 12 Jan 2024
Abstract
The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration
[...] Read more.
The urban built environment, comprising structures, roads, and various facilities, plays a key role in the formation of urban heat islands, which inflict considerable damage upon human society. This phenomenon is particularly pronounced in urban areas characterized by the rapid growth and concentration of populations, a global trend, notably exemplified in megacities such as Bangkok, Thailand. The global trend of urbanization has witnessed unprecedented growth in recent decades, with cities transforming into megametropolises that profoundly impact changes in urban temperature, specifically the urban heat island (UHI) phenomenon induced by the rapid growth of urban areas. Elevated urban concentrations lead to increased city density, contributing to higher temperatures within the urban environment compared to the surrounding areas. The evolving land-use surface has assumed heightened significance due to urban development, necessitating accelerated efforts to mitigate urban heat islands. This study aims to quantify the influence of urban growth on urban surface temperature in Bangkok and its surrounding areas. The inverse relationship between urban temperature and land surface temperature (LST), coupled with urban area density, was examined using Landsat 5 and 8 satellite imagery. The analysis revealed a positive correlation between higher temperatures and levels of urban growth. Areas characterized by high-rise structures and economic activities experienced the most pronounced impact of the heat island phenomenon. The city exhibited a notable correlation between high density and high temperatures (high–high), signifying that increased density contributes to elevated temperatures due to heat dissipation (significant correlation of R2 = 0.8582). Conversely, low-temperature, low-density cities (low–low) with a dispersed layout demonstrated effective cooling of the surrounding area, resulting in a significant correlation with lower local temperatures (R2 = 0.7404). These findings provide valuable insights to assist governments and related agencies in expediting planning and policy development aimed at reducing heat in urban areas and steering sustainable urban development.
Full article
(This article belongs to the Special Issue Urban Heat Islands and Global Warming (2nd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data
Atmosphere 2024, 15(1), 99; https://doi.org/10.3390/atmos15010099 - 12 Jan 2024
Abstract
The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle
[...] Read more.
The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.
Full article
(This article belongs to the Special Issue Land Surface Temperature Retrieval Using Satellite Remote Sensing (2nd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions
by
, , , , , , , , and
Atmosphere 2024, 15(1), 98; https://doi.org/10.3390/atmos15010098 - 12 Jan 2024
Abstract
In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations
[...] Read more.
In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations to the entire Shanxi Province. The technology combines a dynamical model of the atmosphere with trace gas observations, combining forward and backward trajectories to greatly expand the information on long-lived CO2 gas concentrations over a trajectory path. The mapped results show good agreement with the observation results, which reveals the generalizability of the trajectory mapping domain-filling technology. The results show that the spatio-temporal distribution characteristics of CO2 concentration in the entire Shanxi region is significant: during the five years, the provincial average CO2 concentration exhibits an overall increasing trend. The CO2 concentration increases from the north to the south across the province. Influenced by the economic growth rate and COVID-19, there are differences in the annual variation characteristics of the CO2 concentration across the entire province, with the highest year-on-year growth in 2019 and a year-on-year decrease in 2020. The increasing rate of the CO2 concentration in the northern low-value areas is faster than that in the southern high-value areas. Overall, there is a decreasing trend in the CO2 concentration growth from the north to the south in the entire province. There are seasonal differences in the CO2 concentration distribution across the entire province. The CO2 concentration and amplitude are higher in autumn and winter than they are in spring and summer. This study can provide scientific support and methodological reference for the spatio-temporal distribution characteristics analysis of GHGs at the provincial–regional scale, as well as at the national and global scales.
Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
►▼
Show Figures
Figure 1
Open AccessReview
Light-Duty Vehicle Brake Emission Factors
by
, , , , and
Atmosphere 2024, 15(1), 97; https://doi.org/10.3390/atmos15010097 - 11 Jan 2024
Abstract
Particulate Matter (PM) air pollution has been linked to major adverse health effects. Road transport still contributes significantly to ambient PM concentrations, but mainly due to the non-exhaust emissions from vehicles. For the first time worldwide, limits for non-exhaust emissions have been proposed
[...] Read more.
Particulate Matter (PM) air pollution has been linked to major adverse health effects. Road transport still contributes significantly to ambient PM concentrations, but mainly due to the non-exhaust emissions from vehicles. For the first time worldwide, limits for non-exhaust emissions have been proposed by the European Union for the upcoming Euro 7 step. For these reasons, interest in brake emissions has increased in the past few years. Realistic emission factors are necessary to accurately calculate the contribution of brake emissions to air pollution but also to estimate the emissions reduction potential of new or existing technologies and improved brake formulations. This paper reviews emission factors from light-duty vehicles reported in the literature, with a focus on those that followed the recently introduced Global Technical Regulation (GTR 24) methodology on brakes in light-duty vehicles. Reduction efficiencies of non-asbestos organic (NAO) pads, brake dust filters, ceramic discs, coated discs, and regenerative braking are also discussed. Finally, the emission factors are compared with roadside measurements of brake emissions and emission inventories worldwide. The findings of this study can be used as an input in emission inventories to estimate the contribution of brakes to air pollution.
Full article
(This article belongs to the Special Issue Chemical Composition and Sources of Particles in the Atmosphere (2nd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
Synoptic-Scale Wildland Fire Weather Conditions in Mexico
Atmosphere 2024, 15(1), 96; https://doi.org/10.3390/atmos15010096 - 11 Jan 2024
Abstract
Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire
[...] Read more.
Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were “relative humidity” for the SWM and “temperature” for the SEM. The fire-related indices selected were “precipitable water vapor anomaly” for the SWM and “temperature anomaly” for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico.
Full article
(This article belongs to the Section Meteorology)
►▼
Show Figures
Figure 1
Open AccessArticle
Effect of Heating Emissions on the Fractal Size Distribution of Atmospheric Particle Concentrations
Atmosphere 2024, 15(1), 95; https://doi.org/10.3390/atmos15010095 - 11 Jan 2024
Abstract
Excessive particle concentrations during heating periods, which greatly affect people’s physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of
[...] Read more.
Excessive particle concentrations during heating periods, which greatly affect people’s physical and mental health and their normal lives, continue to be a concern. It is more practical to understand and analyze the relationship between the fractal dimension and particle size concentration distribution of atmospheric particulate matter before and after adjusting heating energy consumption types. The data discussed and analyzed in this paper were collected by monitoring stations and measured from 2016 to 2018 in Xi’an. The data include fractal dimension and particle size concentration changes in the atmospheric particulate matter before and after adjusting the heating energy consumption types. The results indicate that adjusting the heating energy consumption types has a significant impact on particulate matter. The average concentration of PM2.5 decreased by 26.4 μg/m3. The average concentration of PM10 decreased by 31.8 μg/m3. At the same time, the different particle sizes showed a downward trend. The particles ranging from 0.265 to 0.475 μm demonstrated the maximum decrease, which was 8.80%. The heating period in Xi’an mainly involves particles ranging from 0 to 0.475 μm. The fractal dimensions of the atmospheric particulate matter before and after adjusting the heating energy consumption types were 4.809 and 3.397, respectively. After adjusting the heating energy consumption types, the fractal dimension decreased by 1.412. At that time, the proportions of particle sizes that were less than 1.0 μm, 2.0 μm, and 2.5 μm decreased by 1.467%, 0.604%, and 0.424%, respectively. This paper provides new methods and a reference value for the distribution and effective control of atmospheric particulate matter by adjusting heating energy consumption types.
Full article
(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (3rd Edition))
►▼
Show Figures
Figure 1
Open AccessArticle
Precipitation and Moisture Transport of the 2021 Shimokita Heavy Precipitation: A Transformed Extratropical Cyclone from Typhoon#9
by
and
Atmosphere 2024, 15(1), 94; https://doi.org/10.3390/atmos15010094 - 11 Jan 2024
Abstract
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy
[...] Read more.
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy rainfall and to verify the role of EC9. The authors created intensive hourly precipitation data over the Aomori Prefecture and analyzed them together with moisture fields. In most locations where the landslide disaster occurred, there were two precipitation peaks: at 9 UTC and 18 UTC on 9 August. The wind shear was strong from the lower to the upper troposphere with easterly winds in the lower troposphere and warm moist air from south for the first peak. A strong horizontal gradient of equivalent potential temperature, a northerly in lower troposphere, and moisture convergence over Shimokita Peninsula indicate the existence of the stationary front for the latter peak (18 UTC). The heavy precipitation and moisture convergence that caused the Shimokita event were identified by the stationary front of EC9 around the latter peak (15 UTC of 9th–06 UTC of 10 August). The precipitation distribution, which has a precipitation peak northeast of the EC center, is a typical typhoon-turned extratropical cyclone (EC) precipitation distribution.
Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
►▼
Show Figures
Figure 1
Open AccessArticle
Evaluation of Daily Temperature Extremes in the ECMWF Operational Weather Forecasts and ERA5 Reanalysis
Atmosphere 2024, 15(1), 93; https://doi.org/10.3390/atmos15010093 - 11 Jan 2024
Abstract
In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from
[...] Read more.
In weather forecasting and climate monitoring, daily maximum and minimum air temperatures (TMAX and TMIN) are fundamental for operational and research purposes, from early warning of extreme events to climate change studies. This study provides an integrated evaluation of TMAX and TMIN from two European Centre for Medium-range Weather Forecasts (ECMWF) products: ERA5 reanalysis (1980–2019) and operational weather forecasts (2017–2021). Both products are evaluated using in situ observations from the Global Historical Climatology Network (GHCN). While the analyses span globally, emphasis is given to four key regions: Europe, East and West United States, and Australia. Results reveal a general underestimation of TMAX and overestimation of TMIN in both operational forecasts and ERA5, highlighting the limitation of the ECMWF model in estimating the amplitude of the diurnal cycle of air temperature. ERA5′s accuracy has improved over the past decade, due to enhanced constrain of land–atmosphere analysis streaming from more and higher-quality satellite data. Furthermore, ERA5 outperforms one-day-ahead weather forecasts, indicating that non-real-time dependent studies should rely on ERA5 instead of real-time operational forecasts. This study underscores the importance of ongoing research in model and data assimilation, considering the relevance of daily temperature extremes forecasting and reanalysis for operational meteorology and climate monitoring.
Full article
(This article belongs to the Special Issue Weather and Climate Extremes: Observations, Modeling, and Impacts)
►▼
Show Figures
Figure 1
Open AccessArticle
A Method for the Ambient Equivalent Dose Estimation in a Wide Range of Altitudes during SEP and GLE Events
Atmosphere 2024, 15(1), 92; https://doi.org/10.3390/atmos15010092 - 10 Jan 2024
Abstract
The paper considers the modeling of proton transport through the Earth’s atmosphere during several SEP events (12 August 1989, 23 March 1991, and 8 November 2000), as well as during the GLE73 event. Solar sources and interplanetary medium conditions during these events are
[...] Read more.
The paper considers the modeling of proton transport through the Earth’s atmosphere during several SEP events (12 August 1989, 23 March 1991, and 8 November 2000), as well as during the GLE73 event. Solar sources and interplanetary medium conditions during these events are described in detail. Calculations are carried out using own model implemented with GEANT4. As the main results, quantitative estimates of the calculated ambient dose equivalent for altitudes in a wide range (also including civil aircraft flight altitudes of 10–11 km) for the geomagnetic cutoff rigidity values Rc = 0.13 GV are given.
Full article
(This article belongs to the Special Issue Novel Insights into the Effects of Space Weather on Human Health)
►▼
Show Figures
Figure 1
Open AccessArticle
Convection-Permitting Future Climate Simulations for Bulgaria under the RCP8.5 Scenario
Atmosphere 2024, 15(1), 91; https://doi.org/10.3390/atmos15010091 - 10 Jan 2024
Abstract
In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap
[...] Read more.
In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change.
Full article
(This article belongs to the Special Issue Characteristics of the Atmosphere and Their Impact on Quality of Life, Ecosystems, and Human Activities)
►▼
Show Figures
Figure 1
Open AccessArticle
Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation
Atmosphere 2024, 15(1), 90; https://doi.org/10.3390/atmos15010090 - 10 Jan 2024
Abstract
Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain
[...] Read more.
Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain adaptation for daily maximum temperature forecasting in experimental low-resource settings. We used an attention-based transformer deep learning architecture as the core forecasting framework and used kernel mean matching (KMM) for domain adaptation. Domain adaptation significantly improved forecasting accuracy in most experimental settings, thereby mitigating domain differences between source and target regions. Specifically, we observed that domain adaptation is more effective than exclusively training on a small amount of target-domain training data. This study reinforces the potential of using DNNs for temperature forecasting and underscores the benefits of domain adaptation using KMM. It also highlights the need for caution when using small amounts of target-domain data to avoid overfitting. Future research includes investigating strategies to minimize overfitting and to further probe the effect of various factors on model performance.
Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
►▼
Show Figures
Figure 1
Open AccessArticle
Teleconnections of Atmospheric Circulations to Meteorological Drought in the Lancang-Mekong River Basin
Atmosphere 2024, 15(1), 89; https://doi.org/10.3390/atmos15010089 - 10 Jan 2024
Abstract
The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to
[...] Read more.
The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to the local socio-economic structures and ecological systems. Thus, this study aimed to analyze the meteorological drought characteristics in the LMRB and identify the impact and correlation of atmospheric circulation on the meteorological drought in the basin. Specifically, the different levels of meteorological drought events were defined using the Run Theory based on the seasonal and annual SPEI from 1980 to 2018. The time lag correlation between meteorological drought events and the EI Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), were analyzed in the LMRB. Our results indicated that, from a temporal perspective, the period from November to April of the following year was particularly prone to meteorological droughts in the basin. In terms of spatial distribution, the primary agricultural regions within the basin, including Thailand, Eastern Cambodia, and Vietnam, were highly susceptible to meteorological droughts. Further analysis revealed a teleconnection between drought events in the LMRB and atmospheric circulation factors. The sensitivity of the basin’s drought timing to its response decreased in the order of the ENSO > AO > NAO > PDO. In general, the ENSO had the most substantial influence on drought events in the basin, with the strongest response relationship, while the upper reaches of the basin displayed the most significant response to the AO; the occurrence and progression of meteorological droughts in this area synchronized with the AO. These findings enhance our understanding of drought-prone areas in the LMRB, including the meteorological factors and driving mechanisms involved. This information is valuable for effectively mitigating and managing drought risks in the region.
Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
►▼
Show Figures
Figure 1
Open AccessArticle
Long-Term Evolution in Noctilucent Clouds’ Response to the Solar Cycle: A Model-Based Study
Atmosphere 2024, 15(1), 88; https://doi.org/10.3390/atmos15010088 - 09 Jan 2024
Abstract
Noctilucent clouds (NLC) are sensitive indicators in the upper mesosphere, reflecting changes in the background atmosphere. Studying NLC responses to the solar cycle is important for understanding solar-induced changes and assessing long-term climate trends in the upper mesosphere. Additionally, it enhances our understanding
[...] Read more.
Noctilucent clouds (NLC) are sensitive indicators in the upper mesosphere, reflecting changes in the background atmosphere. Studying NLC responses to the solar cycle is important for understanding solar-induced changes and assessing long-term climate trends in the upper mesosphere. Additionally, it enhances our understanding of how increases in greenhouse gas concentration in the atmosphere impact the Earth’s upper mesosphere and climate. This study presents long-term trends in the response of NLC and the background atmosphere to the 11-year solar cycle variations. We utilised model simulations from the Leibniz Institute Middle Atmosphere (LIMA) and the Mesospheric Ice Microphysics and Transport (MIMAS) over 170 years (1849 to 2019), covering 15 solar cycles. Background temperature and water vapour (H2O) exhibit an apparent response to the solar cycle, with an enhancement post-1960, followed by an acceleration of greenhouse gas concentrations. NLC properties, such as maximum brightness (βmax), calculated as the maximum backscatter coefficient, altitude of βmax (referred to as NLC altitude) and ice water content (IWC), show responses to solar cycle variations that increase over time. This increase is primarily due to an increase in background water vapour concentration caused by an increase in methane (CH4). The NLC altitude positively responds to the solar cycle mainly due to solar cycle-induced temperature changes. The response of NLC properties to the solar cycle varies with latitude, with most NLC properties showing larger and similar responses at higher latitudes (69° N and 78° N) than mid-latitudes (58° N).
Full article
(This article belongs to the Section Upper Atmosphere)
►▼
Show Figures
Figure 1
Open AccessArticle
A Detailed Limited-Area Atmospheric Energy Cycle for Climate and Weather Studies
Atmosphere 2024, 15(1), 87; https://doi.org/10.3390/atmos15010087 - 09 Jan 2024
Abstract
Lorenz’ seminal work on global atmospheric energetics improved our understanding of the general circulation. With the advent of Regional Climate Models (RCMs), it is important to have a limited-area energetic budget available that is applicable for both weather and climate, analogous to Lorenz’
[...] Read more.
Lorenz’ seminal work on global atmospheric energetics improved our understanding of the general circulation. With the advent of Regional Climate Models (RCMs), it is important to have a limited-area energetic budget available that is applicable for both weather and climate, analogous to Lorenz’ global atmospheric energetics. A regional-scale energetic budget is obtained in this study by applying Reynolds decomposition rules to quadratic forms of the kinetic energy K and the available enthalpy A, to obtain time mean and time deviation contributions. According to the employed definition, the time mean energy contributions are decomposed in a component associated with the time-averaged atmospheric state and a component due to the time-averaged statistics of transient eddies; these contributions are suitable for the study of the climate over a region of interest. Energy fluctuations (the deviations of instantaneous energies from their climate value) that are appropriate for weather studies are split into quadratic and linear contributions. The sum of all the contributions returns exactly to the total primitive kinetic energy and available enthalpy equations.
Full article
(This article belongs to the Section Climatology)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Atmosphere Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Atmosphere, Energies, JMSE, Sustainability, Wind
Wind, Wave and Tidal Energy Technologies in China
Topic Editors: Wei Shi, Qihu Sheng, Fengmei Jing, Dahai Zhang, Puyang ZhangDeadline: 31 January 2024
Topic in
Atmosphere, Hydrology, Remote Sensing, Sustainability, Water
Hydrology and Water Resources Management
Topic Editors: Genxu Wang, Hongwei Lu, Lei Wang, Bahman NaserDeadline: 30 March 2024
Topic in
Air, Atmosphere, Remote Sensing, Sustainability, Pollutants
Accessing and Analyzing Air Quality and Atmospheric Environment
Topic Editors: Enrico Ferrero, Elvira Kovač-AndrićDeadline: 31 March 2024
Topic in
Atmosphere, GeoHazards, Geosciences, Remote Sensing, Water
Natural Hazards and Environmental Challenges in the Anthropocene Age
Topic Editors: Johnbosco C. Egbueri, Chaitanya B. Pande, Quoc Bao PhamDeadline: 1 May 2024
Conferences
Special Issues
Special Issue in
Atmosphere
Contributions of Emission Inventory to Air Quality
Guest Editors: Xin Bo, Zhongjun XuDeadline: 20 January 2024
Special Issue in
Atmosphere
Climate Variability and Change in Brazil
Guest Editors: Flávio Justino, Roger Rodrigues TorresDeadline: 26 January 2024
Special Issue in
Atmosphere
Bioindicators in Air Pollution Monitoring
Guest Editors: Maria Grazia Alaimo, Daniela VarricaDeadline: 15 February 2024
Special Issue in
Atmosphere
Science and Technology of Indoor and Outdoor Environment
Guest Editors: Xingwang Zhao, Junzhou He, Zhipeng DengDeadline: 26 February 2024
Topical Collections
Topical Collection in
Atmosphere
Measurement of Exposure to Air Pollution
Collection Editor: Luca Stabile
Topical Collection in
Atmosphere
Livestock Odor Issues and Air Quality
Collection Editor: Jacek Koziel
Topical Collection in
Atmosphere
Indoor Air Quality: From Sampling to Risk Assessment in the Light of New Legislations
Collection Editors: Pasquale Avino, Gaetano Settimo