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
Deep-Learning Correction Methods for Weather Research and Forecasting (WRF) Model Precipitation Forecasting: A Case Study over Zhengzhou, China
Atmosphere 2024, 15(6), 631; https://doi.org/10.3390/atmos15060631 (registering DOI) - 24 May 2024
Abstract
Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle the complex features of hourly precipitation, resulting in the
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Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle the complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. In this study, we proposed a deep-learning model called PBT (Population-Based Training)-GRU (Gate Recurrent Unit) based on numerical model NWP gridded forecast data and observation data and employed machine-learning (ML) methods, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gradient-Boosted Decision Tree (GBDT), to correct the WRF hourly precipitation forecasts. To select the evaluation method, we conducted a sample balance experiment and found that when the proportion of positive and negative samples was 1:1, the Threat Score (TS) and accuracy scores were the highest, while the Probability of Detection (POD) score was slightly lower. The results showed that: (1) the overall errors of the PBT-GRU model were relatively smaller, and its root mean square error (RMSE) was only 1.12 mm, which was reduced by 63.04%, 51.72%, 58.36%, 37.43%, and 26.32% compared to the RMSE of WRF, SVM, KNN, GBDT, and RF, respectively; and (2) according to the Taylor diagram, the standard deviation ( ) and correlation coefficient (r) of PBT-GRU were 1.02 and 0.99, respectively, while the and r of RF were 1.12 and 0.98, respectively. Furthermore, the and r of the SVM, GBDT, and KNN models were between those of the above models, with values of 1.24 and 0.95, 1.15 and 0.97, and 1.26 and 0.93, respectively. Based on a comprehensive analysis of the TS, accuracy, RMSE, r and , the PBT-GRU model performed the best, with a significantly better correction effect than that of the ML methods, resulting in an overall performance ranking of PBT-GRU > RF > GBDT > SVM > KNN. This study provides a hint of the possibility that the proposed PBT-GRU model can outperform model precipitation correction based on a small sample of one-station data. Thus, due to its promising performance and excellent robustness, we recommend adopting the proposed PBT-GRU model for precipitation correction in business applications.
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(This article belongs to the Special Issue Deep Learning Algorithms for Weather Forecasting and Climate Prediction)
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Open AccessArticle
Unveiling Trends and Hotspots in Air Pollution Control: A Bibliometric Analysis
by
Jing Chen, Qinghai Chen, Lin Hu, Tingting Yang, Chuangjian Yi and Yingtang Zhou
Atmosphere 2024, 15(6), 630; https://doi.org/10.3390/atmos15060630 - 24 May 2024
Abstract
With the continuous acceleration of urbanization, air pollution has become an increasingly serious threat to public health. Strengthening the detection and control of pollutants has become a focal point in current society. In light of the increasing amount of literature in the field
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With the continuous acceleration of urbanization, air pollution has become an increasingly serious threat to public health. Strengthening the detection and control of pollutants has become a focal point in current society. In light of the increasing amount of literature in the field of air pollution control with every passing year, numerous reviews have been compiled; however, only a limited number employ bibliometric methods to comprehensively review and summarize research trends in this field. Herein, this study utilizes two bibliometric analysis tools, namely, CiteSpace (6.1.R6) and VOSviewer (1.6.20), to conduct a visual and comprehensive analysis of air pollution literature spanning 2000 to 2023. By doing so, it establishes a knowledge framework for research on air pollution control. Simultaneously, collaborative network analysis, reference co-citation network analysis, keyword co-occurrence network analysis, and keyword prominence are employed to undertake an exhaustive and profound visual examination within this domain. Results indicate that, over time, the number of relevant papers has exponentially increased, while interdisciplinary cooperation trends have gradually formed. Additionally, this study describes key areas of current research, including air pollution control residue treatment, regional joint air pollution control, and air pollution control mechanism analysis. Finally, challenges faced by researchers in this field and their different perspectives are discussed. To better integrate research findings on air pollution control, we explore the correlations among data and systematically present their developmental trends. This confirms the interdisciplinary nature of air pollution control research, in the hope of its guiding air pollution control in the future.
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(This article belongs to the Special Issue Air Pollution Control in China: Progress, Challenges, and Perspectives (2nd Edition))
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Open AccessArticle
Residual Spatiotemporal Convolutional Neural Network Based on Multisource Fusion Data for Approaching Precipitation Forecasting
by
Tianpeng Zhang, Donghai Wang, Lindong Huang, Yihao Chen and Enguang Li
Atmosphere 2024, 15(6), 628; https://doi.org/10.3390/atmos15060628 - 24 May 2024
Abstract
Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden
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Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Open AccessArticle
Changes in Convective Precipitation Reflectivity over the CONUS Revealed by High-Resolution Radar Observations from 2015 to 2021
by
Haotong Jing, Zhi Li, Yixin Wen, Shang Gao, Yueya Wang, Weikang Qian and Jesse Kisembe
Atmosphere 2024, 15(6), 627; https://doi.org/10.3390/atmos15060627 - 24 May 2024
Abstract
The change in extreme precipitation events in the conterminous United States (CONUS) has been of interest to the research communities in recent years for its intensification under environmental and climate change. Previous studies have not yet used sub-hourly precipitation observations to examine convective
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The change in extreme precipitation events in the conterminous United States (CONUS) has been of interest to the research communities in recent years for its intensification under environmental and climate change. Previous studies have not yet used sub-hourly precipitation observations to examine convective precipitation change over the CONUS. This study aims to fill the gap by examining convective precipitation, identified by radar reflectivity, in the CONUS using the state-of-the-art Multi-radar Multi-sensor data, operated at the NOAA/National Severe Storms Laboratory, with an unprecedentedly high spatial (1 km) and temporal (2 min) resolutions. These high-resolution data are expected to better capture the precipitation peak and the precipitation pattern. The results showed that in CONUS, precipitation reflectivity increased both in magnitude and the number of convective days from 2015 to 2021. For example, in 2019, 60% of areas showed an increase in the magnitude of precipitation, and the average number of convective days over CONUS has increased by 19%. Changes in precipitation also vary by season and region. This study highlights the need for continued monitoring and understanding of the evolving pattern of extreme precipitation in the CONUS, especially at sub-hourly frequency, as it exposes significant impacts on various sectors, including agriculture, infrastructure, and human health.
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(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))
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Open AccessArticle
The Influence of Sudden Stratospheric Warming on the Development of Ionospheric Storms: The Alma-Ata Ground-Based Ionosonde Observations
by
Galina Gordiyenko, Artur Yakovets, Yuriy Litvinov and Alexey Andreev
Atmosphere 2024, 15(6), 626; https://doi.org/10.3390/atmos15060626 - 23 May 2024
Abstract
This paper examines the response of the ionosphere to the impact of two moderate geomagnetic storms observed on January 17 and 26–27, 2013, under conditions of strong sudden stratospheric warming. The study uses data from ground-based ionosonde measurements at the Alma-Ata ionospheric station
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This paper examines the response of the ionosphere to the impact of two moderate geomagnetic storms observed on January 17 and 26–27, 2013, under conditions of strong sudden stratospheric warming. The study uses data from ground-based ionosonde measurements at the Alma-Ata ionospheric station (43.25 N, 76.92 E) combined with optical observation data (The Spectral Airglow Temperature Imager (SATI)). Ionosonde data showed that the geomagnetic storms under consideration do not generate ionospheric storms but demonstrate some unusual types of diurnal foF2 variations with large (up to 60%) deviations in foF2 from median values observed during the night/morning periods on 13–15 and 20–23 January, which do not have any relation to solar or geomagnetic activity. Wave-like disturbances in foF2, h’F, and daily averaged foF2 values with a quasi-period of 5–8 days and peak-to-peak amplitude from about 1 MHz to 2 MHz (from 20% to 40%) and ~40 km are observed during the period 9–28 January, after registration of the occurrence of the major SSW event on 6–7 January. The observed variations in the OH emission rate are found to be quite similar to those observed in the ionospheric parameters that assume a community of processes in the stratosphere/mesosphere/ionosphere system. The study shows that the F region of the ionosphere is influenced by processes in the lower ionosphere, in this case by processes associated with sudden stratospheric warming SSW-2013, which led to modification of the structure of the ionosphere and compensation of processes associated with the development of the ionospheric storms.
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(This article belongs to the Special Issue Effect of Solar Activities to the Earth's Atmosphere)
Open AccessArticle
Direct and Indirect Effects of Mountain Heights on Heavy Rainfall in the Hokitika Region of New Zealand
by
Yang Yang, Ian Boutle, Stuart Moore, Trevor Carey-Smith and John Crouch
Atmosphere 2024, 15(6), 625; https://doi.org/10.3390/atmos15060625 - 23 May 2024
Abstract
In the Hokitika region, on the west coast of the South Island of New Zealand on 18 June 2015, very heavy stratiform precipitation (>200 mm/per day) occurred under north-westerlies with small CAPE (<25 J/kg). Analyses of model simulations and observations showed that this
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In the Hokitika region, on the west coast of the South Island of New Zealand on 18 June 2015, very heavy stratiform precipitation (>200 mm/per day) occurred under north-westerlies with small CAPE (<25 J/kg). Analyses of model simulations and observations showed that this heavy rainfall was due to cold-front lifting enhanced by orographic lifting over the Southern Alps. At 1.5 km grid-length, the model terrain underestimated the average height of the 103 tallest mountains over the South Island by ~800 m. This led to weaker orographic lifting and mountain blocking, and a faster-moving and stronger cold front in the Hokitika region. As a result, large errors in the heavy rainfall prediction occurred. By increasing either the resolved or the sub-grid mountain heights, the simulated rainfall errors were largely reduced through stronger orographic lifting and mountain blocking, and simulation of the cold front movement and strength was improved. All the experiments have the same “flow-over” regime with mountain waves and/or wave breaking ( ranges 0.61–1.21). However, the rainfall amount and distribution on the windward side of the mountains varied significantly. Our new findings were that the Southern Alps can have significant indirect effects on heavy rainfall by altering the speed and strength of the cold front, in addition to the well-known direct dynamical effects (i.e., orographic lifting and mountain blocking). A combination of these direct and indirect effects makes the heavy rainfall simulation sensitive to mountain heights even under the same “flow-over” regime.
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(This article belongs to the Section Meteorology)
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Open AccessArticle
Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data
by
Abdelrahim Salih and Abdalhaleem Hassablla
Atmosphere 2024, 15(6), 624; https://doi.org/10.3390/atmos15060624 - 23 May 2024
Abstract
Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the
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Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.
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(This article belongs to the Special Issue Natural Disasters and Hazards in the Geographical Environment (2nd Edition))
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Open AccessArticle
Ural Blocking and the Amplitude of Wintertime Cold Surges over North China Detected by a Cooling Algorithm
by
Zifan Yang, Wenyu Huang, Ruyan Chen, Daiyu Lin, Bin Wang and Wenqian Ma
Atmosphere 2024, 15(6), 623; https://doi.org/10.3390/atmos15060623 - 22 May 2024
Abstract
A new algorithm is proposed to estimate the cooling amplitude over North China induced by each Ural blocking event. Taking full account of potential transient temperature rises during the cooling process and the lag time of cooling relative to the blocking, this algorithm
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A new algorithm is proposed to estimate the cooling amplitude over North China induced by each Ural blocking event. Taking full account of potential transient temperature rises during the cooling process and the lag time of cooling relative to the blocking, this algorithm provides more detailed analysis which should not be possible by using former methods. The amplitude of the Ural blocking-related cooling events is found to have a broad distribution. Further, although most Ural blocking events lead to severe cold surges over North China, the number of Ural blocking events that do not induce significant cooling over North China cannot be ignored. The possible reasons for the wide range in cooling amplitude are explored in terms of the lifetimes and geographical centers of the blocking highs, the circulation patterns preceding the onset of the cooling events, and the snowfall associated with cooling events. Larger amplitude cooling events occur in Ural blocking highs that have longer lifetimes and northwestward displacements of their geographical centers. The northward displacement of a Ural blocking center favors the advection of extremely cold air from the Arctic regions, which accumulates in West Siberia and subsequently gives rise to the most severe cold surges over North China. The lack of activities of cold surges before the blocking-related cooling events not only amplifies the cooling amplitude directly, but also increases the occurrence probabilities of snowfalls through its modulation on the local specific humidity. The increased albedo and subsequent snow-melt induced cooling further amplify the cooling amplitude.
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(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation)
Open AccessArticle
Lidar Complex for Control of the Ozonosphere over Tomsk, Russia
by
Alexey A. Nevzorov, Alexey V. Nevzorov, Olga Kharchenko and Yaroslav O. Romanovskii
Atmosphere 2024, 15(6), 622; https://doi.org/10.3390/atmos15060622 - 22 May 2024
Abstract
We present a union of three measurement systems on the basis of the Siberian lidar station and mobile ozone lidar. The lidars are designed for studying the ozonosphere using the method of differential absorption and scattering, as well as for studying aerosol fields
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We present a union of three measurement systems on the basis of the Siberian lidar station and mobile ozone lidar. The lidars are designed for studying the ozonosphere using the method of differential absorption and scattering, as well as for studying aerosol fields using elastic single scattering. The systems are constructed on the basis of Nd:YAG lasers (SOLAR) and an Nd:YAG laser (LOTIS TII), a XeCl laser (Lambda Physik) and receiving telescopes assembled using the Kassegrain system with a diameter 0.35 m and the Newtonian 0.5 m system. Lidars operate in photon-counting mode and record lidar signals with a spatial resolution from 1.5 m to 160 m at sensing wavelengths of 299/341 nm in the altitude range of ~0.1–12 km and ~5–20, and at 308/353 nm in the altitude range of ~15–45 km. The union of these three measurement systems was used to carry out field experiments of atmospheric lidar sensing in Tomsk and to present the results of retrieving the vertical profile of the ozone concentration. In this study, coverage of the entire ozonosphere by the lidars was carried out for the first time in Russia.
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(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Open AccessArticle
Imaging and Interferometric Mapping Exploration for PIESAT-01: The World’s First Four-Satellite “Cartwheel” Formation Constellation
by
Tian Zhang, Yonggang Qian, Chengming Li, Jufeng Lu, Jiao Fu, Qinghua Guo, Shibo Guo and Yuxiang Wang
Atmosphere 2024, 15(6), 621; https://doi.org/10.3390/atmos15060621 - 21 May 2024
Abstract
The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains
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The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the “Cartwheel” configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China’s 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data.
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(This article belongs to the Special Issue Land Surface Processes: Modeling and Observation)
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Relations between High Anticyclonic Atmospheric Types and Summer Season Temperature in Bulgaria
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Vulcho Pophristov, Nina Nikolova, Simeon Matev and Martin Gera
Atmosphere 2024, 15(6), 620; https://doi.org/10.3390/atmos15060620 - 21 May 2024
Abstract
The atmospheric circulation, not only near the surface but also at high altitudes, is probably the main factor determining the weather and climate of a given area, along with its latitude, altitude, the shape of the relief of the area and its surroundings,
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The atmospheric circulation, not only near the surface but also at high altitudes, is probably the main factor determining the weather and climate of a given area, along with its latitude, altitude, the shape of the relief of the area and its surroundings, and the proximity of water basins of different sizes. The main objective of this study is to investigate the relationship between anticyclonic circulation types in the middle troposphere at the 500 hPa level and the seasonal summer temperature over the region of the central Balkan Peninsula, particularly Bulgaria. A previously compiled classification of atmospheric circulation is used, and the frequencies of the circulation types are correlated with the mean seasonal (monthly) temperature, where the extreme seasons and months are defined as the 10th percentile for cold summer seasons and months and the 90th percentile for warm ones. A positive and statistically significant correlation was found for the anticyclones located southwest of Bulgaria and a negative one for those located southeast of it. A comparison between the last two 30-year climatological periods (1961–1990 and 1991–2020) was also made, and an irrefutable decrease in the number of cold summer seasons from 257 to just 17 was found in the last 30 years, respectively, as well as a rapid increase in the number of extreme warm summer seasons from 26 to 263, encompassing all 15 meteorological stations studied.
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(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation)
Open AccessArticle
Assessing the Impacts of Mulching-Induced Warming Effects on Machine-Picked Cotton Zones
by
Yuanshuai Dai, Hui Zhang, Gang Li, Mingfeng Yang and Xin Lv
Atmosphere 2024, 15(6), 619; https://doi.org/10.3390/atmos15060619 - 21 May 2024
Abstract
The 20th century saw notable fluctuations in global temperatures, which significantly impacted agricultural climate zones across the Earth. Focusing on Xinjiang, China, a leading region in machine-picked cotton production, we identified several key thermal indicators influencing the yield, including the sum of active
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The 20th century saw notable fluctuations in global temperatures, which significantly impacted agricultural climate zones across the Earth. Focusing on Xinjiang, China, a leading region in machine-picked cotton production, we identified several key thermal indicators influencing the yield, including the sum of active temperatures ≥ 10 °C, the mean temperature in July, the climatological growing season length, the April–May sum of active temperatures, the last frost day, and the defoliant spray time. Using meteorological data from 58 weather stations in Xinjiang, we examined the spatiotemporal trends of these indicators during the 1981–2020 period. Additionally, we attempted to determine the effects of plastic mulching on the sowing area and the zoning area of machine-picked cotton in different suitable zones based on these indicators. In conclusion, the overall thermal resources in Xinjiang are exhibiting an upward trend and show a distribution pattern of “more in the south of Xinjiang than in the north of Xinjiang, and more in the plains and basins than in the mountains”. Under the plastic-mulching mechanism, the zoning area of the suitable zone has increased by 15.7% (2.15 × 103 km2), suggesting that climate warming and the widespread application of mulching technology provide unexplored potential for the most suitable regions for machine-picked cotton in Xinjiang, while the 14.5% (0.26 × 103 km2) and 7.8% (0.17 × 103 km2) reductions in the unsuitable and less suitable zones, respectively, suggest that the planting areas of machine-picked cotton in both the less suitable and unsuitable zones, particularly with the existing regional planning, continue to demonstrate an irrational expansion. Therefore, to sustain Xinjiang’s cotton industry’s resilience and productivity, policymakers need to prioritize proactive land management and sustainable land allocation practices in response to changing climate patterns to optimize cotton production.
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(This article belongs to the Section Biometeorology)
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Open AccessEditorial
Air Pollution, Health Effects Indicators, the Exposome, and One Health
by
Daniele Contini and Francesca Costabile
Atmosphere 2024, 15(5), 618; https://doi.org/10.3390/atmos15050618 - 20 May 2024
Abstract
Ambient air pollution is the seventh highest risk factor for human health, being responsible for millions of premature deaths per year globally [...]
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(This article belongs to the Special Issue Feature Papers in Air Pollution, Health Effects Indicators, Exposome, and One Health)
Open AccessArticle
Using HawkEye Level-2 Satellite Data for Remote Sensing Tasks in the Presence of Dust Aerosol
by
Anna Papkova, Darya Kalinskaya and Evgeny Shybanov
Atmosphere 2024, 15(5), 617; https://doi.org/10.3390/atmos15050617 - 20 May 2024
Abstract
This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol
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This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ−3 to λ−9. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this.
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(This article belongs to the Special Issue Optical Characteristics of Aerosol Pollution)
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Impact of Meteorological Conditions on PM2.5 Pollution in Changchun and Associated Health Risks Analysis
by
Chunsheng Fang, Xinlong Li, Juan Li, Jiaqi Tian and Ju Wang
Atmosphere 2024, 15(5), 616; https://doi.org/10.3390/atmos15050616 - 20 May 2024
Abstract
The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM2.5 levels in Changchun,
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The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM2.5 reduction strategies.
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(This article belongs to the Section Air Quality and Human Health)
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Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China
by
Fang Zhang, Jin Ding, Yu Chen, Tingzhao Yu, Xinxin Zhang, Jie Guo, Xiaodan Liu, Yan Wang, Qingyang Liu and Yingying Song
Atmosphere 2024, 15(5), 615; https://doi.org/10.3390/atmos15050615 - 20 May 2024
Abstract
In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast
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In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.
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(This article belongs to the Section Climatology)
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Assessment of Deadly Heat Stress and Extreme Cold Events in the Upper Midwestern United States
by
Manas Khan, Rabin Bhattarai and Liang Chen
Atmosphere 2024, 15(5), 614; https://doi.org/10.3390/atmos15050614 - 19 May 2024
Abstract
Understanding and addressing the implications of extreme temperature-related events are critical under climate change, as they directly impact public health and strain energy infrastructure. This study delved into the critical assessment of deadly heat stress and extreme cold events in the Upper Midwestern
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Understanding and addressing the implications of extreme temperature-related events are critical under climate change, as they directly impact public health and strain energy infrastructure. This study delved into the critical assessment of deadly heat stress and extreme cold events in the Upper Midwestern United States (UMUS), from 1979 to 2021, recognizing the substantial and disparate impact these phenomena have on socially vulnerable communities. In the current study, the modified Mann–Kendall method was applied to understand the temporal trend of extreme heat stress, as well as extreme cold events, from 1979 to 2021 in the UMUS. The results showed that the average annual frequency of daytime extreme heat stress events was comparatively lower in the northern parts of the UMUS compared to the southern parts from 1979 to 2021. Furthermore, a significant increasing trend in daytime extreme heat stress was found in parts of Michigan, Wisconsin (around the lake region), Ohio, and lower parts of Indiana and Kentucky from 1979 to 2021. In contrast, a decreasing trend was noticed in western parts of the UMUS (parts of Minnesota, Iowa, and Missouri). A significant decreasing trend in extreme cold events was found throughout the UMUS from 1979 to 2021. However, an increasing trend was also noticed in Iowa and northern parts of Minnesota, Michigan, and Wisconsin. The results provide important insights for better understanding the unique risks posed by extreme temperature-related events, especially toward socially vulnerable communities in the UMUS, which is crucial for developing targeted interventions and fostering resilience in the face of escalating climate-related threats.
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(This article belongs to the Section Climatology)
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Research on CC-SSBLS Model-Based Air Quality Index Prediction
by
Lin Wang, Yibing Wang, Jian Chen, Shuangqing Zhang and Lanhong Zhang
Atmosphere 2024, 15(5), 613; https://doi.org/10.3390/atmos15050613 - 19 May 2024
Abstract
Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable
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Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results.
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(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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About the Possible Solar Nature of the ~200 yr (de Vries/Suess) and ~2000–2500 yr (Hallstadt) Cycles and Their Influences on the Earth’s Climate: The Role of Solar-Triggered Tectonic Processes in General “Sun–Climate” Relationship
by
Boris Komitov
Atmosphere 2024, 15(5), 612; https://doi.org/10.3390/atmos15050612 - 19 May 2024
Abstract
(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and
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(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and the Holocene, as well as their forcing on the regional East European climate during the last two calendar millennia. The results obtained here are compared with those from our previous studies, as well as with the results obtained by other authors and with other types of data. A possible scenario of solar activity changes during the 21st century, as well as different possible mechanisms of solar–climatic relationships, is discussed. (2) Data and methods: Two types of indirect (historical) data series for solar activity were used: (a) the international radiocarbon tree ring series (INTCAL13) for the last 13,900 years; (b) the Schove series of the calendar years of minima and maxima and the magnitudes of 156 quasi 11 yr sunspot Schwabe–Wolf cycles since 296 AD and up to the sunspot cycle with number 24 (SC24) in the Zurich series; (c) manuscript messages about extreme meteorological and climatic events (Danube and Black Sea near-coast water freezing), extreme summer droughts, etc., in Bulgaria and adjacent territories since 296 and up to 1899 AD, when the Bulgarian meteorological dataset was started. A time series analysis and χ2-test were used. (3) Results and analysis: The amplitude modulation of the 200 yr solar cycle by the 2400 yr (Hallstadt) cycle was confirmed. Two groups of extremely cold winters (ECWs) during the last ~1700 years were established. Both groups without exclusion are concentrated near 11 yr sunspot cycle extremes. The number of ECWs near sunspot cycle minima is about 2 times greater than that of ECWs near sunspot cycle maxima. This result is in agreement with our earlier studies for the instrumental epoch since 1899 AD. The driest “spring-summer-early autumn” seasons in Bulgaria and adjacent territories occur near the initial and middle phases of the grand solar minima of the Oort–Dalton type, which relate to the downward phases and minima of the 200 yr Suess cycle. (4) Discussion: The above results confirm the effect of the Sun’s forcing on climate. However, it cannot be explained by the standard hypothesis for total solar irradiation (TSI) variations. That is why another hypothesis is suggested by the author. The mechanism considered by Svensmark for galactic cosmic ray (GCR) forcing on aerosol nuclei was taken into account. However, in the hypothesis suggested here, the forcing of solar X-ray flux changes (including solar flares) on the low ionosphere (the D-layer) and following interactions with the Earth’s lithosphere due to the terrestrial electric current systems play a key role for aerosol nuclei and cloud generation and dynamics during sunspot maxima epochs. The GCR flux maximum absorption layer at heights of 35–40 km replaces the ionosphere D-layer role during the sunspot minima epochs.
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(This article belongs to the Special Issue The Influence of Solar Cyclicity on the Earth’s Climate)
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Assessment of Indoor Radon Gas Concentration in Latvian Households
by
Jeļena Reste, Nadīna Rīmere, Andris Romans, Žanna Martinsone, Inese Mārtiņsone, Ivars Vanadziņš and Ilona Pavlovska
Atmosphere 2024, 15(5), 611; https://doi.org/10.3390/atmos15050611 - 18 May 2024
Abstract
Exposure to radon gas in households presents serious health risks, including an increased likelihood of lung cancer. Following the COVID-19 pandemic, the change in individual habits has led to more time spent in indoor environments with remote activities; thus, the need to raise
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Exposure to radon gas in households presents serious health risks, including an increased likelihood of lung cancer. Following the COVID-19 pandemic, the change in individual habits has led to more time spent in indoor environments with remote activities; thus, the need to raise the awareness of air quality in dwellings and to mitigate the exposure of inhabitants to radon has emerged. This study investigated radon gas concentrations in the air of Latvian dwellings. RadTrack2 passive detectors were deployed in a representative sample of households across 106 municipalities of Latvia (98% of the territory), yielding data from 487 households (973 detectors). The data revealed a median radon concentration of 52 Bq/m3 (Q1 and Q3 were 29 and 93 Bq/m3), with the majority of samples (95.6%) falling below the national reference limit of 200 Bq/m3. The building type and presence of a cellar significantly impacted radon levels, with structures lacking cellars and older buildings exhibiting higher concentrations. Mechanical ventilation proved to be more effective in reducing radon levels, compared to natural ventilation. These findings emphasize the necessity of proactive measures to mitigate indoor radon exposure and to ensure the well-being of occupants. Additionally, the dissemination of research data on radon exposure through open-access scientific publications is vital for raising awareness and implementing effective mitigation strategies.
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(This article belongs to the Special Issue Indoor Air Quality Control)
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