Atmosphere

30 pages, 7742 KiB

Open AccessArticle

Rainfall Enhancement Downwind of Hills Due to Stationary Waves on the Melting Level and the Extreme Rainfall of December 2015 in the Lake District of Northwest England

by Edward Carroll

Atmosphere 2024, 15(10), 1252; https://doi.org/10.3390/atmos15101252 (registering DOI) - 19 Oct 2024

Abstract

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity [...] Read more.

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity downwind of hills. This mechanism, which depends on the differential velocities of rain and snow, offers fresh insights into how orographic effects can intensify rainfall. A two-dimensional diagnostic model based on linear gravity wave theory is used to investigate the record-breaking rainfall of December 2015 in the Lake District of northwest England. The pattern of ascent is shown to have a qualitatively good fit to that of the Met Office’s operational high-resolution UKV model averaged over 24 h, suggesting that orographically excited stationary waves were the principal cause of the rain. Precipitation trajectories imply that a persistent downstream elevated wave caused by the Isle of Man supported a spray of seeding ice particles directed towards the Lake District, and that these grew whilst suspended in strong upslope flow before being focused by the undulating melting-level into intense shafts of rain. Full article

(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))

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21 pages, 11655 KiB

Open AccessArticle

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

by Jinzi Wang, Hengcai Zhang, Jianing Yu, Feng Lu and Yafei Li

Atmosphere 2024, 15(10), 1251; https://doi.org/10.3390/atmos15101251 (registering DOI) - 19 Oct 2024

Abstract

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the [...] Read more.

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields. Full article

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

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20 pages, 3602 KiB

Open AccessArticle

Machine Learning for Optimising Renewable Energy and Grid Efficiency

by Bankole I. Oladapo, Mattew A. Olawumi and Francis T. Omigbodun

Atmosphere 2024, 15(10), 1250; https://doi.org/10.3390/atmos15101250 (registering DOI) - 19 Oct 2024

Abstract

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the [...] Read more.

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO₂ emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets. Full article

(This article belongs to the Special Issue Air Quality and Energy Transition: Interactions and Impacts)

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16 pages, 6700 KiB

Open AccessArticle

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

by Ying Gao and Xiaolei Wang

Atmosphere 2024, 15(10), 1249; https://doi.org/10.3390/atmos15101249 (registering DOI) - 18 Oct 2024

Viewed by 305

Abstract

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response [...] Read more.

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends. Full article

(This article belongs to the Special Issue A Dynamical System for the Earth's Ionosphere—Space Weather through Complex System Science)

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24 pages, 6356 KiB

Open AccessArticle

The Evaluation of Global and Regional Applications of Modeling Platform Across Scales-Atmosphere (MPAS) Against Weather Research Forecast (WRF) Model over California for a Winter (2013 DISCOVER-AQ) and Summer (2016 CABOTS) Episode

by Kemal Gürer, Zhan Zhao, Chenxia Cai and Jeremy C. Avise

Atmosphere 2024, 15(10), 1248; https://doi.org/10.3390/atmos15101248 (registering DOI) - 18 Oct 2024

Viewed by 425

Abstract

The Modeling Platform Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus [...] Read more.

The Modeling Platform Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California. Full article

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

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14 pages, 2057 KiB

Open AccessReview

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

by Ge Qu, Yusheng Shi, Yongliang Yang, Wen Wu and Zhitao Zhou

Atmosphere 2024, 15(10), 1247; https://doi.org/10.3390/atmos15101247 - 18 Oct 2024

Viewed by 143

Abstract

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and [...] Read more.

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling)

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21 pages, 3364 KiB

Open AccessArticle

by Adama Faye, Georges A. Abbey, Amadou Ndiaye and Mbaye Diop

Atmosphere 2024, 15(10), 1246; https://doi.org/10.3390/atmos15101246 - 18 Oct 2024

Viewed by 194

Abstract

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a [...] Read more.

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions. Full article

(This article belongs to the Section Climatology)

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15 pages, 6060 KiB

Open AccessArticle

Patterns and Drivers of Greenhouse Gas Emissions in a Tropical Rubber Plantation from Hainan, Danzhou

by Siqi Yang, Yuanhong Xian, Wei Tang, Mengyang Fang, Bo Song, Qing Hu and Zhixiang Wu

Atmosphere 2024, 15(10), 1245; https://doi.org/10.3390/atmos15101245 - 18 Oct 2024

Viewed by 204

Abstract

The intensification of global climate change has made the study of greenhouse gas (GHG) emissions increasingly important. To gain a deeper understanding of the emission characteristics and driving factors of nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH [...] Read more.

The intensification of global climate change has made the study of greenhouse gas (GHG) emissions increasingly important. To gain a deeper understanding of the emission characteristics and driving factors of nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄) from rubber plantation soils, this study conducted a 16-month continuous observation in a rubber plantation in Danzhou, Hainan, employing the static chamber method for the monthly sampling and measurement of GHG emissions while analyzing the soil’s physical and chemical properties. The results indicated that the N₂O flux exhibited no significant diurnal variation between the dry and rainy seasons, with an average emission rate of 0.03 ± 0.002 mg·m⁻²·h⁻¹. A clear seasonal trend was observed, with higher emissions in summer than in winter, resulting in an annual flux of 3 kg·hm⁻²·a⁻¹ (equivalent to 1.9 kg N·hm⁻²·a⁻¹). N₂O emissions were significantly correlated with soil temperature and moisture, explaining 46% and 40% of the variations, respectively, while soil ammonium nitrogen content also significantly influenced N₂O and CO₂ emissions. The rubber plantation soil acted as a source of N₂O and CO₂ emissions and a sink for CH₂, with lower emissions of N₂O and CO₂ during the daytime compared to nighttime, and higher CH₄ uptake during the daytime. In the dry season, there was a significant positive correlation between N₂O and CO₂ emissions (R² = 0.74, p < 0.001). This study reveals the diurnal and seasonal patterns of GHG emissions from rubber plantation soils in Hainan and their interrelationships, providing a scientific basis for the low-carbon management of rubber plantations and GHG mitigation strategies, thereby contributing to attempts to reduce the impact of rubber cultivation on climate change. Full article

(This article belongs to the Special Issue Agriculture-Climate Interactions in Tropical Regions)

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24 pages, 8975 KiB

Open AccessArticle

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

by Richard L. Carpenter, Jr., Taylor A. Gowan, Samuel P. Lillo, Scott J. Strenfel, Arthur. J. Eiserloh, Jr., Evan J. Duffey, Xin Qu, Scott B. Capps, Rui Liu and Wei Zhuang

Atmosphere 2024, 15(10), 1244; https://doi.org/10.3390/atmos15101244 - 18 Oct 2024

Viewed by 327

Abstract

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) [...] Read more.

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system. Full article

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

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14 pages, 4266 KiB

Open AccessArticle

Drought Hazards and Hydrological Variations in the South Hebei Plain of China over the Past 500 Years

by Guifang Yang and Changhong Yao

Atmosphere 2024, 15(10), 1243; https://doi.org/10.3390/atmos15101243 - 17 Oct 2024

Viewed by 324

Abstract

High-frequency drought hazards have presented persistent challenges for environmental management and sustainable development in the South Hebei Plain, China. In this paper, the assessment of meteorological droughts in the South Hebei Plain was conducted using a multifaceted approach to ensure a comprehensive analysis. [...] Read more.

High-frequency drought hazards have presented persistent challenges for environmental management and sustainable development in the South Hebei Plain, China. In this paper, the assessment of meteorological droughts in the South Hebei Plain was conducted using a multifaceted approach to ensure a comprehensive analysis. Our results demonstrated that distinct timescale cycles, ranging from centennial–semicentennial to interdecadal variations, can be identified over the past few centuries. These cycles aligned with patterns observed in the middle Yangtze basin and corresponded to regional climatic conditions. The drought cycles in the South Hebei Plain showed significant correlations with variations in the monsoon climate, sunspot activity, global changes, and human disturbances. Changes in the frequency, duration, and intensity of droughts have notably impacted hydrological variations. Extreme droughts, in particular, have heightened concerns about their effects on river systems, potentially increasing the risk of channel migration. This study enhanced our understanding of meteorological hazard patterns in the South Hebei Plain and provided valuable insights into different stages of drought management. It thus can offer lessons for improving drought preparedness and resilience and for formulating adaptive measures to mitigate future droughts and promote regional sustainability. Full article

(This article belongs to the Special Issue Environmental Footprints of Drought: Focusing on Emerging Issues and Their Underlying Mechanisms (2nd Edition))

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24 pages, 6253 KiB

Open AccessArticle

WRF-ROMS-SWAN Coupled Model Simulation Study: Effect of Atmosphere–Ocean Coupling on Sea Level Predictions Under Tropical Cyclone and Northeast Monsoon Conditions in Hong Kong

by Ngo-Ching Leung, Chi-Kin Chow, Dick-Shum Lau, Ching-Chi Lam and Pak-Wai Chan

Atmosphere 2024, 15(10), 1242; https://doi.org/10.3390/atmos15101242 - 17 Oct 2024

Viewed by 239

Abstract

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other [...] Read more.

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other meteorological factors. By adding the sea level rise forecast to the astronomical tide prediction using the harmonic analysis method, coastal sea level prediction can be produced for the sites with tidal observations, which supports the high water level forecast operation and alert service for risk assessment of sea flooding in Hong Kong. The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System, which comprises the Weather Research and Forecasting (WRF) Model and Regional Ocean Modelling System (ROMS), which in itself is coupled with wave model WaveWatch III and nearshore wave model SWAN, was tested with tropical cyclone cases where there was significant water level rise in Hong Kong. This case study includes two super typhoons, namely Hato in 2017 and Mangkhut in 2018, three cases of the combined effect of tropical cyclone and northeast monsoon, including Typhoon Kompasu in 2021, Typhoon Nesat and Severe Tropical Storm Nalgae in 2022, as well as two cases of monsoon-induced sea level anomalies in February 2022 and February 2023. This study aims to evaluate the ability of the WRF-ROMS-SWAN model to downscale the meteorological fields and the performance of the coupled models in capturing the maximum sea levels under the influence of significant weather events. The results suggested that both configurations could reproduce the sea level variations with a high coefficient of determination (R²) of around 0.9. However, the WRF-ROMS-SWAN model gave better results with a reduced RMSE in the surface wind and sea level anomaly predictions. Except for some cases where the atmospheric model has introduced errors during the downscaling of the ERA5 dataset, bias in the peak sea levels could be reduced by the WRF-ROMS-SWAN coupled model. The study result serves as one of the bases for the implementation of the three-way coupled atmosphere–ocean–wave modelling system for producing an integrated forecast of storm surge or sea level anomalies due to meteorological factors, as well as meteorological and oceanographic parameters as an upgrade to the two-way coupled Operational Marine Forecasting System in the Hong Kong Observatory. Full article

(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)

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10 pages, 3980 KiB

Open AccessEditorial

Greece 2023: Crazy Summer or New Normal—Lessons Not Learned

by Andreas Matzarakis and Panagiotis Nastos

Atmosphere 2024, 15(10), 1241; https://doi.org/10.3390/atmos15101241 - 17 Oct 2024

Viewed by 200

Abstract

The year 2023 in Greece started with a mild winter and spring [...] Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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20 pages, 4907 KiB

Open AccessArticle

Phenolic and Acidic Compounds in Radiation Fog at Strasbourg Metropolitan

by Dani Khoury, Maurice Millet, Yasmine Jabali and Olivier Delhomme

Atmosphere 2024, 15(10), 1240; https://doi.org/10.3390/atmos15101240 - 17 Oct 2024

Viewed by 184

Abstract

Sixty-four phenols grouped as nitrated, bromo, amino, methyl, chloro-phenols, and cresols, and thirty-eight organic acids grouped as mono-carboxylic and dicarboxylic are analyzed in forty-two fog samples collected in the Alsace region between 2015 and 2021 to check their atmospheric behavior. Fogwater samples are [...] Read more.

Sixty-four phenols grouped as nitrated, bromo, amino, methyl, chloro-phenols, and cresols, and thirty-eight organic acids grouped as mono-carboxylic and dicarboxylic are analyzed in forty-two fog samples collected in the Alsace region between 2015 and 2021 to check their atmospheric behavior. Fogwater samples are collected using the Caltech Active Strand Cloudwater Collector (CASCC2), extracted using liquid–liquid extraction (LLE) on a solid cartridge (XTR Chromabond), and then analyzed using gas chromatography coupled with mass spectrometry (GC-MS). The results show the high capability of phenols and acids to be scavenged by fogwater due to their high solubility. Nitro-phenols and mono-carboxylic acids have the highest contributions to the total phenolic and acidic concentrations, respectively. 2,5-dinitrophenol, 3-methyl-4-nitrophenol, 4-nitrophenol, and 3,4-dinitrophenol have the highest concentration, originating mainly from vehicular emissions and some photochemical reactions. The top three mono-carboxylic acids are hexadecenoic acid (C16), eicosanoic acid (C18), and dodecanoic acid (C12), whereas succinic acid, suberic acid, sebacic acid, and oxalic acid are the most concentrated dicarboxylic acids, originated either from atmospheric oxidation (mainly secondary organic aerosols (SOAs)) or vehicular transport. Pearson’s correlations show positive correlations between organic acids and previously analyzed metals (p < 0.05), between mono- and dicarboxylic acids (p < 0.001), and between the analyzed acidic compounds (p < 0.001), whereas no correlations are observed with previously analyzed inorganic ions. Total phenolic and acidic fractions are found to be much higher than those observed for pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) measured at the same region due to their higher scavenging by fogwater. Full article

(This article belongs to the Section Meteorology)

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17 pages, 5177 KiB

Open AccessArticle

A Branched Convolutional Neural Network for Forecasting the Occurrence of Hazes in Paris Using Meteorological Maps with Different Characteristic Spatial Scales

by Chien Wang

Atmosphere 2024, 15(10), 1239; https://doi.org/10.3390/atmos15101239 - 17 Oct 2024

Viewed by 261

Abstract

A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The [...] Read more.

A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The strategy is to make the machine learn from available historical data to recognize various regional weather and hydrological regimes associated with low-visibility events. To better preserve the characteristic spatial information of input features in training, two branched architectures have recently been developed. These architectures process input features firstly through several branched CNNs with different kernel sizes to better preserve patterns with certain characteristic spatial scales. The outputs from the first part of the network are then processed by the second part, a deep non-branched CNN, to further deliver predictions. The CNNs with new architectures have been trained using data from 1975 to 2019 in a two-class (haze versus non-haze) classification mode as well as a regression mode that directly predicts the value of surface visibility. The predictions of regression have also been used to perform the two-class classification forecast using the same definition in the classification mode. This latter procedure is found to deliver a much better performance in making class-based forecasts than the direct classification machine does, primarily by reducing false alarm predictions. The branched architectures have improved the performance of the networks in the validation and also in an evaluation using the data from 2021 to 2023 that have not been used in the training and validation. Specifically, in the latter evaluation, branched machines captured 70% of the observed low-visibility events during the three-year period at Charles de Gaulle Airport. Among those predicted low-visibility events by the machines, 74% of them are true cases based on observation. Full article

(This article belongs to the Special Issue Data Analysis in Atmospheric Research)

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11 pages, 2132 KiB

Open AccessArticle

The Single-Scattering Albedo of Black Carbon Aerosols in China

by Xiaolin Zhang and Yuanyuan Wu

Atmosphere 2024, 15(10), 1238; https://doi.org/10.3390/atmos15101238 - 16 Oct 2024

Viewed by 288

Abstract

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the [...] Read more.

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the MERRA-2 satellite reanalysis data from 1983 to 2022 in terms of the spatial, yearly, seasonal, and monthly variations. The extinction and scattering AOTs of BC show similar spatial distribution, with high values in eastern and southern China, generally as opposed to BC SSA. A decrease in BC extinction and scattering AOTs has been documented over the last decade. The mean BC extinction AOT, scattering AOT, and SSA over China are 0.0054, 0.0014, and 0.26, respectively. The BC SSA showed small variations during 1983–2022, although a high BC extinction AOT and scattering AOT have been seen in the last two decades. During different decades, the seasonal patterns of BC extinction and scattering AOTs may differ, whereas the BC SSA shows seasonal consistency. Significant monthly variations in the BC SSA are seen over four decades, which are in agreement with their seasonal patterns. The mean BC extinction AOTs are 0.037, 0.033, 0.023, and 0.0054, whereas the average BC scattering AOTs are 0.0088, 0.0082, 0.0060, and 0.0014 in the Pearl River Delta (PRD), Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH) region, and Tarim Basin (TB), respectively. It is interesting to see that BC SSA values in the TB region are generally higher than those over the PRD, YRD and BTH areas, whereas the reverse is true for BC extinction and scattering AOTs. This study provides references for further research on black carbon aerosols and air pollution in China. Full article

(This article belongs to the Special Issue Atmospheric Black Carbon: Monitoring and Assessment)

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18 pages, 14457 KiB

Open AccessArticle

Variations of Planetary Wave Activity in the Lower Stratosphere in February as a Predictor of Ozone Depletion in the Arctic in March

by Pavel Vargin, Andrey Koval, Vladimir Guryanov, Eugene Volodin and Eugene Rozanov

Atmosphere 2024, 15(10), 1237; https://doi.org/10.3390/atmos15101237 - 16 Oct 2024

Viewed by 310

Abstract

This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere [...] Read more.

This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere in March (Tmin) and the amplitude of the planetary wave with zonal number 1 (PW1) in February were calculated. Tmin determines the conditions for the formation of polar stratospheric clouds (PSCs) following the chemical destruction of the ozone layer. The NCEP and ERA5 reanalysis data and the modern and future climate simulations of the Earth system models INM CM5 and SOCOLv4 were employed. It is shown that the maximum significant CC value between Tmin at 70 hPa in the polar region in March and the amplitude of the PW1 in February in the reanalysis data in the lower stratosphere is 0.67 at the pressure level of 200 hPa. The CCs calculated using the model data are characterized by maximum values of ~0.5, also near the same pressure level. Thus, it is demonstrated that the change in the planetary wave activity in the lower extratropical stratosphere in February can be one of the predictors of the Tmin. For further analysis of the dynamic structure in the lower stratosphere, composites of 10 seasons with the lowest and highest Tmin of the Arctic lower stratosphere in March were assembled. For these composites, differences in the vertical distribution and total ozone content, surface temperature, and residual meridional circulation (RMC) were considered, and features of the spatial distribution of wave activity fluxes were investigated. The obtained results may be useful for the development of forecasting of the Arctic winter stratosphere circulation, especially for the late winter season, when substantial ozone depletion occurs in some years. Full article

(This article belongs to the Special Issue Measurement and Variability of Atmospheric Ozone)

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15 pages, 4580 KiB

Open AccessArticle

A Study on the Pre-Survey and Plan for the Establishment of the Korean Typhoon Impact-Based Forecast

by Hana Na and Woo-Sik Jung

Atmosphere 2024, 15(10), 1236; https://doi.org/10.3390/atmos15101236 - 16 Oct 2024

Viewed by 315

Abstract

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean [...] Read more.

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean Peninsula originate and develop. The SST in these regions is increasing at a faster rate than the global average. Typhoon-related meteorological disasters are not isolated events, such as strong winds, heavy rains, or storm surges, but rather multi-hazard occurrences that can affect different areas simultaneously. As a result, preparation and evaluation must address multi-hazard disasters, rather than focusing on individual weather phenomena. This study develops the Typhoon Ready System (TRS) to improve impact-based forecasting in Korea, in response to the growing threat of multi-hazard weather disasters. By providing region-specific pre-disaster information, the TRS enables local governments and individuals to better prepare for and mitigate the impacts of typhoons. The system will be continuously refined in collaboration with the U.S. Weather-Ready Nation (WRN), which possesses advanced impact forecasting capabilities. The findings of this study offer a crucial framework for enhancing Korea’s ability to forecast and respond to the escalating threats posed by typhoons. By utilizing the TRS, it will be possible to assess the risks of various multi-hazard weather disasters specific to each region during the typhoon forecast period, and the relevant data can be efficiently applied at both the individual and local government levels for typhoon prevention efforts. The system will be continuously improved through cooperation with the U.S. WRN, leveraging their advanced impact forecasting systems. It is expected that the TRS will enhance the accuracy of typhoon impact forecasts, which have been responsible for significant damage in Korea. Full article

(This article belongs to the Special Issue Advances in Tropical Cyclone Prediction: Observation, Simulation, and Verification)

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21 pages, 7736 KiB

Open AccessArticle

Carbonyl Compounds Observed at a Suburban Site during an Unusual Wintertime Ozone Pollution Event in Guangzhou

by Aoqi Ge, Zhenfeng Wu, Shaoxuan Xiao, Xiaoqing Huang, Wei Song, Zhou Zhang, Yanli Zhang and Xinming Wang

Atmosphere 2024, 15(10), 1235; https://doi.org/10.3390/atmos15101235 - 16 Oct 2024

Viewed by 265

Abstract

Carbonyl compounds are important oxygenated volatile organic compounds (VOCs) that play significant roles in the formation of ozone (O₃) and atmospheric chemistry. This study presents comprehensive field observations of carbonyl compounds during an unusual wintertime ozone pollution event at a suburban [...] Read more.

Carbonyl compounds are important oxygenated volatile organic compounds (VOCs) that play significant roles in the formation of ozone (O₃) and atmospheric chemistry. This study presents comprehensive field observations of carbonyl compounds during an unusual wintertime ozone pollution event at a suburban site in Guangzhou, South China, from 19 to 28 December 2020. The aim was to investigate the characteristics and sources of carbonyls, as well as their contributions to O₃ formation. Formaldehyde, acetone, and acetaldehyde were the most abundant carbonyls detected, with average concentrations of 7.11 ± 1.80, 5.21 ± 1.13, and 3.00 ± 0.94 ppbv, respectively, on pollution days, significantly higher than those of 2.57 ± 1.12, 2.73 ± 0.88, and 1.10 ± 0.48 ppbv, respectively, on nonpollution days. The Frame for 0-D Atmospheric Modeling (F0AM) box model simulations revealed that local production accounted for 62–88% of observed O₃ concentrations during the pollution days. The calculated ozone formation potentials (OFPs) for various precursors (carbonyls and VOCs) indicated that carbonyl compounds contributed 32.87% of the total OFPs on nonpollution days and 36.71% on pollution days, respectively. Formaldehyde, acetaldehyde, and methylglyoxal were identified as the most reactive carbonyls, and formaldehyde ranked top in OFPs, and it alone contributed 15.92% of total OFPs on nonpollution days and 18.10% of total OFPs on pollution days, respectively. The calculation of relative incremental reactivity (RIR) indicates that ozone sensitivity was a VOC-limited regime, and carbonyls showed greater RIRs than other groups of VOCs. The model simulation showed that secondary formation has a significant impact on formaldehyde production, which is primarily controlled by alkenes and biogenic VOCs. The characteristic ratios and backward trajectory analysis also indicated the indispensable impacts of local primary sources (like industrial emissions and vehicle emissions) and regional sources (like biomass burning) through transportation. This study highlights the important roles of carbonyls, particularly formaldehyde, in forming ozone pollution in megacities like the Pearl River Delta region. Full article

(This article belongs to the Section Air Quality)

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16 pages, 2895 KiB

Open AccessArticle

Accuracy Assessment of NOAA IMS 4 km Products on the Tibetan Plateau with Landsat-8 OLI Images

by Duo Chu

Atmosphere 2024, 15(10), 1234; https://doi.org/10.3390/atmos15101234 - 15 Oct 2024

Viewed by 322

Abstract

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and [...] Read more.

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area. Full article

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

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16 pages, 6843 KiB

Open AccessArticle

Seasonal–Diurnal Distribution of Lightning over Bulgaria and the Black Sea and Its Relationship with Sea Surface Temperature

by Savka Petrova, Rumjana Mitzeva, Vassiliki Kotroni and Elisaveta Peneva

Atmosphere 2024, 15(10), 1233; https://doi.org/10.3390/atmos15101233 - 15 Oct 2024

Viewed by 206

Abstract

A seasonal–diurnal analysis of land-sea contrast in lightning activity over Bulgaria and the Black Sea over 10 years is presented here. The maximum number of flashes over both surface types is registered during the summer (with a peak over Bulgaria in June and [...] Read more.

A seasonal–diurnal analysis of land-sea contrast in lightning activity over Bulgaria and the Black Sea over 10 years is presented here. The maximum number of flashes over both surface types is registered during the summer (with a peak over Bulgaria in June and over the Black Sea in July) and a minimum number in winter (December/February, respectively). During spring, the maximum flash density is observed over Bulgaria (in May), while in autumn, it is over the Black Sea (in September). The results show that only in autumn lightning activity dominates over the Black Sea compared to over land (Bulgaria), while in winter, spring, and summer is vice versa. For this reason, an additional investigation was conducted to determine whether there is a relationship between lightning activity and the sea surface temperature (SST) of the Black Sea in autumn. The analysis reveals that the influence of SST on the formation of thunderstorms over the Black Sea varies depending on the diurnal time interval, with the effect being more significant at night. At nighttime intervals, there is a clear trend of increasing mean flash frequency per case with rising SST (linear correlation coefficients range from R = 0.92 to 0.98), while during the daytime, this trend is not as evident. This indicates that, during the day, other favorable atmospheric processes have a greater influence on the formation of thunderstorms than sea-surface temperature, while in the autumn night hours, the higher SST values probably play a more significant role in thunderstorms formation, in combination with the corresponding orographic conditions. Full article

(This article belongs to the Special Issue Atmospheric Electricity (2nd Edition))

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13 pages, 471 KiB

Open AccessArticle

Implications of Traditional Cooking on Air Quality and Female Health: An In-Depth Analysis of Particulate Matter, Carbon Monoxide, and Carbon Dioxide Exposure in a Rural Community

by Kenia González-Pedraza, Arturo Figueroa-Montaño, Martha Orozco-Medina, Felipe Lozano-Kasten and Valentina Davydova Belitskaya

Atmosphere 2024, 15(10), 1232; https://doi.org/10.3390/atmos15101232 - 15 Oct 2024

Viewed by 392

Abstract

Indoor air pollution, particularly in rural communities, is a significant health determinant, primarily due to the prevalence of traditional cooking practices. The WHO estimates 4.3 million annual deaths related to household air pollution. This study quantifies indoor pollutants and assesses health impacts and [...] Read more.

Indoor air pollution, particularly in rural communities, is a significant health determinant, primarily due to the prevalence of traditional cooking practices. The WHO estimates 4.3 million annual deaths related to household air pollution. This study quantifies indoor pollutants and assesses health impacts and perceptions regarding traditional cooking. Using Extech air quality monitoring equipment, the study measured particulate matter (PM), carbon monoxide (CO), and carbon dioxide (CO₂) in 48 rural homes. A survey of 39 women gathered insights on their use of wood for cooking and perceptions of air quality. This dual approach analyzed both environmental and social dimensions. Findings showed fine particulate matter (0.3, 0.5, 1.0, and 2.5 μm) exceeded safety limits by threefold, while coarser particulates (5.0 and 10 µm) were concerning but less immediate. CO levels were mostly acceptable, but high concentrations posed risks. CO₂ levels indicated good ventilation. Survey responses highlighted reliance on wood and poor air quality perceptions demonstrating little awareness of health risks. Common symptoms included eye discomfort, respiratory issues, and headaches. The study emphasizes the need for interventions to reduce exposure to indoor pollutants and increase awareness of health risks to encourage cleaner cooking practices in rural communities. Full article

(This article belongs to the Special Issue Exposure Assessment of Air Pollution (2nd Edition))

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11 pages, 3547 KiB

Open AccessArticle

Influence of the Thickness of Freezing of the Soil Surface and Snow Cover on Methane Emissions during Freezing of Seasonal Permafrost

by Chenzheng Li, Anatoly V. Brouchkov, Viktor G. Cheverev, Andrey V. Sokolov and Bicheng Zhou

Atmosphere 2024, 15(10), 1231; https://doi.org/10.3390/atmos15101231 - 15 Oct 2024

Viewed by 290

Abstract

Methane, a type of greenhouse gas, poses considerable concern for humans. This study uses field experiments and satellite measurements to explore methane emission mechanisms during the freezing of seasonal permafrost and the contributing factors. In the transitional seasons of autumn and winter, as [...] Read more.

Methane, a type of greenhouse gas, poses considerable concern for humans. This study uses field experiments and satellite measurements to explore methane emission mechanisms during the freezing of seasonal permafrost and the contributing factors. In the transitional seasons of autumn and winter, as soil begins to freeze, methane emissions surge dramatically in a brief period. During this phase, the emissions peak, enabling the soil to accumulate over 9000 mg/m³ of methane rapidly. Snow cover also plays a crucial role in mitigating methane emissions. The porous nature of a sufficiently thick snow cover aids in temporarily trapping methane through a stratified blocking process, effectively matching the inhibitory capability of unfrozen soil. In comparison to unfrozen soil (54–237 mg/m³), snow cover can suppress methane emissions up to 20 times more, reducing emissions by as much as 3399 mg/m³. Full article

(This article belongs to the Special Issue Gas Emissions from Soil)

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22 pages, 3270 KiB

Open AccessArticle

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

by Marina Tautan, Maria Zoran, Roxana Radvan, Dan Savastru, Daniel Tenciu and Alexandru Stanciu

Atmosphere 2024, 15(10), 1230; https://doi.org/10.3390/atmos15101230 - 15 Oct 2024

Viewed by 386

Abstract

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, [...] Read more.

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O₃), nitrogen dioxide (NO₂), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO₂), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO₂), AQI, and AOD but a high increase in ground-level O₃ in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. Full article

(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)

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21 pages, 5946 KiB

Open AccessArticle

A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0)

by Jianbin Zhang, Meng Yin, Pu Wang and Zhiqiu Gao

Atmosphere 2024, 15(10), 1229; https://doi.org/10.3390/atmos15101229 - 15 Oct 2024

Viewed by 399

Abstract

In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism [...] Read more.

In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism (AM). The CNN is tasked with extracting features from the input data, while the BiLSTM effectively captures temporal dependencies. The AM enhances the results by considering the impact of past feature states on severe weather phenomena. Additionally, we assess the performance of our model in comparison to traditional network architectures, including ConvLSTM, Predrnn++, CNN, FC-LSTM, and LSTM. Our results indicate that the CNN-BiLSTM-AM model exhibits superior accuracy in precipitation forecasting. Especially with the extension of the forecast time, the model performs well across multiple evaluation metrics. Furthermore, an interpretability analysis of the convective weather mechanisms utilizing machine learning highlights the critical role of total precipitable water (PWAT) in short-term heavy precipitation forecasts. It also emphasizes the significant impact of regional variables on convective weather patterns and the role of convective available potential energy (CAPE) in fostering conditions conducive to convection. These findings not only confirm the effectiveness of deep learning in the automatic identification of severe weather features but also validate the suitability of the sample dataset employed. Given its remarkable performance and robustness, we advocate for the adoption of this model to enhance the forecast of severe convective weather across various business applications. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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13 pages, 31428 KiB

Open AccessArticle

Inter- and Intra-Annual Variations in Oak Tree Ring δ¹³C Values across Different Elevations and Their Climatic Responses in Qinling Mountains

by Chao Li, Rong Fan, Weilong Jiang, Hang Zhang, Xin Li, Feiyu Chen and Huan Zhang

Atmosphere 2024, 15(10), 1228; https://doi.org/10.3390/atmos15101228 - 15 Oct 2024

Viewed by 362

Abstract

The Qinling Mountains, serving as a natural geographical and climatic boundary in China, require comprehensive climatic records to elucidate the trends in climate changes across the country. While stable isotopes in tree rings are widely employed to indicate historical environmental changes, investigations into [...] Read more.

The Qinling Mountains, serving as a natural geographical and climatic boundary in China, require comprehensive climatic records to elucidate the trends in climate changes across the country. While stable isotopes in tree rings are widely employed to indicate historical environmental changes, investigations into tree ring isotopes in the Qinling Mountains, particularly within the widespread broad-leaf oaks, remain limited. In this study, we investigated both intra- and inter-annual variations in the δ¹³C values of tree rings and their correlations with climatic signals over the past two decades for Quercus aliena var. acuteserrata, a dominant species among oak trees on the main peak of the Qinling Mountains. Our results reveal that responses to climate differ among altitudes and individual trees, with trees at higher altitudes exhibiting higher sensitivity to extreme climate, which is low temperatures and rainfall fluctuations during the growth period in intra-annual δ¹³C variations. Furthermore, the positive correlations are observed between temperature during growing season and both tree growth and the inter-annual δ¹³C variations. However, the climate signal appears to be hampered by oak-specific factors, such as intense competition among individuals and the age of trees. Therefore, we suggest a more rigorous selection of sampling and propose further investigations into isotopic fractionation processes in oaks for future studies. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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14 pages, 5405 KiB

Open AccessArticle

Characterization of the Major Elements and Paleoenvironmental Significance of the Shiyang Profile in the Weinan Basin, China

by Jing Cao, Qiuyao Dong, Chengxiang Li, Pan Wang, Zhenjing Yang and Hongyun Chen

Atmosphere 2024, 15(10), 1227; https://doi.org/10.3390/atmos15101227 - 14 Oct 2024

Viewed by 349

Abstract

The enrichment and migration patterns of different chemical elements record paleoclimatic information in loess formations. The chemical elemental measurements of 245 samples from the Shiyang profile in the Weinan Region were compared with the geochemical characteristics of typical wind-formed profiles, and the paleoclimatic [...] Read more.

The enrichment and migration patterns of different chemical elements record paleoclimatic information in loess formations. The chemical elemental measurements of 245 samples from the Shiyang profile in the Weinan Region were compared with the geochemical characteristics of typical wind-formed profiles, and the paleoclimatic evolution was discussed. The results showed the following: (1) the standardized curves of the cumulative concentrations of SiO₂, Al₂O_3, and CaO along with the Upper Continental Crust (UCC) in the Shiyang profile exhibited significant similarities with typical wind-formed profiles. This strongly suggests that the Shiyang profile has a wind-formed origin. (2) The mean value of the chemical index of alteration (CIA) of the Shiyang profile is 62.06, indicating that the Shiyang profile has been in the stage of primary chemical weathering. (3) The ratios of K₂O/Al₂O₃, TiO₂/Al₂O₃, and Fe₂O₃/Al₂O₃ in the Shiyang profile are comparable to those found in typical wind-formed profiles, suggesting a common source area and supporting the premise that the Shiyang profile is of wind-induced origin.(4) The regional climate has undergone a series of transitions: from a dry and cool phase in the early Holocene to warm and humid yet unstable conditions in the middle Holocene, and returning to dry and cool during the late Holocene. Full article

(This article belongs to the Topic Environmental Change, Geomorphological and Sedimentological Processes in Asian Hinterlands)

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27 pages, 7003 KiB

Open AccessArticle

Resonant Forcing by Solar Declination of Rossby Waves at the Tropopause and Implications in Extreme Precipitation Events and Heat Waves—Part 2: Case Studies, Projections in the Context of Climate Change

by Jean-Louis Pinault

Atmosphere 2024, 15(10), 1226; https://doi.org/10.3390/atmos15101226 - 14 Oct 2024

Viewed by 309

Abstract

Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only [...] Read more.

Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only form under specific patterns of the amplitude of the speed of modulated airflows of Rossby waves at the tropopause, in particular period ranges. This remains true even if extreme events appear as compound events where chaos and timing are crucial. Extreme events are favored when modulated cold and warm airflows result in a dual cyclone-anticyclone system, i.e., the association of two joint vortices of opposite signs. They reverse over a period of the dominant harmonic mode in spatial and temporal coherence with the modulated airflow speed pattern. This key role could result from a transfer of humid/dry air between the two vortices during the inversion of the dual system. Finally, focusing on the two period ranges 17.1–34.2 and 8.56–17.1 days corresponding to 1/16- and 1/32-year period harmonic modes, projections of the amplitude of wind speed at 250 mb, geopotential height at 500 mb, ground air temperature, and precipitation rate are performed by extrapolating their amplitude observed from January 1979 to March 2024. Projected amplitudes are regionalized on a global scale for warmest and coldest half-years, referring to extratropical latitudes. Causal relationships are established between the projected amplitudes of modulated airflow speed and those of ground air temperature and precipitation rate, whether they increase or decrease. The increase in the amplitude of modulated airflow speed of polar vortices induces their latitudinal extension. This produces a tightening of Rossby waves embedded in the polar and subtropical jet streams. In the context of climate change, this has the effect of increasing the efficiency of the resonant forcing of Rossby waves from the solar declination, the optimum of which is located at mid-latitudes. Hence the increased or decreased vulnerability to heatwaves or extreme precipitation events of some regions. Europe and western Asia are particularly affected, which is due to increased activity of the Arctic polar vortex between longitudes 20° W and 40° E. This is likely a consequence of melting ice and changing albedo, which appears to amplify the amplitude of variation in the period range 17.1–34.2 days of poleward circulation at the tropopause of the Arctic polar cell. Full article

(This article belongs to the Special Issue Climate Change and Climate Variability, and Their Impact on Extreme Events (2nd Edition))

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16 pages, 3322 KiB

Open AccessArticle

The Non-Monotonic Response of Cumulus Congestus to the Concentration of Cloud Condensation Nuclei

by Xin Deng, Shizuo Fu and Huiwen Xue

Atmosphere 2024, 15(10), 1225; https://doi.org/10.3390/atmos15101225 - 14 Oct 2024

Viewed by 278

Abstract

This study uses idealized simulations to investigate the impact of cloud condensation nuclei (CCN) on a cumulus congestus. Thirteen cases with the initial CCN_C, which is the CCN concentration at 1% supersaturation with respect to water, from 10 to 10,000 cm⁻³ [...] Read more.

This study uses idealized simulations to investigate the impact of cloud condensation nuclei (CCN) on a cumulus congestus. Thirteen cases with the initial CCN_C, which is the CCN concentration at 1% supersaturation with respect to water, from 10 to 10,000 cm⁻³ are simulated. The analysis focuses on the liquid phase due to the negligible ice phase in this study. A non-monotonic response of cloud properties and precipitation to CCN concentration is observed. When CCN_C is increased from 10 to 50 cm⁻³, the enhanced condensation due to the more numerous droplets invigorates the cumulus congestus. The delayed precipitation formation due to the smaller droplets also facilitates the cloud development. The two processes together lead to a higher liquid water path (LWP), higher cloud top, and heavier precipitation. The cumulus congestus has the highest cloud top, the strongest updraft, and the most accumulated precipitation and at CCN_C = 50 cm⁻³. When CCN_C is increased from 50 to 500 cm⁻³, the condensation near the cloud base is further enhanced and the precipitation is further delayed, both of which lead to more liquid water remaining in the cloud, and thus an even higher LWP and heavier precipitation rate in the later stage. However, the significantly enhanced evaporation near the cloud top limits the vertical development of the cumulus congestus, leading to a lower cloud top. When CCN_C is further increased to be higher than 1000 cm⁻³, the cumulus congestus is strongly suppressed, and no precipitation forms. The ratio of the precipitation production rate to vertical cloud water flux in the updraft is not a constant, as is generally assumed in cumulus parameterization schemes, but decreases significantly with increasing CCN concentration. It is also found that the CCN effect on the cumulus congestus relies on which parameters are used to describe the cloud strength. In this study, as CCN_C increases, the LWP and the maximum precipitation rate peak at CCN_C = 500 cm⁻³, while the cloud top height, maximum updraft, and accumulated precipitation amount peak at CCN_C = 50 cm⁻³. Full article

(This article belongs to the Section Aerosols)

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23 pages, 8735 KiB

Open AccessArticle

Fossil Diesel, Soybean Biodiesel and Hydrotreated Vegetable Oil: A Numerical Analysis of Emissions Using Detailed Chemical Kinetics at Diesel Engine Like Conditions

by Leonel R. Cancino, Jessica F. Rebelo, Felipe da C. Kraus, Eduardo H. de S. Cavalcanti, Valéria S. de B. Pimentel, Decio M. Maia and Ricardo A. B. de Sá

Atmosphere 2024, 15(10), 1224; https://doi.org/10.3390/atmos15101224 - 14 Oct 2024

Viewed by 382

Abstract

Nowadays, emissions from internal combustion engines are a relevant topic of investigation, taking into account the continuous reduction of emission limits imposed by environmental regulatory agencies around the world, obviously as the result of earnest studies that have pointed out the impact on [...] Read more.

Nowadays, emissions from internal combustion engines are a relevant topic of investigation, taking into account the continuous reduction of emission limits imposed by environmental regulatory agencies around the world, obviously as the result of earnest studies that have pointed out the impact on the human health of high levels of contaminants released into the environment. Over recent years, the use of biofuels has contributed to attenuating this environmental issue; however, new problems have been raised, such as NOx emissions tend to increase as the biofuel percentage in the fuel used in engines increases. In this research, the emissions of a compression ignition internal combustion engine modeled as a variable volume reactor with homogeneous combustion were numerically investigated. To analyze the combustion process, a detailed kinetics model tailored specifically for this purpose was used. The kinetics model comprised 30,975 chemical reactions involving 691 chemical species. Mixtures of fuel surrogates were then created to represent the fuel used in the Brazilian fuel marketplace, involving (i) fossil diesel—“diesel A”, (ii) soybean diesel—“biodiesel”, and (iii) hydrotreated vegetable oil— “HVO”. Surrogate species were then selected for each of the aforementioned fuels, and blends of those surrogates were then proposed as mixture M1 (diesel A:biodiesel:HVO—90:10:0), mixture M2 (diesel A:biodiesel:HVO—85:15:0), and mixture M3 (diesel A:biodiesel:HVO—80:15:5). The species allowed in the kinetics model included all the fuel surrogates used in this research as well as the target emission species of this study: total hydrocarbons, non-methane hydrocarbons, carbon monoxide, methane, nitrogen oxides, carbon dioxide, soot, and soot precursors. When compared to experimental trends of emissions available in the literature, it was observed that, for all the proposed fuel surrogates blends, the numerical approach performed in this research was able to capture qualitative trends for engine power and the target emissions in the whole ranges of engine speeds and engine loads, despite the CO and NOx emissions at specific engine speeds and loads. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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19 pages, 294 KiB

Open AccessArticle

Economic Assessment of Meteorological Information Services for Capture Fisheries in Taiwan

by Hen-I Lin, Sheng-Jang Sheu, Chu-Wei Chen, Fang-I Wen, Chin-Wen Yang, Je-Liang Liou, Meng-Wei Chen, Jen-Hung Hsu and Yu-Chieh Chang

Atmosphere 2024, 15(10), 1223; https://doi.org/10.3390/atmos15101223 - 14 Oct 2024

Viewed by 436

Abstract

This study evaluates the economic benefits of meteorological information services (MISs) provided by the Central Weather Administration (CWA) in Taiwan, specifically for Taiwan’s capture fishery industry. Using the contingent valuation method (CVM) and conducting in-person interviews, we collected questionnaires from capture fishery practitioners [...] Read more.

This study evaluates the economic benefits of meteorological information services (MISs) provided by the Central Weather Administration (CWA) in Taiwan, specifically for Taiwan’s capture fishery industry. Using the contingent valuation method (CVM) and conducting in-person interviews, we collected questionnaires from capture fishery practitioners nationwide to gather their subjective evaluations of the meteorological information services provided by the CWA. Based on these evaluations, we further investigated the respondents’ willingness to pay (WTP) for the CWA’s meteorological information services. An empirical analysis of the bid function was conducted to identify the key factors influencing the respondents’ bidding behavior. The empirical findings indicate that the primary factors affecting bids include subjective perceptions (such as forecast accuracy ratings and the ratings of the impacts on fishing production), working location, fishing vessel tonnage, and fishing methods. The median WTP for Taiwanese capture fishery practitioners is Taiwan dollar (TWD) 2111.12 per person per year. Based on the number of capture fishery practitioners in Taiwan in 2019, the total annual economic value of applying MIS in coastal and offshore fisheries is estimated to be between TWD 376 million and TWD 496 million per year. Full article

(This article belongs to the Section Meteorology)

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Atmosphere, Volume 15, Issue 10 (October 2024) – 108 articles

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