Atmosphere

22 pages, 1684 KiB

Open AccessArticle

Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan

by Karan Nayak, Rosendo Romero-Andrade, Gopal Sharma, Charbeth López-Urías, Manuel Edwiges Trejo-Soto and Ana Isela Vidal-Vega

Atmosphere 2024, 15(12), 1492; https://doi.org/10.3390/atmos15121492 (registering DOI) - 14 Dec 2024

Abstract

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and [...] Read more.

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts. Full article

(This article belongs to the Special Issue Electromagnetic Observations and Their Applications in Earthquake Research)

30 pages, 5261 KiB

Open AccessArticle

An Investigation of the SOCOLv4 Model’s Suitability for Predicting the Future Evolution of the Total Column Ozone

by Georgii Nerobelov, Yurii Timofeyev, Alexander Polyakov, Yana Virolainen, Eugene Rozanov and Vladimir Zubov

Atmosphere 2024, 15(12), 1491; https://doi.org/10.3390/atmos15121491 (registering DOI) - 14 Dec 2024

Abstract

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. [...] Read more.

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities. Full article

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

25 pages, 1625 KiB

Open AccessArticle

The Impact of Ecological Governance Projects on Regional Ecological Carrying Capacity Under Climate Change

by Shaobo Liu

Atmosphere 2024, 15(12), 1490; https://doi.org/10.3390/atmos15121490 - 13 Dec 2024

Abstract

Ecological governance projects have great potential benefits, but there is a lack of quantitative evaluation of their impacts in terms of enhancing regional ecological carrying capacity under climate change. To quantitatively evaluate the impact of ecological governance projects on regional ecological carrying capacity, [...] Read more.

Ecological governance projects have great potential benefits, but there is a lack of quantitative evaluation of their impacts in terms of enhancing regional ecological carrying capacity under climate change. To quantitatively evaluate the impact of ecological governance projects on regional ecological carrying capacity, a quantitative evaluation model was developed by coupling the classical ecological footprint and ecological service value theory. This model was validated using the water source treatment project (hereinafter referred to as the “DZ” project) of the Middle Route of China’s South to North Water Diversion Project, which is the world’s largest water diversion project, as an example. The results showed the following: (1) During the implementation of the “DZ” project, the per capita ecological carrying capacity of the reservoir area experienced a wave-like growth trend, with an increase of 0.103615 hm² and a yield increase rate of 20.00%. The “DZ” project has outstanding ecological benefits, valued at approximately USD 125.272266 million. (2) The “DZ” project has contributed to the improvement of the ecological carrying capacity in the Henan area of the Danjiang Reservoir by about 10.14%, demonstrating that such projects have a considerable impact on efforts to improve regional ecological carrying capacity under climate change. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events (2nd Edition))

19 pages, 20601 KiB

Open AccessArticle

The Influence of Climate Change and Socioeconomic Transformations on Land Use and NDVI in Ordos, China

by Yin Cao, Zhigang Ye and Yuhai Bao

Atmosphere 2024, 15(12), 1489; https://doi.org/10.3390/atmos15121489 - 13 Dec 2024

Abstract

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and [...] Read more.

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and analysis platform, was used to realize the combination of Landsat TM/OLI data images with spectral features and topographic features, and the random forest machine learning classification method was used to supervise and classify the low-cloud composite image data of Ordos City. The results show that: (1) GEE has a powerful computing function, which can realize efficient and high-precision in-depth analysis of long-term multi-temporal remote sensing images and monitoring of land use change, and the accuracy of acquisition can reach 87%. Compared with other data sets in the same period, the overall and local classification results are more distinct than ESRI (Environmental Systems Research Institute) and GlobeLand 30 data products. Slightly lower than the Institute of Aerospace Information Innovation of the Chinese Academy of Sciences to obtain global 30 m of land cover fine classification products. (2) The overall accuracy of the land cover data of Ordos City from 2003 to 2023 is between 79–87%, and the Kappa coefficient is between 0.79–0.84. (3) Climate, terrain, population and other interactive factors combined with socio-economic population data and national and local policies are the main factors affecting land use change between 2003 and 2023. Full article

(This article belongs to the Special Issue Response of Vegetation to Climatic and Anthropogenic Drivers in the Plateau)

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14 pages, 3274 KiB

Open AccessArticle

Reconstructed Phase Space of Tropical Cyclone Activity in the North Atlantic Basin for Determining the Predictability of the System

by Sarah M. Weaver, Christopher A. Steward, Jason J. Senter, Sarah S. Balkissoon and Anthony R. Lupo

Atmosphere 2024, 15(12), 1488; https://doi.org/10.3390/atmos15121488 - 12 Dec 2024

Abstract

Tropical cyclone prediction is often described as chaotic and unpredictable on time scales that cross into stochastic regimes. Predictions are bounded by the depth of understanding and the limitations of the physical dynamics that govern them. Slight changes in global atmospheric and oceanic [...] Read more.

Tropical cyclone prediction is often described as chaotic and unpredictable on time scales that cross into stochastic regimes. Predictions are bounded by the depth of understanding and the limitations of the physical dynamics that govern them. Slight changes in global atmospheric and oceanic conditions may significantly alter tropical cyclone genesis regions and intensity. The purpose of this paper is to characterize the predictability of seasonal storm characteristics in the North Atlantic basin by utilizing the Largest Lyapunov Exponent and Takens’ Theorem, which is rarely used in weather or climatological analysis. This is conducted for a post-weather satellite era (1960–2022). Based on the accumulated cyclone energy (ACE) time series in the North Atlantic basin, cyclone activity can be described as predictable at certain timescales. Insight and understanding into this coupled non-linear system through an analysis of time delay, embedded dimension, and Lyapunov exponent-reconstructed phase space have provided critical information for the system’s predictability. Full article

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

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10 pages, 3126 KiB

Open AccessArticle

Comparison of the Contrail Drift Parameters Calculated Based on the Radiosonde Observation and ERA5 Reanalysis Data

by Ilia Bryukhanov, Oleg Loktyushin, Evgeny Ni, Ignatii Samokhvalov, Konstantin Pustovalov and Olesia Kuchinskaia

Atmosphere 2024, 15(12), 1487; https://doi.org/10.3390/atmos15121487 (registering DOI) - 12 Dec 2024

Abstract

Aircraft contrails exhibit optical properties similar to those of natural high-level clouds (HLCs) and also form persistent cirrus cloudiness. This paper outlines a methodology for detecting and identifying contrails based on the joint analysis of aircraft trajectories (ADS-B monitoring), the vertical profiles of [...] Read more.

Aircraft contrails exhibit optical properties similar to those of natural high-level clouds (HLCs) and also form persistent cirrus cloudiness. This paper outlines a methodology for detecting and identifying contrails based on the joint analysis of aircraft trajectories (ADS-B monitoring), the vertical profiles of meteorological parameters (radiosonde observation (RAOB) and ERA5 reanalysis), and polarization laser sensing data obtained with the matrix polarization lidar. The potential application of ERA5 reanalysis for determining contrail drift parameters (azimuth, speed, distance, duration, and time of the contrail appearance above the lidar) and interpreting atmospheric polarization laser sensing data in terms of the presence of crystalline ice particles and the assessment of the degree of their horizontal orientation is demonstrated. In the examined case (6 February 2023; Boeing 777-F contrail; flight altitude of 10.3 km; HLC altitude range registered with the lidar of 9.5–10.3 km), the difference in the times of appearance of the contrail over the lidar, calculated from RAOB and ERA5 data, did not exceed 10 min. The difference in the wind direction was 12°, with a wind speed difference of 2 m/s, and the drift distance was approximately the same at about 30 km. The demonstrated technique will allow the experimental dataset of contrail optical and microphysical characteristics to be enhanced and empirical relationships between these characteristics and meteorological quantities to be established. Full article

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

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19 pages, 12447 KiB

Open AccessArticle

Characteristics of Strong Cooling Events in Winter of Northeast China and Their Association with 10–20 d Atmosphere Low-Frequency Oscillation

by Qianhao Wang and Liping Li

Atmosphere 2024, 15(12), 1486; https://doi.org/10.3390/atmos15121486 (registering DOI) - 12 Dec 2024

Abstract

In the past 42 years from 1980 to 2021, 103 regional strong cooling events (RSCEs) occurred in winter in Northeast China, and the frequency has increased significantly in the past 10 years, averaging 2.45 per year. The longest (shortest) duration is 10 (2) [...] Read more.

In the past 42 years from 1980 to 2021, 103 regional strong cooling events (RSCEs) occurred in winter in Northeast China, and the frequency has increased significantly in the past 10 years, averaging 2.45 per year. The longest (shortest) duration is 10 (2) days. The minimum temperature series in 60 events exists in 10–20 d of significant low-frequency (LF) periods. The key LF circulation systems affecting RSCEs include the Lake Balkhash–Baikal ridge, the East Asian trough (EAT), the robust Siberian high (SH) and the weaker (stronger) East Asian temperate (subtropical) jet, with the related anomaly centers moving from northwest to southeast and developing into a nearly north–south orientation. The LF wave energy of the northern branch from the Atlantic Ocean disperses to Northeast China, which excites the downstream disturbance wave train. The corresponding LF positive vorticity enhances and moves eastward, leading to the formation of deep EAT. The enhanced subsidence motion behind the EAT leads to SH strengthening. The cold advection related to the northeast cold vortex is the main thermal factor causing the local temperature to decrease. The Scandinavian Peninsula is the primary cold air source, and the Laptev Sea is the secondary one, with cold air from the former along northwest path via the West Siberian Plain and Lake Baikal, and from the latter along the northern path via the Central Siberian Plateau, both converging towards Northeast China. Full article

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

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21 pages, 5612 KiB

Open AccessArticle

Beyond Temperature Peaks: The Growing Persistence and Intensity of Tmin and Tmax Heatwaves in Portugal’s Changing Climate (1980/1981–2022/2023)

by Luis Angel Espinosa, Maria Manuela Portela and Nikte Ocampo-Guerrero

Atmosphere 2024, 15(12), 1485; https://doi.org/10.3390/atmos15121485 (registering DOI) - 12 Dec 2024

Abstract

This study examines the trends in heatwave characteristics across mainland Portugal from 1980/1981 to 2022/2023, utilising ERA5-Land reanalysis data. To achieve this, the study applies the Heatwave Magnitude Index (HWMI) to identify heatwave days for minimum (T_min) and maximum (T_max [...] Read more.

This study examines the trends in heatwave characteristics across mainland Portugal from 1980/1981 to 2022/2023, utilising ERA5-Land reanalysis data. To achieve this, the study applies the Heatwave Magnitude Index (HWMI) to identify heatwave days for minimum (T_min) and maximum (T_max) temperatures across 15 grid-points representing Portugal’s diverse geography and climate. Three key annual parameters are analysed: the number of heatwave days (ANDH), the average temperature during heatwaves (AATW), and the intensity of heatwave events (AIHD). Results reveal a consistent increase in heatwave persistence throughout mainland Portugal, with more pronounced trends observed for T_max compared to T_min. ANDH T_min shows upward trends across all grid-points, with increases ranging from 0.8 to 4.2 days per decade. ANDH T_max exhibits even more significant increases, with 11 out of 15 grid-points showing statistically significant rises, ranging from 2.2 to 4.4 days per decade. Coastal areas, particularly in the south, demonstrate the most substantial increases in heatwave persistence. The intensity of heatwaves, as measured by AIHD, also shows positive trends across all grid-points for both T_min and T_max, with southern locations experiencing the most significant increases. The study also discusses decadal trends in annual averages of T_min and T_max, as well as extreme measures such as annual minimum (AMIN) and annual maximum (AMAX), daily temperatures spatially represented across mainland Portugal. These analyses reveal widespread warming trends, with more pronounced increases in T_max compared to T_min. The AMIN and AMAX trends further corroborate the overall warming pattern from the heatwave analyses, with notable spatial variations observed. The findings indicate a substantial worsening in the occurrence, duration, and intensity of heatwave events. This increased persistence of heatwaves, especially evident from the early 2000s onwards, suggests a potential climate regime shift in mainland Portugal. The results underscore the need for adaptive strategies to address the growing challenges posed by more frequent and intense heatwaves in the region. Full article

(This article belongs to the Special Issue New Perspectives in Hydrological Extremes)

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22 pages, 18686 KiB

Open AccessArticle

An Idealized Cloud-Resolving Modeling Study on Rainfall in Taiwan Under Uniform Northeasterly Flow in Winter, Part I: Effects of Wind Direction, Speed, and Moisture Amount

by Chung-Chieh Wang, Chun-Hsien Li, Shin-Yi Huang and Chih-Sheng Chang

Atmosphere 2024, 15(12), 1484; https://doi.org/10.3390/atmos15121484 - 12 Dec 2024

Abstract

This study performs idealized simulations of rainfall in Taiwan in a winter monsoon regime under different wind direction (W_d; every 15° from 0° to 90°), speed (W_s; 12, 8, and 4 m s⁻¹), and relative [...] Read more.

This study performs idealized simulations of rainfall in Taiwan in a winter monsoon regime under different wind direction (W_d; every 15° from 0° to 90°), speed (W_s; 12, 8, and 4 m s⁻¹), and relative humidity (RH; 80, 70, and 60%) combinations at low levels, based on a simplified mean sounding profile from observations during the event over 20–24 November 2020. Thus, at a horizontal grid size of 2 km, a total of 7 × 3 × 3 = 63 runs are performed and the aim is to investigate the response in daily rainfall in northern Taiwan to these three parameters. The model results from the closest combination are verified against the observed rainfall both during the reference event and in several historical cases with reasonable agreement, indicating the usefulness of the approach, albeit with some limitations. From the experiments, our main findings can be summarized as below. First and foremost, with W_s = 12 m s⁻¹ and RH = 80%, when the prescribed W_d changes from 0° (northerly) to 90° (easterly) gradually, the main rainfall areas shift from northern to northeastern Taiwan, with local maxima at the northern tip, northeastern tip of Taiwan, and near Suao (at the end of the Central Mountain Range) in response, indicating topographic uplifting for rainfall production. At a larger impinging angle, the Suao area tends to receive the most daily rainfall and can exceed 300 mm at W_d of about 75°–80°. Second, when W_s decreases to 8 m s⁻¹, the general rainfall regions often remain similar but the amounts become lower, especially at local maxima. The peak amount near Suao is only about 100 mm. At weak wind of W_s = 4 m s⁻¹, only moderate rainfall of 20 mm or below can be produced in Taiwan, and the local centers become not discernable. Third, when RH is lowered, the rainfall in northern Taiwan decreases significantly, especially along and near the coast under weaker winds coming from smaller angles. At RH = 70%, a higher accumulation (≥100 mm or so) near Suao is only possible with W_d ≥ about 55° at W_s = 12 m s⁻¹ or when W_d ≥ 70° at W_s = 8 m s⁻¹. At RH = 60%, the rainfall in northern Taiwan (and on the entire island) further decreases, again more evidently in cases with smaller W_d and W_s values. Full article

(This article belongs to the Special Issue Island Effects on Weather and Climate)

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19 pages, 4346 KiB

Open AccessArticle

Experimental Study on the Change in Freezing Temperature During the Remediation of Pb-Contaminated Soils with Biochar

by Shanbo Han, Wei Cao, Yaling Chou and Erxing Peng

Atmosphere 2024, 15(12), 1483; https://doi.org/10.3390/atmos15121483 - 12 Dec 2024

Abstract

In the remediation process of heavily metal-contaminated soil, biochar can change the ion content and soil structure, significantly impacting soil freezing. This experiment used freezing ambient temperature, water (W) content, heavy metal (M) contents, and biochar (BC) contents as factors affecting soil freezing. [...] Read more.

In the remediation process of heavily metal-contaminated soil, biochar can change the ion content and soil structure, significantly impacting soil freezing. This experiment used freezing ambient temperature, water (W) content, heavy metal (M) contents, and biochar (BC) contents as factors affecting soil freezing. The test soil was manually compacted in a homemade acrylic device to achieve a compaction level of 90%. The temperature changes of the soil during low-temperature freezing were monitored through thermometry experiments. The results indicated that soil freezing temperature decreased with increasing heavy metal and biochar contents and increased with increasing initial water content and freezing ambient temperature. Multiple freeze–thaw cycles revealed the interaction between biochar and heavy metals. The effects of biochar on the freezing temperature of soil with different heavy metal contents were different; in the soil with the same heavy metal content, 3% biochar contents had little effect on the freezing temperature of heavy metal-polluted soil, and 5% and 7% biochar contents significantly improved the freezing resistance of the soil. Freeze–thaw cycling had little effect on the soil’s microporous structure, resulting in minimal changes in soil freezing temperatures after seven cycles. Correlation analyses of heavy metals, water content, and biochar revealed that the effects of these factors on freezing temperature were in the order of heavy metals > water > biochar. The composite freezing temperature of biochar and heavy metal overlaps well. The initial freezing temperature of the soil was used to predict the unfrozen water in the soil. The prediction results showed that biochar increased the content of unfrozen water in the soil. Full article

(This article belongs to the Section Climatology)

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28 pages, 19078 KiB

Open AccessArticle

Analysis of PM_2.5 Pollution Transport Characteristics and Potential Sources in Four Chinese Megacities During 2022: Seasonal Variations

by Kun Mao, Yuan Yao, Kun Wang, Chen Liu, Guangmin Tang, Shumin Feng, Yue Shen, Anhua Ju, Hao Zhou and Zhiyu Li

Atmosphere 2024, 15(12), 1482; https://doi.org/10.3390/atmos15121482 - 12 Dec 2024

Abstract

Atmospheric particulate pollution in China’s megacities has heightened public concern over air quality, highlighting the need for precise identification of urban pollution characteristics and pollutant transport mechanisms to enable effective control and mitigation. In this study, a new method combing the High Accuracy [...] Read more.

Atmospheric particulate pollution in China’s megacities has heightened public concern over air quality, highlighting the need for precise identification of urban pollution characteristics and pollutant transport mechanisms to enable effective control and mitigation. In this study, a new method combing the High Accuracy Surface Modeling (HASM) and Multiscale Geographically Weighted Regression (MGWR) was proposed to derive seasonal high spatial resolution PM_2.5 concentrations. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) was applied to analyze the seasonal spatial variations, transport pathways, and potential sources of PM_2.5 concentrations across China’s four megacities: Beijing, Shanghai, Xi’an, and Chengdu. The result indicates that: (1) the proposed method outperformed Kriging, inverse distance weighting (IDW), and HASM, with coefficient of determination values ranging from 0.91 to 0.94, and root mean square error values ranging from 1.98 to 2.43 µg/m³, respectively; (2) all cities show a similar seasonal pattern, with PM_2.5 concentrations highest in winter, followed by spring, autumn, and summer; Beijing has higher concentrations in the south, Shanghai and Xi’an in the west, and Chengdu in central urban areas, decreasing toward the rural area; (3) potential source contribution function and concentration weighted trajectory analysis indicate that Beijing’s main potential PM_2.5 sources are in Hebei Province (during winter, spring, and autumn), Shanghai’s are in the Yellow Sea and the East China Sea, Xi’an’s are in Southern Shaanxi Province, and Chengdu’s are in Northeastern and Southern Sichuan Province, with all cities experiencing higher impacts in winter; (4) there is a negative correlation between precipitation, air temperature, and seasonal PM_2.5 levels, with anthropogenic emissions sources such as industry combustion, power plants, residential combustion, and transportation significantly impact on seasonal PM_2.5 pollution. Full article

(This article belongs to the Section Air Quality)

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18 pages, 2383 KiB

Open AccessArticle

An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics

by Shuo Zhang and Mengya Pan

Atmosphere 2024, 15(12), 1481; https://doi.org/10.3390/atmos15121481 - 11 Dec 2024

Abstract

Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, [...] Read more.

Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. This study proposes an analytical framework for forest fire prediction based on Automated Machine Learning (AutoML) technology to address the challenges traditional machine learning methods face in forest fire prediction. We collected meteorological, topographical, and vegetation data from Guangxi Province, with meteorological data covering 1994 to 2023, providing comprehensive background information for our prediction model. Using the prediction model, which was constructed with the AutoGluon framework, the experimental results indicate that models under the AutoGluon framework (e.g., KNeighborsDist classifier) significantly outperform traditional machine learning models in terms of accuracy, precision, recall, and F1-Score, with the highest accuracy rate reaching 0.960. Model error analysis shows that models under the AutoGluon framework perform better in error control. This study provides an efficient and accurate method for forest fire prediction, which is of great significance for decision-making in forest fire management and for protecting forest resources and ecological security. Full article

(This article belongs to the Special Issue Forest Ecosystems in a Changing Climate)

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23 pages, 1892 KiB

Open AccessReview

Are Agroecosystem Services Under Threat? Examining the Influence of Climate Externalities on Ecosystem Stability

by Temidayo Olowoyeye, Gideon Abegunrin and Mariusz Sojka

Atmosphere 2024, 15(12), 1480; https://doi.org/10.3390/atmos15121480 - 11 Dec 2024

Abstract

This study examines the impacts of climate-induced externalities on the stability of agroecosystems and the ecosystem services they provide. Using the PRISMA approach, we review literature published from 2015 to 2024. The study identifies how extreme weather events such as droughts, floods, heatwaves, [...] Read more.

This study examines the impacts of climate-induced externalities on the stability of agroecosystems and the ecosystem services they provide. Using the PRISMA approach, we review literature published from 2015 to 2024. The study identifies how extreme weather events such as droughts, floods, heatwaves, and altered precipitation patterns disrupt the provisioning, regulating, and supporting services critical to food security, soil fertility, water purification, and biodiversity. Our findings show a continued increase in climate extremes, raising concerns about food security, environmental resilience, and socio-economic stability. It also reveals that regions dependent on rain-fed agriculture, such as parts of Africa, Asia, and the Mediterranean, are particularly vulnerable to these stressors. Adaptation strategies, including conservation agriculture, crop diversification, agroforestry, and improved water management, are identified as crucial for mitigating these impacts. This study emphasises the importance of proactive, policy-driven approaches to foster climate resilience, support agroecosystem productivity, and secure ecosystem services critical to human well-being and environmental health. Full article

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

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16 pages, 2182 KiB

Open AccessArticle

Enhancements of Triethanolamine CO₂ Absorption Rate and Degradation in the Presence of Nickel Nanoparticles Catalysts

by Harold W. Orendi, Kevin Joby and Lidija Šiller

Atmosphere 2024, 15(12), 1479; https://doi.org/10.3390/atmos15121479 - 11 Dec 2024

Abstract

Here, the catalytic and degradation effect of nickel nanoparticles (NiNPs) on triethanolamine (TEA) with CO₂ at 20 °C and 50 °C and a range of TEA concentrations (3–30 wt%) was studied. We show that TEA absorption rate of CO₂ can be [...] Read more.

Here, the catalytic and degradation effect of nickel nanoparticles (NiNPs) on triethanolamine (TEA) with CO₂ at 20 °C and 50 °C and a range of TEA concentrations (3–30 wt%) was studied. We show that TEA absorption rate of CO₂ can be enhanced with NiNPs, the maximum enhancement was 8.3% when compared to a control solution found at 50 °C with 30 wt% TEA alone. Additionally, the time for TEA to be fully loaded with CO₂ is reduced; compared to the control, NiNPs enhanced solutions were up to 26.3% faster. Also, to the best of our knowledge, this is the first time the degradation of TEA with NiNPs has been studied. TEA was subject to both oxygen (30 wt%, 55 °C, 0.35 L/min of air, 0.4 mol_CO2/mol_TEA, 7.5 mL/min of CO₂) and thermal degradation with and without NiNPs (30 wt%, 0.5 mol_CO2/mol_TEA, 135 °C). In both degradation experiments, surprisingly, there was no significant difference in TEA degradation in the presence of NiNPs. At high temperature (135 °C), the solution lost 19.2% and 20.3% of the original TEA, with and without NiNPs, respectively. In the presence of oxygen, the solution lost 30.5% and 33.6% of the original TEA, with and without NiNPs, respectively. This indicates that TEA or its mixture with other amines and NiNPs could improve post-combustion CO₂ capture. Full article

(This article belongs to the Special Issue Advances in CO₂ Capture and Absorption)

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19 pages, 906 KiB

Open AccessArticle

Forecasting of Local Lightning Using Spatial–Channel-Enhanced Recurrent Convolutional Neural Network

by Wei Zhou, Jinliang Li, Hongjie Wang, Donglai Zhang and Xupeng Wang

Atmosphere 2024, 15(12), 1478; https://doi.org/10.3390/atmos15121478 - 11 Dec 2024

Abstract

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models [...] Read more.

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models at capturing the spatiotemporal predictors of lightning events. However, these methods struggle to integrate predictors from diverse data sources, which leads to lower accuracy and interpretability. To address these challenges, the Multi-Scale Spatial–Channel-Enhanced Recurrent Convolutional Neural Network (SCE-RCNN) is proposed to improve forecasting accuracy and timeliness by utilizing multi-source data and enhanced attention mechanisms. The proposed model incorporates a multi-scale spatial–channel attention module and a cross-scale fusion module, which facilitates the integration of data from diverse sources. The multi-scale spatial–channel attention module utilizes a multi-scale convolutional network to extract spatial features at different spatial scales and employs a spatial–channel attention mechanism to focus on the most relevant regions for lightning prediction. Experimental results show that the SCE-RCNN model achieved a critical success index (CSI) of 0.83, a probability of detection (POD) of 0.991, and a false alarm rate (FAR) reduced to 0.351, outperforming conventional deep learning models across multiple prediction metrics. This research provides reliable lightning forecasts to support real-time decision-making, making significant contributions to aviation safety, outdoor event planning, and disaster risk management. The model’s high accuracy and low false alarm rate highlight its value in both academic research and practical applications. Full article

(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)

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18 pages, 1492 KiB

Open AccessArticle

Exposure to Extreme Temperatures and Change in Dietary Patterns in Korea

by Seungyeon Cho

Atmosphere 2024, 15(12), 1477; https://doi.org/10.3390/atmos15121477 - 11 Dec 2024

Abstract

Climate change has led to increased volatility in extreme temperatures, which poses a significant threat to human health. One way in which extreme temperatures impact health is through changes in dietary patterns, particularly food consumption and nutrient intake. This study uses a restricted-access [...] Read more.

Climate change has led to increased volatility in extreme temperatures, which poses a significant threat to human health. One way in which extreme temperatures impact health is through changes in dietary patterns, particularly food consumption and nutrient intake. This study uses a restricted-access version of 24 h dietary recall data from the 2018–2021 Korea National Health and Nutrition Examination Survey and meteorological data to examine the effect of heat and cold waves on food consumption and nutrient intake. The results suggest that cold waves increase individuals’ intake of key nutrients such as calories, protein, fat, saturated fat, and sodium. In contrast, heat waves have little to no significant effect on nutrient intake. Aside from the significant increase in the consumption of beer and chicken, the effects of heat or cold waves on overall food consumption are minimal. Depending on age and income level, cold and heat waves have different effects on food consumption and nutrient intake. This study suggests that temperature, particularly extreme heat and cold, plays a significant role in shaping individuals’ dietary patterns. Therefore, special attention is needed to maintain a balanced and healthy diet during extreme temperatures. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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17 pages, 3410 KiB

Open AccessArticle

The Aerosol Optical Depth Retrieval from Wide-Swath Imaging of DaQi-1 over Beijing

by Zhongting Wang, Ruijie Zhang, Ruizhi Chen and Hui Chen

Atmosphere 2024, 15(12), 1476; https://doi.org/10.3390/atmos15121476 - 10 Dec 2024

Abstract

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation [...] Read more.

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Techniques in Application of Air Quality and Climate Study)

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13 pages, 5759 KiB

Open AccessArticle

Design and Performance Analysis of Meteorological Temperature Sensor Calibration Device Using Gas Cavities

by Yafei Huang, Chenhao Gao, Zhaopeng Wen, Fei Qian and Lijun He

Atmosphere 2024, 15(12), 1475; https://doi.org/10.3390/atmos15121475 - 10 Dec 2024

Abstract

In order to solve the problem of a meteorology temperature sensor not being able to touch a liquid, an open gas cavity structure immersed in the liquid was designed. According to the characteristics that the temperature sensing position of the meteorological temperature sensor [...] Read more.

In order to solve the problem of a meteorology temperature sensor not being able to touch a liquid, an open gas cavity structure immersed in the liquid was designed. According to the characteristics that the temperature sensing position of the meteorological temperature sensor is in the bottom area of the gas cavity, a simulation and experimental study of the bottom temperature field of φ50 mm cylindrical and φ(50-35-25) mm stepped column gas cavities were carried out. The experimental results at (−30~30) °C show that the gas stability of the gas cavities was better than that of the liquid constant temperature bath, and the performance of the cylindrical gas cavity was the best. The gas temperature stability of the stepped column gas cavity and the liquid constant temperature bath follow a strong trend. The maximum stability of the cylindrical gas cavity is 0.0054 °C, and the maximum stability of the stepped column gas cavity is 0.0080 °C. The results also show that the maximum uniformity of the stepped gas cavity is 0.0077 °C, and the maximum uniformity of the cylindrical gas cavity is 0.0528 °C. The uncertainty introduced in the measurement process was evaluated to ensure the confidence of the experimental data. The maximum value of the extended uncertainty was U = 0.0027 °C (k = 2). Compared with the solid-state constant temperature bath calibration method, the temperature sensor of different shapes can be directly placed into the gas cavity without the need for the meteorological temperature sensor to be closely attached to the wall of the gas cavity, and a sealing plug is used to seal the cavity mouth. The operation is very convenient, rapid turnover of the calibration of the meteorological temperature sensor can be achieved, and the work efficiency can be improved. Superior stability and uniformity can be obtained compared to gas constant temperature cavities. This study provides a valuable reference for the structural design of large-volume gas cavities and provides support and guarantee for global climate change monitoring. Full article

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

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13 pages, 4497 KiB

Open AccessArticle

A Comparison of the Effects of Climate and Human Variability on the Thermal Resistance of Clothing

by Ferenc Ács, Zsófia Szalkai, Erzsébet Kristóf and Annamária Zsákai

Atmosphere 2024, 15(12), 1474; https://doi.org/10.3390/atmos15121474 - 10 Dec 2024

Abstract

We used a clothing thermal resistance model to investigate and compare the effects of climate and human variability on human thermal load. To investigate the effect of climate variability, we introduced the mean clothing thermal resistance

\bar{r_{c l}}

. For characterizing [...] Read more.

We used a clothing thermal resistance model to investigate and compare the effects of climate and human variability on human thermal load. To investigate the effect of climate variability, we introduced the mean clothing thermal resistance

\bar{r_{c l}}

. For characterizing the effect of human variability, we used the standard deviation of clothing thermal resistance

∆ r_{c l}

. We distinguished people based on their body type. We also defined the average human, a man and a woman, with thermal resistances of r_cl,m and r_cl,f. The investigation was carried out for the European region in the cold season for the period of 1981–2010. The climate variables were taken from the ERA5 reanalysis database. Our most important results are the following. (1) The macroscale pattern of the

\bar{r_{c l}}

and

∆ r_{c l}

fields are very similar, based on which it can be stated that human variability does not modify the spatial distribution of

\bar{r_{c l}}

. (2) The

∆ r_{c l}

values are roughly a quarter of the

\bar{r_{c l}}

values. The highest

\bar{r_{c l}}

values (3.2–3.4 clo) are in Lapland, and the smallest (1–1.2 clo) in Andalusia. (3) The macroscale pattern of the r_cl,m and r_cl,f fields is similar to the macroscale pattern of the r_cl values of the mesomorphic person r_cl,2. The field of r_cl,2 can be used for climate classification purposes. Full article

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

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