Journal of Atmospheric and Solar-Terrestrial Physics最新文献

A case study on the dust storm that occurred on March 13–18, 2022, over the Algerian Sahara, using satellite remote sensing

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-09-14 DOI: 10.1016/j.jastp.2024.106345

{"title":"A case study on the dust storm that occurred on March 13–18, 2022, over the Algerian Sahara, using satellite remote sensing","authors":"","doi":"10.1016/j.jastp.2024.106345","DOIUrl":"10.1016/j.jastp.2024.106345","url":null,"abstract":"<div><p>This study investigates the dynamics of a significant dust storm that occurred in Algeria in March 2022, employing data derived from the Sentinel-5P and CALIPSO satellite instruments. We examine the Aerosol Absorbing Index (AAI) to detect n absorbing aerosols, with a focus on desert dust, and analyze the attenuation coefficient. Additionally, we employ the HYSPLIT trajectory analyze to study dust transport and MERRA-2 to examine wind patterns wind. The key findings unveil a detailed trajectory of a prominent dust storm in Algeria in March 2022. The Aerosol Absorbing Index (AAI) effectively identifies absorbing aerosols, particularly desert dust, through thorough analyses of dust trajectory and wind patterns; augmenting these findings, CALIPSO satellite data has provided a detailed vertical profile of aerosols within the dust plume, emphasizing spatial and altitudinal extents. This research significantly contributes to advancing scientific discussions on atmospheric dynamics in arid regions and enhances our understanding and forecasting capabilities related to Saharan dust storm initiation and trajectory.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consistency of climatic changes at different time scales in Central England and Greenland

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-09-13 DOI: 10.1016/j.jastp.2024.106343

{"title":"Consistency of climatic changes at different time scales in Central England and Greenland","authors":"","doi":"10.1016/j.jastp.2024.106343","DOIUrl":"10.1016/j.jastp.2024.106343","url":null,"abstract":"<div><p>Characteristic variations in the Greenland isotope temperature data over the last 1000 years and in the meteorological temperature measurements collected from Central England during the past four centuries have been analyzed. We take advantage of the continuous wavelet transform to analyze the simultaneous occurrence of temperature variations of different time scales. We assess the extent to which these phenomena can be compared when examining two different northern hemisphere locations at different time scales. Among the long-term variations, we focus on the cooling at the turn of the 18th century, which occurred slightly later in Greenland than in central England, and the warming observed at present. On the short time scale, the range under study is limited to times of the order of 5-10 years. It has been found that it is on these scales that temperature variations in the two locations are relatively consistent, with a cross-correlation coefficient as high as 0.6 for timescales of the order of 9 years. The main solar activity cycle also falls within the interval of significant correlations. It is shown that despite the absence of direct correlation between temperature and solar activity, the time dependence of the wavelet cross-correlation coefficient of the two temperature series on the scale of 11 years reproduces the long-term variations of solar activity.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of anomalous lightning activity during the January 15, 2022 Tonga volcano eruption based on measurements of the VLF and ELF electromagnetic fields 根据 VLF 和 ELF 电磁场测量结果调查 2022 年 1 月 15 日汤加火山爆发期间的异常闪电活动

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-09-13 DOI: 10.1016/j.jastp.2024.106344

{"title":"Investigation of anomalous lightning activity during the January 15, 2022 Tonga volcano eruption based on measurements of the VLF and ELF electromagnetic fields","authors":"","doi":"10.1016/j.jastp.2024.106344","DOIUrl":"10.1016/j.jastp.2024.106344","url":null,"abstract":"<div><p>An anomalous increase in the level of Very Low Frequency (VLF, 3–30 kHz) and Extremely Low Frequency (ELF, 3–3000 Hz) radio noise and the rate of VLF atmospherics was registered during the explosive eruption of the Tonga volcano on January 15, 2022 at the Akademik Vernadsky station (65.246°S; 64.257°W) about 8870 km from the volcano. At the peak activity around 5 UT, the number of atmospherics in 2-min intervals increased by almost 15 times compared to the period preceding the eruption. At this point, the estimated rate reached 360 VLF atmospherics per second. At the same time, an increase in the power spectral density of the magnetic field by 5–9 times was observed in both the ELF and VLF ranges. After 40 min, only on ELF an increased peak lasting ∼10 min was observed, comparable in magnitude to the main peak. According to the Worldwide Lightning Location Network (WWLLN), increased thunderstorm activity was concentrated very close to the volcano during this period. This discrepancy between the intensities of ELF and VLF radiation suggests a significant difference in the parameters of currents in lightning discharges occurring in the area of the volcano vent and in the area of the volcanic ash plume.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of machine learning models for rainfall prediction 降雨预测机器学习模型的比较分析

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-30 DOI: 10.1016/j.jastp.2024.106340

{"title":"Comparative analysis of machine learning models for rainfall prediction","authors":"","doi":"10.1016/j.jastp.2024.106340","DOIUrl":"10.1016/j.jastp.2024.106340","url":null,"abstract":"<div><p>Predicting rainfall is essential for many applications, including agriculture, hydrology, and disaster management. In this work, we undertake a comparison examination of various machine learning models to forecast rainfall based on meteorological data. The target variable in this study is rainfall, and the dataset used includes characteristics like temperature, relative humidity, wind speed, and wind direction. The following seven machine learning models were assessed: Support Vector Regression (SVR), Multivariate adaptive regression splines (MARS), Random Forest Regression, and Deep Neural Network with Historical Data (DWFH), Haar Wavelet Function, Decision Tree and Discrete wavelet Transform (DWT). Data preprocessing, which includes standardisation and lagging to capture temporal dependencies, comes first in the analysis phase. A wavelet transformation is also used to capture complex patterns in the data. Each model is tested on a different test set after being trained on a subset of the dataset. The results are assessed using the Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), focusing on the RMSE and MSE values for better comparison across models. Our findings reveal that the DWFH model achieved an RMSE of 0.0138807 mm and MSE of 0.000193 mm<sup>2</sup>, demonstrating their effectiveness in predicting rainfall. The Random Forest and SVR models also provided competitive results. This study highlights the importance of selecting an appropriate machine learning model for rainfall prediction and the significance of preprocessing techniques in improving model performance. These insights can aid decision-makers in choosing the most suitable model for their specific application, contributing to more accurate rainfall predictions and enhanced decision support systems.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A statistical analysis of atmospheric parameters for cataloged astronomical observatory sites 对编目天文观测站点大气参数的统计分析

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-30 DOI: 10.1016/j.jastp.2024.106334

引用次数: 0

Time series analysis of sea surface temperature change in the coastal seas of Türkiye 图尔基耶近海海面温度变化的时间序列分析

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-28 DOI: 10.1016/j.jastp.2024.106339

{"title":"Time series analysis of sea surface temperature change in the coastal seas of Türkiye","authors":"","doi":"10.1016/j.jastp.2024.106339","DOIUrl":"10.1016/j.jastp.2024.106339","url":null,"abstract":"<div><p>Sea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Türkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5<sup>o</sup>x0.5°) monthly SST dataset spanning the years 1970–2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 °C, 20.2 °C, 17.0 °C, and 16.6 °C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 °C, 21.9 °C, 18.1 °C, and 18.8 °C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of machine learning models for predicting PM2.5 concentrations using meteorological and chemical indicators 利用气象和化学指标预测 PM2.5 浓度的机器学习模型比较分析

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-27 DOI: 10.1016/j.jastp.2024.106338

{"title":"Comparative analysis of machine learning models for predicting PM2.5 concentrations using meteorological and chemical indicators","authors":"","doi":"10.1016/j.jastp.2024.106338","DOIUrl":"10.1016/j.jastp.2024.106338","url":null,"abstract":"<div><p>Air pollution significantly impacts human health, causing numerous premature deaths, particularly with the rise in PM<sub>2.5</sub> concentrations. Therefore, comparing different machine learning (ML) models for predicting PM<sub>2.5</sub> concentration is crucial. This research focuses on six ML models: Linear Regression (LR), Regression Tree (RT), Support Vector Machine (SVM), Ensemble Regression (ERT), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). Trained on six years of data (July 2015–December 2021) with optimized hyperparameters, the models consider eight meteorological and chemical indicators as PM<sub>2.5</sub> predictors, including temperature, relative humidity, air pressure, O<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, dew point, and wind speed. Model efficiency is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Coefficient of Determination (R<sup>2</sup>) values. The models achieve R<sup>2</sup> and RMSE values as follows: LR (0.72, 13.52), RT (0.8, 12.156), SVM (0.82, 10.28), ERT (0.81, 11.87), GPR (0.94, 7.65), and ANN (0.99, 2.36). These metrics indicate the superior performance of ANN, with its R<sup>2</sup> value approaching 1 and the lowest RMSE compared to other models. The results highlight the effectiveness of ANN, particularly the model with three hidden layers, in predicting PM<sub>2.5</sub> concentration. Utilizing ML models for this purpose is crucial for understanding and mitigating the impacts on human health and the environment, with ANN emerging as a promising tool for various investigations.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mathematical modelling for solar irradiance reduction of sunshades and some near-future albedo modification approaches for mitigation of global warming 降低遮阳板太阳辐照度的数学模型和一些近未来减缓全球变暖的反照率修正方法

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-24 DOI: 10.1016/j.jastp.2024.106337

{"title":"A mathematical modelling for solar irradiance reduction of sunshades and some near-future albedo modification approaches for mitigation of global warming","authors":"","doi":"10.1016/j.jastp.2024.106337","DOIUrl":"10.1016/j.jastp.2024.106337","url":null,"abstract":"<div><p>To address the global warming problem, one of the space-based geoengineering solutions suggests the construction of an occluding disc that can work as a solar curtain to mitigate solar irradiation penetration to the earth atmosphere. A widely discussed concept needs the construction of a large-scale sunshade system near the Sun–Earth L<sub>1</sub> equilibrium point in order to control the average global temperature. However, to improve the accuracy of theoretical estimations, more consistent modeling of the Sun-Curtain-Earth system and solar irradiance reduction rate are required. This study revisits the mathematical modeling of the solar irradiance reduction system and considers the fundamentals of shading physics. Simplified mathematical modeling of solar irradiance reduction rate is derived based on the solar flux density. For the climate control, controllability of the reduction rate by using some physical parameters (e.g., flux reflection rate and angle of the curtain) is discussed. Based on the results of this model, the technical challenges and feasibility of constructing a sunshade system at L<sub>1</sub> Lagrange point are evaluated. Some technologically feasible, near-future options for the warming problem are discussed briefly.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel particulate matter (PM2.5) forecasting method based on deep learning with suitable spatiotemporal correlation analysis 基于深度学习和适当时空相关性分析的新型颗粒物（PM2.5）预报方法

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-23 DOI: 10.1016/j.jastp.2024.106336

{"title":"Novel particulate matter (PM2.5) forecasting method based on deep learning with suitable spatiotemporal correlation analysis","authors":"","doi":"10.1016/j.jastp.2024.106336","DOIUrl":"10.1016/j.jastp.2024.106336","url":null,"abstract":"<div><p>Since air pollution caused by PM 2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) is a serious threat to human health, the accurate forecasting of PM 2.5 concentration in metropolitan areas is one of the prior conditions to reduce and eliminate the harmful impacts on human beings produced by PM2.5. In this study, we analyzed the spatiotemporal correlations between target and observation parameters relevant to air pollution forecasting and proposed a convolutional neural network (CNN) and long short-term memory (LSTM) model (also called PM predictor) for next day's daily average PM 2.5 concentration forecasting in Beijing. The proposed spatiotemporal correlations were analyzed for efficient estimation of mutual information, not only if the degrees of variations between the two spaces under consideration are similar, but also if the degrees of variations are significantly different, thereby generating a spatiotemporal feature vector. CNN provided an efficient extraction of inherent features for latent air quality and meteorological input data relevant to PM 2.5, and LSTM delivered the historical information in the time series data, thus a novel PM predictor with remarkably improved performance was constructed, compared with multi-layer perceptron (MLP) and LSTM model in overall forecasting. The air quality and meteorological data from the monitoring stations in Beijing and four surrounding cities from January 1, 2015 to December 31, 2017 were adopted as dataset. The forecasting results suggest that the proposed PM predictor is superior to other models in overall forecasting, while LSTM is better than PM predictor with slight difference in seasonal forecasting.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polarization lidar observations of diurnal and seasonal variations in the atmospheric mixing layer above a tropical rural place gadanki, India 极化激光雷达对印度加丹吉热带农村地区上空大气混合层昼夜和季节变化的观测

IF 1.8 4区地球科学

Journal of Atmospheric and Solar-Terrestrial Physics Pub Date : 2024-08-23 DOI: 10.1016/j.jastp.2024.106335

{"title":"Polarization lidar observations of diurnal and seasonal variations in the atmospheric mixing layer above a tropical rural place gadanki, India","authors":"","doi":"10.1016/j.jastp.2024.106335","DOIUrl":"10.1016/j.jastp.2024.106335","url":null,"abstract":"<div><p>This study presents the daily and seasonal variation of the atmospheric mixing layer height (MLH) over Gadanki, India (13.45°N, 79.18°E), a tropical rural location based on polarization lidar observations. The observations spanned the years 2009–2014, encompassing 303 instances, and coinciding with radiosonde and surface weather station measurements. The MLH was determined through the analysis of aerosol profiles and confirmed with the MLH values derived from radiosonde data. The lidar depolarization ratio was employed to characterize aerosol shape. This study aims to establish a connection between aerosol backscatter and its shape through lidar observations, considering diurnal and seasonal variations, while also identifying the influencing factors. This study illustrates four distinct case studies conducted during different seasons to depict aerosol behavior in both convectively active and non-active periods. These case studies unveil the influence of aerosol shape on water intake and subsequent residual layer and cloud formation. The observed fluctuations in MLH and aerosol shape suggest a dynamic relationship between local meteorology and long-range aerosol transport.</p></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0