Atmosphere最新文献

{"title":"Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area","authors":"Davide Cinquegrana, Myriam Montesarchio, Alessandra Lucia Zollo, Edoardo Bucchignani","doi":"10.3390/atmos15091119","DOIUrl":"https://doi.org/10.3390/atmos15091119","url":null,"abstract":"The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence temperature, humidity, and other atmospheric variables of the surrounding areas. Heat fluxes occur especially during the nighttime in large urbanized areas, characterized by poor vegetation, and are responsible for the formation of Urban Heat and Dry Island, i.e., higher temperatures and lower humidity compared to rural areas. They can be exacerbated under severe conditions, with dangerous consequences for people living in these urban areas. For these reasons, the need of accurately forecasting these phenomena is particularly felt. The present work represents one of the first attempts of using a very high resolution (about 600 m) in a Numerical Weather Prediction model. Performances of this advanced version of ICON have been investigated over a domain located in southern Italy, including the urban metropolitan area of Naples, considering a week characterized by extremely high temperatures. Results highlight that the activation of TERRA-URB scheme entails a better representation of temperature, relative humidity, and wind speed in urban areas, especially during nighttime, also allowing a proper reproduction of Urban Heat and Dry Island effects. Over rural areas, instead, no significant differences are found in model results when the urban canopy scheme is used.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"16 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248086","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}

{"title":"Association between Short-Term Exposure to Ambient Air Pollution and Mortality from Cardiovascular Diseases in Ulaanbaatar, Mongolia","authors":"Nandin-Erdene Bayart, Krassi Rumchev, Christopher M. Reid, Sylvester Dodzi Nyadanu, Gavin Pereira","doi":"10.3390/atmos15091110","DOIUrl":"https://doi.org/10.3390/atmos15091110","url":null,"abstract":"Cardiovascular diseases (CVD) are one of the leading causes of death globally, and a major contributor to CVD mortality is ambient air pollution (AAP). This study aimed to evaluate associations between AAP and mortality from CVD, including ischemic heart diseases (IHD) and strokes. Data on daily mortality records, six criteria AAP and meteorology in the capital city of Mongolia were collected between 1 January 2016 and 31 December 2022. A time-stratified case-crossover design was analysed with distributed lag conditional Poisson regression to estimate the relative risk of CVD mortality. We found that for each interquartile range increase in PM2.5, PM10, SO2 and NO2 pollutants, the risk of CVD mortality increased by 1.5% (RR = 1.015; 95% CI: 1.005, 1.025), 4.4% (RR = 1.044; 95% CI: 1.029, 1.059), 3.1% (RR = 1.033; 95% CI: 1.015, 1.047) and 4.8% (RR = 1.048; 95% CI: 1.013, 1.085) at lag01, respectively. The association between all pollutants, except O3, and CVD mortality was higher in subgroups ≥ 65 years and male, during the cold season and after using a new type of coal briquettes. Despite using the new type of coal briquettes, Ulaanbaatar’s ambient air pollution remained higher than the WHO’s guidelines. Based on our findings, we recommend that efforts should be focused on adopting more efficient strategies to reduce the current pollution level.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190122","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}

{"title":"Risk Associations between Air Pollution Exposure and Cardiovascular Diseases: A Residential Retrospective Cohort Study","authors":"Elisa Bustaffa, Cristina Mangia, Liliana Cori, Marco Cervino, Fabrizio Bianchi, Fabrizio Minichilli","doi":"10.3390/atmos15091113","DOIUrl":"https://doi.org/10.3390/atmos15091113","url":null,"abstract":"The population of the Venafro Valley (Southern Italy) faces various type of air pollution problems (industrial facilities, traffic, and biomass combustion). To estimate exposure to various pollution sources, a multi-stage random forest model was used, integrating particulate matter (PM) data with satellite observations, land-use patterns, and meteorological information generating maps of PM2.5 concentration. Four distinct PM2.5 exposure categories were established using the quartile method. To assess the association between PM2.5 and cause-specific mortality and morbidity, a time-dependent and sex-specific Cox multiple regression analysis was conducted, adjusting for age classes. In addition, the hazard ratios were accompanied by a probability measure of the strength of the evidence toward a hypothesis of health risk associated with the exposure under study (1−p value). The whole cohort was exposed to PM2.5 annual levels exceeding the 5 µg/m3 limit recommended by the World Health Organization. Mortality excesses were observed in class 3 for both sexes for cardiac heart diseases. Excesses of cardiovascular diseases were observed for both sexes in class 3 and 4. The study highlights significant signals warranting mitigation actions, which regional authorities are currently considering.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"311 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224811","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}

{"title":"Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations","authors":"Jingyue Mo, Yanbo Shen, Bin Yuan, Muyuan Li, Chenchen Ding, Beixi Jia, Dong Ye, Dan Wang","doi":"10.3390/atmos15091112","DOIUrl":"https://doi.org/10.3390/atmos15091112","url":null,"abstract":"This study conducts a comprehensive evaluation of four scenario experiments using the CMA_WSP, WRF, and WRF_FITCH models to enhance forecasts of hub-height wind speeds at multiple wind farms in Northern China, particularly under significant wind speed fluctuations during high wind conditions. The experiments apply various wind speed calculation methods, including the Monin–Obukhov similarity theory (ST) and wind farm parameterization (WFP), within a 9 km resolution framework. Data from four geographically distinct stations were analyzed to assess their forecast accuracy over a 72 h period, focusing on the transitional wind events characterized by substantial fluctuations. The CMA_WSP model with the ST method (CMOST) achieved the highest scores across the evaluation metrics. Meanwhile, the WRF_FITCH model with the WFP method (FETA) demonstrated superior performance to the other WRF models, achieving the lowest RMSE and a greater stability. Nevertheless, all models encountered difficulties in predicting the exact timing of extreme wind events. This study also explores the effects of these methods on the wind power density (WPD) distribution, emphasizing the boundary layer’s influence at the hub-heighthub-height of 85 m. This influence leads to significant variations in the central and coastal regions. In contrast to other methods that account for the comprehensive effects of the entire boundary layer, the ST method primarily relies on the near-surface 10 m wind speed to calculate the hub-height wind speed. These findings provide important insights for enhancing wind speed and WPD forecasts under transitional weather conditions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"2011 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190124","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}

{"title":"MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction","authors":"Feng Gao, Jiaen Fei, Yuankang Ye, Chang Liu","doi":"10.3390/atmos15091114","DOIUrl":"https://doi.org/10.3390/atmos15091114","url":null,"abstract":"The spatiotemporal forecasting of temperature is a critical issue in meteorological prediction, with significant implications for fields such as agriculture and energy. With the rapid advancement of data-driven deep learning methods, deep learning-based spatiotemporal sequence forecasting models have seen widespread application in temperature spatiotemporal forecasting. However, statistical analysis reveals that temperature evolution varies across temporal and spatial scales due to factors like terrain, leading to a lack of existing temperature prediction models that can simultaneously learn both large-scale global features and small to medium-scale local features over time. To uniformly model temperature variations across different temporal and spatial scales, we propose the Multi-Scale Large Kernel Spatiotemporal Attention Neural Network (MSLKSTNet). This model consists of three main modules: a feature encoder, a multi-scale spatiotemporal translator, and a feature decoder. The core module of this network, Multi-scale Spatiotemporal Attention (MSSTA), decomposes large kernel convolutions from multi-scale perspectives, capturing spatial feature information at different scales, and focuses on the evolution of multi-scale spatial features over time, encompassing both global smooth changes and local abrupt changes. The results demonstrate that MSLKSTNet achieves superior performance, with a 35% improvement in the MSE metric compared to SimVP. Ablation studies confirmed the significance of the MSSTA unit for spatiotemporal forecasting tasks. We apply the model to the regional ERA5-Land reanalysis temperature dataset, and the experimental results indicate that the proposed method delivers the best forecasting performance, achieving a 42% improvement in the MSE metric over the widely used ConvLSTM model for temperature prediction. This validates the effectiveness and superiority of MSLKSTNet in temperature forecasting tasks.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190126","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}

{"title":"A Review on the Arctic–Midlatitudes Connection: Interactive Impacts, Physical Mechanisms, and Nonstationary","authors":"Shuoyi Ding, Xiaodan Chen, Xuanwen Zhang, Xiang Zhang, Peiqiang Xu","doi":"10.3390/atmos15091115","DOIUrl":"https://doi.org/10.3390/atmos15091115","url":null,"abstract":"In light of the rapid Arctic warming and continuous reduction in Arctic Sea ice, the complex two-way Arctic–midlatitudes connection has become a focal point in recent climate research. In this paper, we review the current understanding of the interactive influence between midlatitude atmospheric variability and Arctic Sea ice or thermal conditions on interannual timescales. As sea ice diminishes, in contrast to the Arctic warming (cooling) in boreal winter (summer), Eurasia and North America have experienced anomalously cold (warm) conditions and record snowfall (rainfall), forming an opposite oscillation between the Arctic and midlatitudes. Both statistical analyses and modeling studies have demonstrated the significant impacts of autumn–winter Arctic variations on winter midlatitude cooling, cold surges, and snowfall, as well as the potential contributions of spring–summer Arctic variations to midlatitude warming, heatwaves and rainfall, particularly focusing on the role of distinct regional sea ice. The possible physical processes can be categorized into tropospheric and stratospheric pathways, with the former encompassing the swirling jet stream, horizontally propagated Rossby waves, and transient eddy–mean flow interaction, and the latter manifested as anomalous vertical propagation of quasi-stationary planetary waves and associated downward control of stratospheric anomalies. In turn, atmospheric prevailing patterns in the midlatitudes also contribute to Arctic Sea ice or thermal condition anomalies by meridional energy transport. The Arctic–midlatitudes connection fluctuates over time and is influenced by multiple factors (e.g., continuous melting of climatological sea ice, different locations and magnitudes of sea ice anomalies, internal variability, and other external forcings), undoubtedly increasing the difficulty of mechanism studies and the uncertainty surrounding predictions of midlatitude weather and climate. In conclusion, we provide a succinct summary and offer suggestions for future research.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190125","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}

{"title":"Research on the Mechanism of the Influence of Thermal Stress on Tourists’ Environmental Responsibility Behavior Intention: An Example from a Desert Climate Region, China","authors":"Dong Li, Pengtao Wang, Jingyun Guan, Xiaoliang Xu, Kaiyu Li","doi":"10.3390/atmos15091116","DOIUrl":"https://doi.org/10.3390/atmos15091116","url":null,"abstract":"The desert climate region attracts a multitude of tourists due to its distinctive landforms and climatic conditions, however, it also presents challenges for environmental protection. This article constructs a theoretical model that examines the influence of thermal stress on tourists’ environmental responsibility behavior intention (ERBI), with anticipated pride and anticipated guilt serving as mediating factors. An empirical study is conducted in Turpan, Xinjiang, which represents a typical inland arid area in China. The results indicate that: (1) thermal stress does not have a significant direct impact on ERBI, nevertheless, anticipated pride and anticipated guilt play crucial mediating roles between thermal stress and this intention. (2) Furthermore, environmental knowledge positively moderates the relationship between anticipated pride, anticipated guilt, and the ERBI. This research contributes to the understanding of how tourists’ anticipatory emotions affect their ERBI in desert climate regions while deepening our comprehension of the driving mechanisms behind such intentions among tourists. Moreover, it provides theoretical references for promoting environmentally responsible behaviors among tourists visiting desert climate regions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"33 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268308","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}

{"title":"Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics","authors":"William A. Gough, Zhihui Li","doi":"10.3390/atmos15091111","DOIUrl":"https://doi.org/10.3390/atmos15091111","url":null,"abstract":"The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"73 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190123","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}

{"title":"Calibration for Improving the Medium-Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity","authors":"Yakai Guo, Changliang Shao, Guanjun Niu, Dongmei Xu, Yong Gao, Baojun Yuan","doi":"10.3390/atmos15091107","DOIUrl":"https://doi.org/10.3390/atmos15091107","url":null,"abstract":"The high spatial complexities of soil temperature modeling over semiarid land have challenged the calibration–forecast framework, whose composited objective lacks comprehensive evaluation. Therefore, this study, based on the Noah land surface model and its full parameter table, utilizes two global searching algorithms and eight kinds of objectives with dimensional-varied metrics, combined with dense site soil moisture and temperature observations of central Tibet, to explore different metrics’ performances on the spatial heterogeneity and uncertainty of regional land surface parameters, calibration efficiency and effectiveness, and spatiotemporal complexities in surface forecasting. Results have shown that metrics’ diversity has shown greater influence on the calibration—predication framework than the global searching algorithm’s differences. The enhanced multi-objective metric (EMO) and the enhanced Kling–Gupta efficiency (EKGE) have their own advantages and disadvantages in simulations and parameters, respectively. In particular, the EMO composited with the four metrics of correlated coefficient, root mean square error, mean absolute error, and Nash–Sutcliffe efficiency has shown relatively balanced performance in surface soil temperature forecasting when compared to other metrics. In addition, the calibration–forecast framework that benefited from the EMO could greatly reduce the spatial complexities in surface soil modeling of semiarid land. In general, these findings could enhance the knowledge of metrics’ advantages in solving the complexities of the LSM’s parameters and simulations and promote the application of the calibration–forecast framework, thereby potentially improving regional surface forecasting over semiarid regions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190154","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}

{"title":"De-Carbonisation Pathways in Jiangxi Province, China: A Visualisation Based on Panel Data","authors":"Shun Li, Jie Hua, Gaofeng Luo","doi":"10.3390/atmos15091108","DOIUrl":"https://doi.org/10.3390/atmos15091108","url":null,"abstract":"Environmental degradation remains a pressing global concern, prompting many nations to adopt measures to mitigate carbon emissions. In response to international pressure, China has committed to achieving a carbon peak by 2030 and carbon neutrality by 2060. Despite extensive research on China’s overall carbon emissions, there has been limited focus on individual provinces, particularly less developed provinces. Jiangxi Province, an underdeveloped province in southeastern China, recorded the highest GDP (Gross Domestic Product) growth rate in the country in 2022, and it holds significant potential for carbon emission reduction. This study uses data from Jiangxi Province’s 14th Five-Year Plan and Vision 2035 to create three carbon emission reduction scenarios and predict emissions. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology), along with various visualisation techniques, is employed to analyse the impacts of population size, primary electricity application level, GDP per capita, the share of the secondary industry in fixed-asset investment, and the number of civilian automobiles owned on carbon emissions. The study found that there is an inverted U-shaped curve relationship between GDP per capita and carbon emissions, car ownership is not a major driver of carbon emissions, and the development of primary electricity has significant potential as a means for reducing carbon emissions in Jiangxi Province. If strict environmental protection measures are implemented, Jiangxi Province can reach its peak carbon target by 2029, one year ahead of the national target. These findings provide valuable insights for policymakers in Jiangxi Province to ensure that their environmental objectives are met.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"73 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190156","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}