Characterising climate change discourse on social media during extreme weather events     

《Global Environmental Change》2019

When extreme weather events occur, people often turn to social media platforms to share information, opinions and experiences. One of the topics commonly discussed is the role climate change may or may not have played in influencing an event. Here, we examine Twitter posts that mentioned climate change in the context of three high-magnitude extreme weather events – Hurricane Irene, Hurricane Sandy and Snowstorm Jonas – in order to assess how the framing of the topic and the attention paid to it can vary between events. We also examine the role that contextual factors can play in shaping climate change coverage on the platform. We find that criticism of climate change denial dominated during Irene, while political and ideological struggle frames dominated during Sandy. Discourse during Jonas was, in contrast, more divided between posts about the scientific links between climate change and the events, and posts contesting climate science in general. The focus on political and ideological struggle frames during Sandy reflects the event’s occurrence at a time when the Occupy movement was active and the 2012 US Presidential Election was nearing. These factors, we suggest, could also contribute to climate change being a more prominent discussion point during Sandy than during Irene or Jonas. The Jonas frames, meanwhile, hint at lesser public understanding of how climate change may influence cold weather events when compared with tropical storms. Overall, our findings demonstrate how event characteristics and short-term socio-political context can play a critical role in determining the lenses through which climate change is viewed.  相似文献   

高分辨率统计降尺度数据集NEX-GDDP对中国极端温度指数模拟能力的评估   总被引：2，自引：0，他引：2  

李金洁  王爱慧  郭东林  王丹 《气象学报》2019,77(3):579-593

利用1986—2005年中国地面气象台站观测的格点化逐日气温资料（CN05.1）评估了高分辨率统计降尺度数据集NASA Earth Exchange/Global Daily Downscaled Projections（NEX-GDDP）中21个全球气候模式对中国极端温度指数的模拟能力。在选用了日最低温度最大值（TNx）、日最高温度最大值（TXx）、暖夜指数（TN90p）和暖昼指数（TX90p）来研究极端温度事件的变化。结果显示:（1）除MRI-CGCM3模拟的日最高温度最大值外,其余模式对4个指数的模拟结果均表现出与观测一致的上升趋势,但模拟结果的平均值相对观测平均低0.26℃/（10 a）（日最低温度最大值）、0.19℃/（10 a）（日最高温度最大值）、2.21%/（10 a）（暖夜指数）、1.04%/（10 a）（暖昼指数）。（2）不同模式对各指数变化趋势空间分布特征的模拟存在较大差别,对日最低温度最大值、日最高温度最大值、暖夜指数和暖昼指数模拟能力最优模式分别为CCSM4、CESM1-BGC、MIROC-ESM-CHEM和bcc-csm1-1。模式模拟的日最低温度最大值和日最高温度最大值气候态平均值与观测值的相关系数在0.97以上。暖夜指数和暖昼指数模拟结果与观测值的标准差比值为0.34—1.58,均方根误差变化为1.6%—3.47%,对这两个指数模拟能力较优的模式分别为MIROC-ESM-CHEM（暖夜指数）和CESM1-BGC（暖昼指数）。（3）综合模式对4个指数在气候态平均值和变化趋势模拟能力的评估结果来看,CanESM2、CESM1-BGC和MIROC-ESM-CHEM显示了相对较高的模拟能力。因此,在利用GDDP-NEX研究未来极端温度事件时,建议将它们作为优选模式。   相似文献   

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia     

Ahmed Mohamed Youssef  Hamid Reza Pourghasemi 《地学前缘(英文版)》2021,12(2):639-655

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.  相似文献   

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization     

Wengang Zhang  Chongzhi Wu  Haiyi Zhong  Yongqin Li  Lin Wang 《地学前缘(英文版)》2021,12(1):469-477

Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.  相似文献   

Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods     

《New Astronomy》2022

A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA’s Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system is trained initially with Kepler exoplanetary data and validated with confirmed exoplanets before its application to TESS data. Existing and new features of the data, based on various observational parameters, are constructed and used in the AI/ML analysis by employing semi-supervised and unsupervised machine learning techniques. By the application of ThetaRay system to 10,803 light curves of threshold crossing events (TCEs) produced by the TESS mission, obtained from the Mikulski Archive for Space Telescopes, the algorithm yields about 50 targets for further analysis, and we uncover three new exoplanetary candidates by further manual vetting. This study demonstrates for the first time the successful application of the particular combined multiple AI/ML-based methodologies to a large astrophysical dataset for rapid automated classification of TCEs.  相似文献   

Joint effects of three oceans on the 2020 super mei-yu          下载免费PDF全文

Hua Li  Bo Sun  Huijun Wang  Xing Yuan 《大气和海洋科学快报》2022,15(1):12-20

2020年发生在江淮流域,朝鲜半岛和日本南部(简称梅雨区)的暴力梅造成了巨大的人员伤亡和经济损失.此次暴力梅的主要特征为:入梅早(6月1日),出梅晚(8月1日)以及较强的梅雨期降水.2020年异常早入梅和晚出梅时期的降水占梅雨期总降水的一半以上.因此,为了深入解析2020暴力梅的机制,本文将分析2020异常早入梅和晚出...  相似文献   

秋季北极海冰对中国冬季气温的影响   总被引：7，自引：0，他引：7  

谢永坤  刘玉芝  黄建平 《气象学报》2014,72(4):703-710

利用海冰资料、中国地面气候资料、环流特征量资料及NCEP/NCAR再分析资料,研究了秋季北极海冰变化对中国冬季平均气温、日气温变率以及异常低温天气的影响。分析结果表明,秋季北极海冰异常偏多年中国冬季常为暖冬;异常偏少年中国冬季常为冷冬,且异常低温天气出现频率更高,常发生低温灾害事件。秋季北极海冰通过影响后期的北半球极涡、东亚冬季风和西伯利亚高压进而影响中国冬季的平均气温,且通过影响冬季异常强西伯利亚高压的出现频次,影响中国冬季异常低温天气的发生频次。合成分析结果表明,秋季北极海冰异常偏少年的冬季,中国以北亚欧大陆高纬度的偏北风较强,且中国及其以北的中高纬度地区空气异常偏冷,导致极地和高纬度的冷空气易向南爆发,造成中国冬季气温偏低,异常低温天气频发。  相似文献   

INDEX TO CONTENTS OF 2014 JMR          下载免费PDF全文

《Acta Meteorologica Sinica》2014,(6)

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中国干旱半干旱区洪涝灾害的初步分析   总被引：5，自引：1，他引：5  

黄建平  冉津江  季明霞 《气象学报》2014,72(6):1096-1107

中国干旱半干旱区的洪涝灾害是一个尚未引起人们重视的重大科学问题,这主要是因为干旱半干旱区对洪涝灾害的防范意识比较薄弱。而极端降水事件的次数、强度和持续时间与干旱半干旱区的洪涝灾害有密切联系,直接影响该区域洪涝灾害及其次生地质灾害的次数与严重程度。以干旱半干旱区的极端降水事件为切入点,分析了中国干旱半干旱区的极端降水事件次数和极端降水量的变化特征,旨在为干旱半干旱区的洪涝灾害研究提供科学依据。结果表明,进入21世纪以来,中国110°E以西的干旱半干旱区极端降水事件的日数有所增多,而110°E以东的区域日数都有所减少。干旱半干旱区极端降水量的变化也呈现出西增东减的分布,大部分干旱半干旱区的极端降水量变化占总降水量变化的40%以上,一部分地区能达到50%,甚至100%-200%。从季节变化来看,春季天山以北、新疆南部、甘肃敦煌和内蒙古包头以北地区极端降水量增加较多,夏季110°E以西的干旱半干旱区极端降水量均增大明显,秋季陕西榆林、内蒙古鄂尔多斯、包头和呼和浩特等地极端降水量增大较明显。  相似文献   

MM5V3模式对IPCCA1B情景下中国地区极端事件的模拟和预估   总被引：1，自引：1，他引：0       下载免费PDF全文

胡伯彦  汤剑平  王淑瑜 《地球物理学报》2013,56(7):2195-2206

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式,对中国地区1978-2000年及IPCC A1B情景下2038-2070年气候分别进行了水平分辨率为50 km的模拟试验.文章首先检验了模式模拟的当代极端气候结果,在此基础上对6个极端温度指数和6个极端降水指数的未来变化进行了预估.检验结果表明:MM5V3模式对中国地区当代日最高、最低温度及强降水(大雨和暴雨)日数的空间分布和概率特征均具有一定的模拟能力,但模拟的日最高温度在大部分地区偏低,日最低温度在南方地区偏低、西北地区偏高.概率统计结果显示日最高温度向低值频段偏移,日最低温度在0℃的峰值附近明显偏高.模式对大雨和暴雨年平均日数的模拟在东部地区偏多,概率统计结果则为一致偏大.未来中国地区极端气候预估结果表明:极端高温、极端低温和相对高温在全国范围内都将升高,且线性趋势均为上升;霜日日数则为减少,并具有下降趋势;暖日日数和相对低温在青藏高原和新疆部分地区有所减少、其它地区均为增加,且线性趋势暖日日数为上升,相对低温不明显.极端降水指数的变化具有区域特征,其中单日最大降水、连续五日最大降水、最长无雨期、强降水日数、简单降水强度和极端降水总量均在江淮、华南及西南地区有所增多,而在东北及内蒙古地区有所减少,未来中国南方地区降水的极端化趋势将更加显著.极端降水指数的线性趋势除最长无雨期外其它均为上升.  相似文献