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Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

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Advances in Climate Change Research 2024年第4期15卷 669-679页

作者： Jin-Lin ZHA Ting CHUAN Yuan qiu Jian WU De-Ming ZHAO Wen-Xuan FAN Yan-Jun LYU Hui-Ping JIANG Kai-Qiang DENG Miguel ANDRES-MARTIN Cesar AZORIN-MOLINA Deliang CHEN Key Laboratory of atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region Department of Atmospheric ScienceYunnan UniversityKunming 650091China Key Laboratory of Regional Climate and Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Center for Energy&Environmental Policy Research Beijing Institute of Technology Beijing 100081China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China School of atmospheric sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Centro de Investigaciones Sobre Desertificacion Consejo Superior de Investigaciones Cientificas(CIDECSIC-UV-Generalitat Valenciana)ClimateAtmosphere and Ocean Laboratory(Climatoc-Lab)Moncada 46113Spain Regional Climate Group.Deparment of Earth sciences University of GothenburgGothenburg 40530Sweden

Wind energy development in Central Asia can help alleviate drought and fragile ***,current studies mainly used the global climate models(GCMs)to project wind speed and *** simulated biases in GCMs remain prominent,which induce a large uncertainty in the projected *** reduce the uncertainties of projected near-surface wind speed(NSW)and better serve the wind energy development in Central Asia,the Weather Research and Forecasting(WRF)model with bias-corrected GCMs was *** with the outputs of GCMs,dynamical downscaling acquired using the WRF model can better capture the high-and low-value centres of NSWS,especially those of Central Asia's ***,the simulated NSWS bias was also *** future changes in wind speed and wind energy,under the Representative Concentration Pathway 4.5(RCP4.5)scenario,NSWS during 2031-2050 is projected to decrease compared with that in *** magnitude of NSwS reduction during 2031-2050 willreach 0.1 m s^(-1).and the maximum reduction is projected to occur over the central and western regions(>0.2 m s^(-1)).Furthermore,future wind power density(WPD)can reveal nonstationarity and strong volatility,although a downward trend is expected during *** addition,the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s^(-1)can render the plain regions more suitable for wind energy development than the mountains from 2031 to *** study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

关键词： Near-surface wind speed Wind power density Dynamical downscaling Central Asia WRF

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

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Fundamental Research 2024年第3期4卷 430-441页

作者： Jianping Huang Danfeng Wang Yongguan Zhu Zifeng Yang Maosheng Yao Xiaoming Shi Taicheng An Qiang Zhang Cunrui Huang Xinhui Bi Jiang Li Zifa Wang Yongqin Liu Guibing Zhu Siyu Chen Jian Hang Xinghua qiu Weiwei Deng Huaiyu Tian Tengfei Zhang Tianmu Chen Sijin Liu Xinbo Lian Bin Chen Beidou Zhang Yingjie Zhao Rui Wang Han Li Collaborative Innovation Center for Western Ecological Safety College of Atmospheric SciencesLanzhou UniversityLanzhou 730000China Research Center for Eco-Environmental sciences Chinese Academy of SciencesBeijing 100085China National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory HealthFirst Affiliated Hospital of Guangzhou Medical UniversityState Key Laboratory of Respiratory Disease(Guangzhou Medical University)Guangzhou 510230China College of Environmental sciences and Engineering Peking UniversityBeijing 100871China China CDC Key Laboratory of Environment and Population Health National Institute of Environmental HealthChinese Center for Disease Control and PreventionBeijing 100021China Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Vanke School of Public Health Tsinghua UniversityBeijing 100084China State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization Guangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou 510640China National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China State Key Laboratory of atmospheric Boundary Layer physics and atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Center for Pan-third Pole Environment Lanzhou UniversityLanzhou 730000China College of atmospheric sciences Lanzhou UniversityLanzhou 730000China School of atmospheric sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 510640

Corona virus disease 2019(COVID-19)has exerted a profound adverse impact on human *** have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among *** monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic *** of pathogenic microorganisms in the air,especially in densely populated areas,may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier *** multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation,allocate health resources,and formulate epidemic response *** review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission,which lays a theoretical foundation for the monitoring and prediction of epidemic ***,the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and ***,this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology,atmospheric sciences,environmental sciences,sociology,demography,*** summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere,this review proposes suggestions for epidemic response,namely,the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

关键词： Aerosol transmission Epidemic monitoring methods Epidemic prediction modelsEpidemic response Multidisciplinary cooperation

基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测

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太阳能学报 2024年第3期45卷 73-82页

作者：邱文智张文煜郭振海赵晶马可可郑州大学计算机与人工智能学院郑州450052 郑州大学地球科学与技术学院郑州450052 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室北京100029

针对风速的波动性和随机性等特点,提出一种基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测方法。该方法的基本思路是构造基于变分模态分解、样本熵和奇异谱分析的二次分解的方法,将原始风速序列分解为不同的子序列,并对这些... 详细信息

针对风速的波动性和随机性等特点,提出一种基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测方法。该方法的基本思路是构造基于变分模态分解、样本熵和奇异谱分析的二次分解的方法,将原始风速序列分解为不同的子序列,并对这些子序列分别建立预测模型,最后重构。对变分模态分解的子序列建立基于长短时记忆网络的深度学习模型预测,而残差序列进行二次分解后的子序列建立乌鸦搜索算法优化的组合预测模型预测。最后,对子序列进行重构并得到最终的预测结果。使用实际的风速观测资料开展模拟实验,结果表明:在3个风电场中,所提模型与其他模型相比平均相对误差分别提升了30.07%、37.56%和37.4.%,验证了混合模型在超短期风速预测中的有效性和稳定性,以及在不同数据集上的泛化性能。

关键词：风速预测长短时记忆二次分解乌鸦搜索算法组合预测模型

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一次极端暴雨过程低空急流变化机制分析

大气科学 2024年第4期48卷 1608-1626页

作者：田利庆李舒文邱晓滨冉令坤申冬冬刘宇琛河北省气象与生态环境重点实验室石家庄050021 中国气象局雄安大气边界层重点开放实验室雄安新区071800 河北省气象科学研究所石家庄050021 中国科学院大气物理研究所北京100029 天津市海洋气象重点实验室天津300074 中国科学院大学北京100049 云南省大湄公河次区域气象灾害与气候资源重点实验室云南大学昆明650500

2021年7月19~21日河南极端暴雨伴有显著的低空急流活动。本文利用ERA5(Fifth Generation of ECMWF Reanalyses)数据,采用等压坐标系水平运动方程、动能方程和位势高度梯度方程分析影响低空急流发展的关键物理因素。分析表明,低空急流中... 详细信息

2021年7月19~21日河南极端暴雨伴有显著的低空急流活动。本文利用ERA5(Fifth Generation of ECMWF Reanalyses)数据,采用等压坐标系水平运动方程、动能方程和位势高度梯度方程分析影响低空急流发展的关键物理因素。分析表明,低空急流中心首先从郑州附近925 hPa发展起来,然后垂直倾斜地向西北方向发展增强,中心位置先后抬升到850 hPa和700 hPa。低空急流纬向风和经向风局地变化主要受位势高度水平梯度的影响。郑州西侧山区存在位势高度低值系统,郑州东北侧平原地区为位势高度高值区,二者在郑州附近产生显著的纬向梯度力,与纬向东风耦合做功,驱动低空急流动能增长。位势高度这种异常分布主要与地表位势高度和近地层虚位温垂直积分有关。位势高度纬向梯度方程计算表明,垂直速度纬向梯度引起的位温垂直平流和非绝热加热纬向梯度项是导致位势高度纬向梯度局地变化的主要强迫项。总体上,近地面层山区相对低温区和平原地区相对高温区对低空急流发展演变有重要影响。

关键词：极端暴雨低空急流动能位势高度梯度

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High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

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Advances in atmospheric sciences 2022年第10期39卷 1734-1745页

作者： Yuan qiu Jinming FENG Zhongwei YAN Jun WANG Key Laboratory of Regional Climate-Environment for Temperate East Asia(RCE-TEA) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia(CA),six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project(CMIP5),and their changes in the near-term future(2031-50)are assessed relative to the reference period(1986-2005).The quantile mapping(QM)method is applied to correct the model data before calculating the *** show the QM method largely reduces the biases in all the *** season length(GSL,day),summer days(SU,day),warm spell duration index(WSDI,day),and tropical nights(TR,day)are projected to significantly increase over CA,and frost days(FD,day)are projected to ***,changes in biologically effective degree days(BEDD,°C)are spatially *** high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

关键词： agroclimatic indicators Central Asia near-term future agriculture quantile mapping

Projection of weather potential for winter haze episodes in beijing by 1.5℃ and 2.0℃ global warming

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Advances in Climate Change Research 2020年第3期11卷 218-226页

作者： qiu Liang YUE Xu HUA Wei LEI Ya-Dong School of atmospheric sciences Chengdu University of Information and TechnologyChengdu610225China Jiangsu Key Laboratory of atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environment Science and EngineeringNanjing University of Information Science&TechnologyNanjing210044China Climate Change Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China University of chinese academy of sciences Beijing100049China

Haze episodes become very frequent in beijing over the past decade,and such trend is related to favorable weather ***,we project the changes of weather conditions conducive to winter haze episodes in beijing by 1.5℃ and 2.0℃ global warming using Haze Weather Index(HWI)and data of ensemble simulations from the Community Earth System Model(CESM)low-warming *** to present day(2006–2015),the frequency in winter season is projected to increase by 14. for regular haze episodes(HWI>0)and 21% for severe haze episodes(HWI>1)at the 1.5℃ global *** shows larger increases of 27% for regular and 18%for severe haze events at the 2℃ global *** additional warming of 0.5℃ largely enhances the persistence of weather conditions conducive to haze *** increased temperature contrast between near-surface and mid-troposphere in eastern Asia accounts for 57% and 81% of the change in HWI by 1.5℃ and 2℃ warming,*** increased haze weather potential caused by climate warming,we suggest that additional efforts in emission reductions of carbon dioxide and air pollution are necessary to mitigate haze episodes in beijing.

关键词： Haze 1.5℃global warming 2℃global warming China Community earth system model(CESM)

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基于灰狼算法和极限学习机的风速多步预测

郑州大学学报（工学版） 2024年第2期45卷 89-96页

作者：张文煜马可可郭振海赵晶邱文智郑州大学地球科学与技术学院河南郑州450001 郑州大学计算机与人工智能学院河南郑州450001 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室北京100029

为了提高风速的多步预测水平,提出了一种基于数据信号分解和灰狼算法优化极限学习机的混合预测模型。首先,使用具有自适应噪声的完全集成经验模态分解算法将原始风速时间序列分解为若干本征模态函数和一个残差序列,并使用偏自相关函数... 详细信息

为了提高风速的多步预测水平,提出了一种基于数据信号分解和灰狼算法优化极限学习机的混合预测模型。首先,使用具有自适应噪声的完全集成经验模态分解算法将原始风速时间序列分解为若干本征模态函数和一个残差序列,并使用偏自相关函数法对模型输入进行特征选择;其次,在分解子序列上分别建立模型并进行预测,构造多输入多输出策略的极限学习机神经网络,使用灰狼优化算法求解其中的最优化隐含层权值和偏置;最后,对子序列进行重构并得到最终的预测结果。使用时间分辨率为15 min的多组实测资料开展模拟实验,所提模型在3个风电场的均方根误差分别为0.859、0.925、0.927 m/s,均低于其他对比模型,验证了该模型在未来4.h风速预测即16步预测中的有效性。

关键词：风速预测多步预测信号分解特征选择灰狼优化算法极限学习机

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Characteristics and Preliminary Causes of Tropical Cyclone Extreme Rainfall Events over Hainan Island

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Advances in atmospheric sciences 2018年第5期35卷 580-591页

作者： Xianling JIANG Fumin REN Yunjie LI Wenyu qiu Zhuguo MA Qinbo CAI Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China University of chinese academy of sciences Beijing 100049 China State Key Laboratory on Severe Weather Chinese Academy of Meteorological Sciences Beijing 100081 China Hainan Meteorological Observatory Haikou 570203 China Zhuhai Meteorological Bureau Zhuhai 519000 China Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China

The characteristics of tropical cyclone（TC） extreme rainfall events over Hainan Island from 1969 to 2014.are analyzed from the viewpoint of the TC maximum daily rainfall（TMDR） using daily station precipitation data from the Meteorological Information Center of the China Meteorological Administration, TC best-track data from the Shanghai Typhoon Institute,and NCEP/NCAR reanalysis data. The frequencies of the TMDR reaching 50, 100 and 250 mm show a decreasing trend[-0.7（10 yr）^（-1）], a weak decreasing trend [-0.2（10 yr）^（-1）] and a weak increasing trend [0.1（10 yr）^（-1）], respectively. For seasonal variations, the TMDR of all intensity grades mainly occurs from July to October, with the frequencies of TMDR 50 mm and 100 mm peaking in September and the frequency of TMDR 250 mm [TC extreme rainstorm（TCER） events]peaking in August and September. The western region（Changjiang） of the Island is always the rainfall center, independent of the intensity or frequencies of different intensity grades. The causes of TCERs are also explored and the results show that topography plays a key role in the characteristics of the rainfall events. TCERs are easily induced on the windward slopes of Wuzhi Mountain, with the coordination of TC tracks and TC wind structure. A slower speed of movement, a stronger TC intensity and a farther westward track are all conducive to extreme rainfall events. A weaker northwestern Pacific subtropical high is likely to make the 500-h Pa steering flow weaker and results in slower TC movement, whereas a stronger South China Sea summer monsoon can carry a higher moisture flux. These two environmental factors are both favorable for TCERs.

关键词： Hainan Island tropical cyclones extreme rainfall events characteristics causes

Comparison of Ozone and PM2.5 Concentrations over Urban,Suburban,and Background Sites in China

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Advances in atmospheric sciences 2020年第12期37卷 1297-1309,I0001-I0004页

作者： Lan GAO Xu YUE Xiaoyan MENG Li DU Yadong LEI Chenguang TIAN Liang qiu Climate Change Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of chinese academy of sciences Beijing 100049China Jiangsu Key Laboratory of atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environment Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China China National Environmental Monitoring Center Beijing 100012China School of atmospheric sciences Chengdu University of Information and TechnologyChengdu 610225China

Surface ozone(O3)and fine particulate matter(PM2.5)are dominant air pollutants in *** of these pollutants can show significant differences between urban and nonurban ***,such contrast has never been explored on the country *** study investigates the spatiotemporal characteristics of urban-to-suburban and urban-tobackground difference for O3(Δ[O3])and PM2.5(Δ[PM2.5])concentrations in China using monitoring data from 1171 urban,110 suburban,and 15 background sites built by the China National Environmental Monitoring Center(CNEMC).On the annual mean basis,the urban-to-suburbanΔ[O3]is−3.7 ppbv in beijing-Tianjin-Hebei,1.0 ppbv in the Yangtze River Delta,−3.5 ppbv in the Pearl River Delta,and−3.8 ppbv in the Sichuan *** the contrary,the urban-to-suburbanΔ[PM2.5]is 15.8,−0.3,3.5 and 2.4.g m^−3 in those areas,*** urban-to-suburban contrast is more significant in winter for bothΔ[O3]andΔ[PM2.5].In eastern China,urban-to-background differences are also moderate during summer,with−5.1 to 6.8 ppbv forΔ[O3]and−0.1 to 22.5μg m^−3 forΔ[PM2.5].However,such contrasts are much larger in winter,with−22.2 to 5.5 ppbv forΔ[O3]and 3.1 to 82.3μg m^−3 forΔ[PM2.5].Since the urban region accounts for only 2%of the whole country’s area,the urban-dominant air quality data from the CNEMC network may overestimate winter[PM2.5]but underestimate winter[O3]over the vast domain of *** study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health,which require more data outside urban areas.

关键词： ozone PM2.5 urban suburban background