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Significant contribution of carbonyls to atmospheric oxidation capacity(AOC)during the winter haze pollution over North China Plain

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Journal of Environmental Sciences 2024年第5期139卷 377-388页

作者： Xue Yang Gen Zhang Guang Pan Guolan Fan Houyong Zhang Xuan Ge Mingyue Du School of Municipal and Environmental Engineering Shandong Jianzhu UniversityJi’nan 250101China State Key Laboratory of Severe Weather&Key Laboratory of Atmospheric Chemistry of China meteorological Administration Chinese Academy of Meteorological SciencesBeijing 100081China Shandong Jinan Ecological Environment Monitoring center Ji’nan 250101China

Atmospheric carbonyl compounds play significant roles in the cycling of radicals and have exhibited surprisingly high levels in winter that were well correlated to particulate matter,for which the reason have not been clearly elucidated.Here we measured carbonyl compounds and other trace gasses together with PM_(2.5)over urban Jinan in North China Plain during the winter.Markedly higher carbonyl concentrations(average:14.63±4.21 ppbv)were found during wintertime haze pollution,about one to three-times relative to those on nonhaze days,with slight difference in chemical composition except formaldehyde(HCHO).HCHO(3.68 ppbv),acetone(3.17 ppbv),and acetaldehyde(CH_3CHO)(2.83 ppbv)were the three most abundant species,accounting for~75% of the total carbonylson both haze and non-haze days.Results from observational-based model(OBM)with atmospheric oxidation capacity(AOC)indicated that AOC significantly increased with the increasing carbonyls during the winter haze events.Carbonyl photolysis have supplied key oxidants such as RO_(2) and HO_(2),and thereby enhancing the formation of fine particles and secondary organic aerosols,elucidating the observed haze-carbonyls inter-correlation.Diurnal variation with carbonyls exhibiting peak values at early-noon and night highlighted the combined contribution of both secondary formation and primary diesel-fuel sources.1-butene was further confirmed to be the major precursor for HCHO.This study confirms the great contribution of carbonyls to AOC,and also suggests that reducing the emissions of carbonyls would be an effective way to mitigate haze pollution in urban area of the NCP region.

关键词： Carbonyls Haze Observation-based model Source apportionment Atmospheric oxidation capacity

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Characteristics of atmospheric nitrous oxide observed at Mt.Waliguan GAW global station in the inland Eurasia during eighteen years

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Science China Earth Sciences 2024年第1期67卷 92-104页

作者： Miao LIANG Shuangxi FANG Lixin LIU Yong ZHANG Jianqiong WANG Shuo LIU Hongyang WANG Liangchun DENG meteorological Observation Centre(MOC) China Meteorological Administration(CMA)Beijing 100081China College of Environment Zhejiang University of TechnologyHangzhou 310014China Mt.Waliguan Background Station China Meteorological Administration(CMA)Xining 810001China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China center for Environmental Progress Wuhan 430070China

This study presents atmospheric N_(2)O mole fractions measured from discrete air samples from 2001 to 2018 at Mt.Waliguan(WLG)station(36°17′N,100°54′E,3816 m asl)in China,which is a global background station of the World meteorological Organization/Global Atmosphere Watch Programme(WMO/GAW)in central Eurasia.Observed N_(2)O characteristics of annual means,interannual variability,and seasonal cycles were investigated.Our results show that N_(2)O at WLG possess a distinct increasing trend and a statistically significant seasonal cycle,with an average growth rate of 0.9±0.01 ppb yr^(−1)(1σ)(1ppb=10^(−9)),which is close to the global mean.The detrended seasonal cycle shows a trough of−0.25±0.04(1σ)ppb in June and a peak of 0.13±0.07(1σ)ppb in September,with an amplitude of 0.38 ppb.The pattern is due to combined effects of variation in surface sources,vertical convection within the boundary layer and stratosphere to troposphere transportation(STE).The interannual variability in growth rate was partly driven by quasi-biennial oscillation(QBO)of tropical zonal wind through stratospheric transport into the troposphere.According to a cluster analysis of back trajectories and the corresponding average N_(2)O load,most air masses cover arid and semi-arid areas in inner Asia with low N_(2)O emissions,indicating that the atmospheric N_(2)O at the WLG represents the background N_(2)O level in central Eurasia.

关键词： Nitrous oxide(N_(2)O) Observation Long-term trend Seasonal variation Interannual variability

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Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental dynamics.Herein, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

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Plant genomic resources at national Genomics Data center:assisting in data-driven breeding applications

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aBIOTECH 2024年第1期5卷 94-106页

作者： Dongmei Tian Tianyi Xu Hailong Kang Hong Luo Yanqing Wang Meili Chen Rujiao Li Lina Ma Zhonghuang Wang Lili Hao Bixia Tang Dong Zou Jingfa Xiao Wenming Zhao Yiming Bao Zhang Zhang Shuhui Song national Genomics Data center Beijing Institute of GenomicsChinese Academy of Sciences&China National Center for BioinformationBeijing 100101China CAS Key Laboratory of Genome Sciences and information Beijing Institute of GenomicsChinese Academy of Sciences&China National Center for BioinformationBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems,including the constituent elements within and among species.Through various efforts in genomic data archiving,integrative analysis and value-added curation,the national Genomics Data center(NGDC),which is a part of the China national center for Bioinformation(CNCB),has successfully established and currently maintains a vast amount of database resources.This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts.Here,we present a comprehensive overview of central repositories dedicated to archiving,presenting,and sharing plant omics data,introduce knowledgebases focused on variants or gene-based functional insights,highlight species-specific multiple omics database resources,and briefly review the online application tools.We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study.

关键词： Plant-omics data Data repositories Data integration Knowledgebase Plant genomics

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An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

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Advances in Atmospheric Sciences 2024年第3期41卷 545-563页

作者： Xin LIU Jing CHEN Yongzhu LIU Zhenhua HUO Zhizhen XU Fajing CHEN Jing WANG Yanan MA Yumeng HAN School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing 210044China Chinese Academy of meteorological Sciences Beijing 100081China CMA Earth System Modeling and Prediction Centre(CEMC) Beijing 100081China Key Laboratory of Earth System Modeling and Prediction of China meteorological Administration Beijing 100081China Tianjin meteorological Observatory Tianjin 300074China China meteorological Administration Xiong’an Atmospheric Boundary Layer Key Laboratory Hebei 071799China

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

关键词： multiscale uncertainty singular vector initial perturbation global ensemble prediction system

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Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

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Atmospheric and Oceanic Science Letters 2024年第4期17卷 52-57页

作者： Meng Zhou Jingya Wu Mingxuan Chen Lei Han Faculty of information Science and Engineering Ocean University of ChinaQingdaoChina Institute of Urban Meteorology China Meteorological AdministrationBeijingChina

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进... 详细信息

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

关键词：保度学习卷积长短期记忆单元卷积门控循环单元分类问题

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Winter extreme precipitation over the Tibetan Plateau influenced by Arctic sea ice on interdecadal timescale

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Advances in Climate Change Research 2024年第1期15卷 42-51页

作者： Qing-Quan LI Miao BI Song YANG Qing-Yuan WU Yi-Hui DING Xin-Yong SHEN Xiao-Ting SUN Meng-Chu ZHAO China meteorological Administration Key Laboratory for Climate Prediction Studies National Climate CentreBeijing 100081China Collaborative Innovation center on Forecast and Evaluation of meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China CMA Key Open Laboratory of Transforming Climate Resources to Economy Chongqing Institute of Meteorological SciencesChongqing 401147China School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University Zhuhai 519082China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China

The Tibetan Plateau(TP)and the Arctic are the most sensitive regions to global climate change.However,the interdecadal varibility of winter extreme precipitation over the TP and its linkage with Arctic sea ice are still unclear.In this study,the characteristics and mechisnems of the TP extreme precipitation(TPEP)influenced by Arctic sea ice on interdecadal timescale are studied based on the daily precipitation,monthly sea ice concentration and ERA5 reanalysis data from 1980 to 2018.We found that the dominant mode of the TPEP in winter mostly exhibits a uniform spatial variation on the interdecadal timescale,with an opposite weak variation in the southeastern TP,and the Arctic sea ice concentration(SIC)before 2002 are larger than that after 2003.The interdecadal variation of TPEP is affected by two teleconnection wave trains regulated by the Barents and Kara Sea ice.In the light ice years,a remarkable positive geopotential height(HGT)anomaly appears over the Barents-Kara Sea and a remarkable negative HGT anomaly is located over the Lake Baikal.Two wave trains originating over the Barents-Kara Sea can be observed.The southern branch forms a wave train through the North Atlantic along the subtropical westerly jet stream,showing a‘+-+-+'pattern of HGT anomalies from Arctic to the TP.Negative HGT anomaly controls the western TP,which creates dynamic and water vapor conditions for the TPEP.The northern branch forms a wave train through the Lake Baikal and the southeast of the TP,showing a‘+-+'HGT anomaly distribution.Positive HGT anomaly controls the southeastern TP,which is not conducive to precipitation in the region.When the SIC in the Barents-Kara Sea increases,the situation is opposite.The above analysis also reveals the reason for the difference in the east-west distribution of the TPEP.

关键词： Arctic sea ice Tibetan Plateau Winter extreme precipitation Rossby wave activity Interdecadal variation

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Predominant types of regional cold waves in North China and their historical changes

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Atmospheric and Oceanic Science Letters 2024年第4期17卷 46-51页

作者： Xinyi Cheng Yiyi Zhang Jiandong Li Xin Hao center for Climate System Prediction Research/Collaborative Innovation center on Forecast and Evaluation of meteorological Disasters/Key Laboratory of meteorological Disaster Ministry of EducationNanjing University of Information Science&TechnologyNanjingChina Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjingChina

寒潮事件对东亚地区的社会经济,生态系统和人体健康影响巨大.根据1980-2019年间10月至次年3月的每日寒潮记录和K-means聚类,本文识别出了中国两种不同类型的区域性寒潮(T1和T2).T1区域性寒潮主要影响东北地区,T2区域性寒潮则主要影响华... 详细信息

寒潮事件对东亚地区的社会经济,生态系统和人体健康影响巨大.根据1980-2019年间10月至次年3月的每日寒潮记录和K-means聚类,本文识别出了中国两种不同类型的区域性寒潮(T1和T2).T1区域性寒潮主要影响东北地区,T2区域性寒潮则主要影响华北和东部地区.与T1区域性寒潮相比T2区域性寒潮强度更强,持续时间更长,影响范围更广.1980-2019年期间,T1区域性寒潮的频率显著增加,而T2区域性寒潮的频率则没有趋势变化T1和T2区域性寒潮事件均与西伯利亚高压增强有关,然而与它们相关的对流层中层波列明显不同.在T1区域性寒潮事件发生期间,西伯利亚蒙古上空出现负-正模态的500-hPa位势高度异常波列,削弱了东亚大槽,导致西伯利亚冷空气东移.T1区域寒潮频次增加趋势可能与全球变暖弓|起的500-hPa位势高度的线性趋势变化有关与T2区域寒潮事件相关的波列则在乌拉尔山脉,蒙古和华北地区形成了脊_槽脊环流异常,导致冷空气向东南方向入侵.本文得出结论,由于两类区域寒潮影响不同,西伯利亚高压增强和对流层中层波列模态在我国区域性寒潮事件的预报中应综合考虑.

关键词：寒潮 K-means聚类西伯利亚高压波列

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Simulations of superconducting quantum gates by digital flux tuner for qubits

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Chinese Physics B 2024年第7期33卷 210-222页

作者：耿霄何楷泳刘建设陈炜 Laboratory of Superconducting Quantum information Processing School of Integrated CircuitsTsinghua UniversityBeijing 100084China Beijing Innovation center for Future Chips Tsinghua UniversityBeijing 100084China Beijing national Research center for information Science and Technology Beijing 100084China26

The interconnection bottleneck caused by limitations of cable number, inner space and cooling power of dilution refrigerators has been an outstanding challenge for building scalable superconducting quantum computers with the increasing number of qubits in quantum processors. To surmount such an obstacle, it is desirable to integrate qubits with quantum–classical interface(QCI) circuits based on rapid single flux quantum(RSFQ) circuits. In this work, a digital flux tuner for qubits(DFTQ) is proposed for manipulating flux of qubits as a crucial part of the interface circuit. A schematic diagram of the DFTQ is presented, consisting of a coarse tuning unit and a fine-tuning unit for providing magnetic flux with different precision to qubits. The method of using DFTQ to provide flux for gate operations is discussed from the optimization of circuit design and input signal. To verify the effectiveness of the method, simulations of a single DFTQ and quantum gates including a Z gate and an iSWAP gate with DFTQs are performed for flux-tunable transmons. The quantum process tomography corresponding to the two gates is also carried out to analyze the sources of gate error. The results of tomography show that the gate fidelities independent of the initial states of the Z gate and the iSWAP gate are 99.935% and 99.676%,respectively. With DFTQs inside, the QCI would be a powerful tool for building large-scale quantum computers.

关键词： quantum computation superconducting electronics quantum control

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Advances in neuromorphic computing:Expanding horizons for AI development through novel artificial neurons and in-sensor computing

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Chinese Physics B 2024年第3期33卷 1-23页

作者：杨玉波赵吉哲刘胤洁华夏扬王天睿郑纪元郝智彪熊兵孙长征韩彦军王健李洪涛汪莱罗毅 Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing national Research center for information Science and Technology Tsinghua UniversityBeijing 100084China

AI development has brought great success to upgrading the information age.At the same time,the large-scale artificial neural network for building AI systems is thirsty for computing power,which is barely satisfied by the conventional computing hardware.In the post-Moore era,the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits(VLSIC)is challenging to meet the growing demand for AI computing power.To address the issue,technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture,and dealing with AI algorithms much more parallelly and energy efficiently.Inspired by the human neural network architecture,neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices.Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network(SNN),the development in this field has incubated promising technologies like in-sensor computing,which brings new opportunities for multidisciplinary research,including the field of optoelectronic materials and devices,artificial neural networks,and microelectronics integration technology.The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing.This paper reviews firstly the architectures and algorithms of SNN,and artificial neuron devices supporting neuromorphic computing,then the recent progress of in-sensor computing vision chips,which all will promote the development of AI.

关键词： neuromorphic computing spiking neural network(SNN) in-sensor computing artificial intelligence

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