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Identifying Three Shapes of Potential Vorticity Streamers Using Mask R-CNN

Advances in Atmospheric sciences 2025年第1期42卷 190-203页

作者： Luqiang HAO Zuowei XIE Yuanfa GONG Jinfang YIN College of Atmospheric Science Chengdu University of Information TechnologyChengdu 610225China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China international center for climate and environment sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Potential vorticity(PV)streamers are elongated filaments of high PV intrusions that generally exhibit three distinct shapes:ordinarily southwestward,hook,and treble-clef,each with significant influences on *** PV streamers are most frequent over arid and semi-arid Central Asia in the mid–high *** study applied the Mask Region-based Convolutional Neural Network algorithm(Mask R-CNN)to PV streamers on the dynamical tropopause during the warm season(May to September)over the years 2000–04 to train a weighted variational model capable of identifying these different *** trained model demonstrated a strong ability to distinguish between the three shapes.A climatological analysis of PV streamers over Central Asia spanning 2000 to 2021 revealed an increasingly deep and pronounced reversal of circulation from ordinary to treble-clef *** treble-clef shape featured a PV tower and distinct cut-off low in the troposphere,but the associated upward motions and precipitation were confined within approximately 1200 km to the east of the PV *** the hook-shape PV streamers were linked to a weaker cut-off low,the extent of upward motion and precipitation was nearly double that of the treble-clef *** contrast,the ordinary PV streamer was primarily associated with tropopause Rossby wave breaking and exhibited relatively shallow characteristics,which resulted in moderate upward motion and precipitation to 500 km to its east.

关键词： Rossby wave breaking potential vorticity streamers Mask-RCNN Central Asia dynamical tropopause

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CAS-ESM2.0 Dataset for the G1ext Experiment of the Geoengineering Model Intercomparison Project(GeoMIP)

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Advances in Atmospheric sciences 2025年第3期42卷 579-592页

作者： Min CUI Duoying JI John CMOORE He ZHANG Jiangbo JIN Kece FEI Chenglai WU Jiawen ZHU Juanxiong HE Zhaoyang CHAI Dongling ZHANG College of Earth System Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Arctic Centre University of LaplandRovaniemi 96200Finland international center for climate and environment sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Solar radiation modification,a scheme aimed at mitigating rapid global warming triggered by anthropogenic greenhouse gas emissions,has been explored through the G1ext experiment under the Geoengineering Model Intercomparison Project(GeoMIP) framework,utilizing the Chinese Academy of sciences Earth System Model version 2(CAS-ESM2.0).This paper briefly describes the basic configuration and experimental design of the CAS-ESM2.0 for G1ext,which involves a sudden reduction in solar irradiance to counterbalance the radiative forcing of an abrupt quadrupling of atmospheric CO_(2) concentration,running for 100 *** results show that this model can reproduce well the compensatory effect of a uniform decrease in global solar radiation on the radiative forcing resulting from an abrupt quadrupling of CO_(2) *** other Earth system models,CAS-ESM2.0 reasonably captures variations in radiative adjustments,surface air temperature,and precipitation patterns,both globally and locally,under the G1ext *** generated datasets have been released on the Earth System Grid Federation data server,providing insight into the potential efficacy and impact of solar geoengineering strategies.

关键词： CAS-ESM2.0 GeoMIP geoengineering radiative adjustment temperature precipitation

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Effect of preprocessing on performances of machine learning-based mineral composition analysis on gas hydrate sediments, Ulleung Basin, East Sea

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Petroleum Science 2025年第1期22卷 151-162页

作者： Hongkeun Jin Ju Young Park Sun Young Park Byeong-Kook Son Baehyun Min Kyungbook Lee Department of Geoenvironmental sciences Kongju National University Tunnel & Underground Facility Division ESCO Consultant & Engineers Company Yellow Sea Institute of Geoenvironmental sciences Petroleum and Marine Research Division Korea Institute of Geoscience and Mineral Resources Department of climate and Energy Systems Engineering Ewha Womans University center for climate/environment Change Prediction Research Ewha Womans University

Gas hydrate(GH) is an unconventional resource estimated at 1000-120,000 trillion *** on GH is ongoing to determine its geological and flow characteristics for commercial production. After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin, the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD). Because the analysis is costly and dependent on experts, a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data. However, the model's performance was limited because of improper preprocessing of the intensity profile. Because preprocessing was applied to each feature, the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition. In this study, the profile was preprocessed for each sample using minmax scaling because relative intensity is critical for mineral analysis. For 49 test data among the 488data, the convolutional neural network(CNN) model improved the average absolute error and coefficient of determination by 41% and 46%, respectively, than those of CNN model with feature-based preprocessing. This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data. The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau. In addition, the overall procedure can be applied to any XRD data of sediments worldwide.

关键词： Sample-based preprocessing X-ray diffraction (XRD) Machine learning Mineral composition Gas hydrate (GH) Ulleung basin

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Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 39-45页

作者： Haolan Ren Fei Zheng Tingwei Cao Qiang Wang international center for climate and environment Science(ICCES) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina University of Chinese Academy of sciences BeijingChina College of Oceanography Hohai UniversityNanjingChina CAS Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of Sciences and Pilot National Laboratory for Marine Science and Technology(Qingdao)QingdaoChina

Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate *** studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC *** this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its *** a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s *** traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of *** results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter ***,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.

关键词： Recovery of AMOC 4×CO_(2) forcing Key parameter Parameter estimation Data assimilation Machine learning

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Interpreting hourly mass concentrations of PM_(2.5)chemical components with an optimal deep-learning model

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Journal of environmental sciences 2025年第5期151卷 125-139页

作者： Hongyi Li Ting Yang Yiming Du Yining Tan Zifa Wang State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China center for Excellence in Regional Atmospheric environment Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China University of Chinese Academy of sciences Beijing 100049China Shenyang environmental Monitoring center Shenyang 110167China

PM_(2.5)constitutes a complex and diversemixture that significantly impacts the environment,human health,and climate ***,existing observation and numerical simulation techniques have limitations,such as a lack of data,high acquisition costs,andmultiple *** limitations hinder the acquisition of comprehensive information on PM_(2.5)chemical composition and effectively implement refined air pollution protection and control *** this study,we developed an optimal deep learning model to acquire hourly mass concentrations of key PM_(2.5)chemical components without complex chemical *** model was trained using a randomly partitioned multivariate dataset arranged in chronological order,including atmospheric state indicators,which previous studies did not *** results showed that the correlation coefficients of key chemical components were no less than 0.96,and the root mean square errors ranged from 0.20 to 2.11μg/m^(3)for the entire process(training and testing combined).The model accurately captured the temporal characteristics of key chemical components,outperforming typical machine-learning models,previous studies,and global reanalysis datasets(such asModern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)and Copernicus Atmosphere Monitoring Service ReAnalysis(CAMSRA)).We also quantified the feature importance using the random forest model,which showed that PM_(2.5),PM_(1),visibility,and temperature were the most influential variables for key chemical *** conclusion,this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data,improved air pollution monitoring and source *** approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

关键词： Pm2.5 chemical composition Hourly mass concentration Deep learning Bayesian optimization Feature importance

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Significant changes in the physicochemical properties of BC-containing particles during the cold season in Beijing

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Journal of environmental sciences 2025年第5期151卷 667-676页

作者： Shuya Hu Linghan Zeng Gang Zhao Shiyi Chen Chunsheng Zhao Weilun Zhao Min Hu State Key Joint Laboratory of environmental Simulation and Pollution Control International Joint Research Center for Atmospheric Research(IJRC)College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Department of Atmospheric and Oceanic sciences School of PhysicsPeking UniversityBeijing 100871China Collaborative Innovation center of Atmospheric environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

The ambient air quality has improved significantly under strict emission controls in Beijing,China over the last *** carbon(BC),as a short-lived climate forcer in ambient aerosols,profoundly impacts the air quality and *** studies have demonstrated a decline in the mass concentration of *** this study,we characterized the chemical compositions and size distributions of BC-containing particles during the cold season of 2022 in Beijing using state-of-the-art instruments capable of exclusively measuring BCcontaining *** optical properties of BC-containing particles were further calculated based on the Mie ***,we compared the properties of BC-containing particles in 2022 with the results of previous *** results showed that the diameters of BC cores became larger while the coating thickness of BC-containing particles became thinner in *** the coating materials,the mass fraction of nitrate increased obviously,and even replaced organic matter as the dominant component during the peak of the pollution *** in chemical compositions and size distributions resulted in lower mass absorption cross-sections(MAC)of BC-containing particles from 10.5±1.1 m^(2)/g in 2016 to 7.3±0.8 m^(2)/g in 2022,reduced by 30.5%.Our results demonstrate the synergistic benefits of air pollution control in improving air quality and mitigating climate ***,the MAC of BC adopted in climate models should vary with the changing air pollution *** study emphasizes that it is imperative to conduct long-term observations of BC-containing particles to better estimate BC’s climate effects.

关键词： Black carbon Chemical composition Size distribution Light absorption ability

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How Do Deep Learning Forecasting Models Perform for Surface Variables in the South China Sea Compared to Operational Oceanography Forecasting Systems?

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Advances in Atmospheric sciences 2025年第1期42卷 178-189页

作者： Ziqing ZU Jiangjiang XIA Xueming ZHU Marie DREVILLON Huier MO Xiao LOU Qian ZHOU Yunfei ZHANG Qing YANG Key Laboratory of Marine Hazards Forecasting National Marine Environmental Forecasting CenterMinistry of Natural ResourcesBeijing 100081China Key Laboratory of Regional climate-environment for Temperate East Asia(RCE-TEA) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China Mercator Ocean international Ramonville Saint Agne 31400France

It is fundamental and useful to investigate how deep learning forecasting models(DLMs)perform compared to operational oceanography forecast systems(OFSs).However,few studies have intercompared their performances using an identical *** this study,three physically reasonable DLMs are implemented for the forecasting of the sea surface temperature(SST),sea level anomaly(SLA),and sea surface velocity in the South China *** DLMs are validated against both the testing dataset and the“OceanPredict”Class 4 *** show that the DLMs'RMSEs against the latter increase by 44%,245%,302%,and 109%for SST,SLA,current speed,and direction,respectively,compared to those against the ***,different references have significant influences on the validation,and it is necessary to use an identical and independent reference to intercompare the DLMs and *** the Class 4 dataset,the DLMs present significantly better performance for SLA than the OFSs,and slightly better performances for other *** error patterns of the DLMs and OFSs show a high degree of similarity,which is reasonable from the viewpoint of predictability,facilitating further applications of the *** extreme events,the DLMs and OFSs both present large but similar forecast errors for SLA and current speed,while the DLMs are likely to give larger errors for SST and current *** study provides an evaluation of the forecast skills of commonly used DLMs and provides an example to objectively intercompare different DLMs.

关键词： forecast error deep learning forecasting model operational oceanography forecasting system validation intercomparison

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On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles

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Journal of environmental sciences 2025年第2期148卷 46-56页

作者： Zhengyang Fang Shuwei Dong Chengpeng Huang Shiguo Jia Fu Wang Haoming Liu He Meng Lan Luo Yizhu Chen Huanhuan Zhang Rui Li Yujiao Zhu Mingjin Tang State Key Laboratory of Organic Geochemistry Guangdong Key Laboratory of Environmental Protection and Resources UtilizationGuangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou 510640China College of Earth and Planetary sciences University of Chinese Academy of SciencesBeijing 101408China Longhua center for Disease Control and Prevention of Shenzhen Shenzhen 518109China School of Atmospheric sciences and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Sun Yat-sen UniversityZhuhai 519082China Institute for environmental and climate Research Jinan UniversityGuangzhou 510632China Qingdao Eco-environment Monitoring center of Shandong Province Qingdao 266003China environment Research Institute Shandong UniversityQingdao 266237China

Thermodynamic modeling is still themostwidely usedmethod to characterize aerosol acidity,a critical physicochemical property of atmospheric ***,it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamicmodels are employed to estimate the acidity of coarse *** this work,field measurements were conducted at a coastal city in northern China across three seasons,and covered wide ranges of temperature,relative humidity and NH_(3) *** examined the performance of different modes of ISORROPIA-II(a widely used aerosol thermodynamic model)in estimating aerosol acidity of coarse and fine *** M0 mode,which incorporates gas-phase data and runs the model in the forward mode,provided reasonable estimation of aerosol acidity for coarse and fine *** to M0,the M1 mode,which runs the model in the forward mode but does not include gas-phase data,may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles;M2,which runs the model in the reverse mode,results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity ***,M1 significantly underestimates liquid water contents for both fine and coarse particles,while M2 provides reliable estimation of liquid water *** summary,our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity,and thus may help improve our understanding of acidity of coarse particles.

关键词： Aerosol acidity Coarse particles Thermodynamic model Aerosol liquid water ISORROPIA-II Gas-aerosol partitioning

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Effect of Parameter Variation in the BMJ Scheme on the Simulation of MJO Propagation and Structure

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Advances in Atmospheric sciences 2025年第2期42卷 397-409页

作者： Xiaoyu ZHU Zhong ZHONG Yimin ZHU Yunying LI Yijia HU Yao HA College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Key Laboratory for Virtual Geographic environment School of Geography ScienceNanjing Normal UniversityNanjing 210023China Jiangsu Collaborative Innovation center for climate Change School of Atmospheric SciencesNanjing UniversityNanjing 210023China

In this study,the Betts-Miller-Janjic(BMJ)convective adjustment scheme in the Weather Research and Forecasting(WRF)model version 4.0 was used to investigate the effect of itsα-parameter,which influences the first-guess potential temperature reference profile on the Madden-Julian oscillation(MJO)propagation and *** study diagnosed the MJO active phase composites of the MJO-filtered outgoing longwave radiation(OLR)during the December-to-February(DJF)period of 2006-2016 over the Indian Ocean(IO),Maritime Continent(MC),and western Pacific(WP).The results show that the MJO-filtered OLR intensity,propagation pattern,and MJO classification(standing,jumping,and propagating clusters)are sensitive to theα-value,but the phase speeds of propagating MJOs are ***,with an increasingα-value,the simulated MJO-filtered OLR intensity increases,and the simulated propagation pattern is *** also show that the intensity and propagation pattern of an eastward-propagating MJO are associated with MJO circulation structures and thermodynamic ***αincreases,the front Walker cell and the low-level easterly anomaly are enhanced,which premoistens the lower troposphere and triggers more active shallow and congestus *** enhanced shallow and congestus convection preconditions the lower to middle troposphere,accelerating the transition from congestus to deep convection,thereby facilitating eastward propagation of the ***,the simulated MJO tends to transfer from standing to eastward propagating asα*** summary,increasing theα-value is a possible way to improve the simulation of the structure and propagation of the MJO.

关键词： MJO WRF model BMJ scheme low-level easterly anomaly

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Enhancing weather predictions: Initial condition sensitivity and error dynamics in data assimilation

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Science China Earth sciences 2025年第2期68卷 649-652页

作者： Weiwei FU Department of Atmospheric and Oceanic sciences Fudan UniversityShanghai 200438China Key Laboratory of Polar Atmosphere-ocean-ice System for Weather and climate Ministry of EducationFudan UniversityShanghai 200438China IRDR international center of Excellence on Risk Interconnectivity and Governance on Weather Fudan UniversityShanghai 200438China

Data Assimilation(DA) plays a crucial role in modern numerical forecasting(Kalnay, 2002). It has been demonstrated that incorporating real-time observational data into forecast models significantly enhances the accuracy of both shortterm weather forecasts(Bauer et al., 2015;Bannister, 2017)and long-term climate predictions(Karspeck et al., 2013;Mochizuki et al., 2016).

关键词： weather assimilation forecasting

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