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Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

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Acta Oceanologica Sinica 2024年第7期43卷 26-39页

作者： Feng Nan Zhuolin Li Jie Yu Suixiang Shi Xinrong Wu Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China Key Laboratory of Digital Ocean National Marine Data and Information ServiceTianjin 300171China

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related ***,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean *** graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among *** this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior *** and spatial dependencies in the time series were then captured using temporal and graph *** also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid *** this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea *** compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

关键词： dynamic associations three-dimensional ocean temperature prediction graph neural network time series gridded data

Sea-level change in coastal areas of China:Status in 2021

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Advances in Climate Change Research 2024年第3期15卷 515-524页

作者： Wen-Shan LI Hui WANG Wen-Xi XIANG Ai-Mei WANG Wei-Qing XU Yu-Xi JIANG Xin-Hui WU Meng-Yuan QUAN national marine data and information service Tianjin 300171China Ocean University of Chin1 Qingdao 266100China University of Michigan‒Shanghai Jiao Tong University Joint Institute Shanghai 200030China

The sea level in coastal areas of China reached the second highest in 2021,just after that recorded in *** force and dynamic analyses based on tide gauges,satellite observations,reanalysis data and regional numerical outputs were conducted to understand these abnormally high sea levels and determine their possible *** show that the coastal sea level of China had increased at an annual rate of 3.4±0.3 mm during 1980-2021,with an acceleration of 0.06±0.02 mm per *** superposition of significant oscillations of quasi-2,3-7,quasi-9,quasi-11,quasi-19 and 20-30 years contributed to the anomalously high sea *** negative-phased El Nino/Southern Oscillation was correlated with the anomalously high sea level and the north-south anti-phase pattern of the coastal sea level in ***,phase lags of 1-4 months occurred with the sea-level *** a decadal timescale,the Pacific Decadal Oscillation(PDO)was negatively correlated with the anomalous mean sea level(MSL),and the negative-phased PDO contributed to the anomalous sea-level change in ***,the monthly MSL peaked in April and July,and the contribution of wind stress to the anomalously high sea level was 38.5%in the south of the Taiwan Strait in April and 30%along the coast of China in *** results were consistent with the tide gauge and satellite *** agreement was also observed between the coastal sea-level fingerprint and the air and sea surface temperatures.

关键词： Sealevel La Nina PDO Wind stress

A Fine-grained Building Height Control Method for Heritage Areas from the Perspective of Visual Integrity

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Journal of Geodesy and Geoinformation Science 2024年第2期7卷 66-79页

作者： WANG Xi HOU Miaole CHEN Fan national marine data and information service Tianjin 300171China The International Joint Laboratory of Safety and Energy Conservation for Ancient Buildings Beijing University of Civil Engineering and ArchitectureBeijing 102616China

This study proposes a refined methodology for controlling building heights in heritage *** order to protect the visual integrity of the heritage area,buildings should not obstruct the view from important site points and viewpoints to the *** calculating the building height thresholds that buildings should not obscure the view from each viewpoint,the results of which are weighted and superimposed,and the values are extracted to each building unit as a refined building height control *** study takes the Zhoukoudian area as a case study,applies the refined building height control criterion to the Zhoukoudian Site,and relies on this refined criterion to assess the visual integrity of the Zhoukoudian area,so as to realize the scientific planning and monitoring of the Zhoukoudian *** refined building height control method can be applied to building height planning and visual landscape protection in large heritage areas.

关键词： building height control visual integrity assessment observation points

Application of the finite analytic numerical method to a flowdependent variational data assimilation

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Acta Oceanologica Sinica 2024年第3期43卷 30-39页

作者： Yan Hu Wei Li Xuefeng Zhang Guimei Liu Liang Zhang Tianjin Key Laboratory for marine Environmental Research and service School of Marine Science and TechnologyTianjin UniversityTianjin 300072China School of marine Science and Technology Tianjin UniversityTianjin 300072China national marine Environmental Forecasting Center Ministry of Natural ResourcesBeijing 100081China

An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix,which can be achieved by solving the advection-diffusion *** of the directionality of the advection term,the discrete method needs to be chosen very *** finite analytic method is an alternative scheme to solve the advection-diffusion *** a combination of analytical and numerical methods,it not only has high calculation accuracy but also holds the characteristic of the auto *** demonstrate its ability,the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic *** more widely used upwind difference method is used as a control *** result indicates that the finite analytic method has higher accuracy than the upwind difference *** the two-dimensional case,the finite analytic method still has a better *** the three-dimensional variational assimilation experiment,the finite analytic method can effectively improve analysis field accuracy,and its effect is significantly better than the upwind difference and the central difference ***,it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.

关键词： finite analytic method advection-diffusion equation data assimilation flow-dependent

A typhoon-induced storm surge numerical model with GPU acceleration based on an unstructured spherical centroidal Voronoi tessellation grid

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Acta Oceanologica Sinica 2024年第3期43卷 40-47页

作者： Yuanyong Gao Fujiang Yu Cifu Fu Jianxi Dong Qiuxing Liu national marine Environmental Forecasting Center Beijing 100081China Key Laboratory of marine Hazards Forecasting Ministry of Natural ResourcesBeijing 100081China

Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal *** and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related *** surge numerical models are important for storm surge *** further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)*** model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C *** SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition *** addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble *** only takes 37 s to simulate a day in the coastal waters of *** newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of *** hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm *** simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.

关键词： typhoon-induced storm surge numerical model GPU acceleration unstructured grid spherical centroidal Voronoi tessellation(SCVT)

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

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Acta Oceanologica Sinica 2024年第5期43卷 78-90页

作者： Pengyi Chen Zhongbiao Chen Runxia Sun Yijun He School of marine Sciences Nanjing University of Information Science&TechnologyNanjing 210044China East Sea information Center of State Oceanic Administration Shanghai 200136China

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice *** obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning ***,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice ***,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea *** proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort *** determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are ***,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

关键词： 2-D Cauchy continuous wavelet transform(CWT) Adaboost Regression sea ice sea ice surface roughness

Summer Atmospheric Water Cycle under the Transition Influence of the Westerly and Summer Monsoon over the Yarlung Zangbo River Basin in the Southern Tibetan Plateau

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Advances in Atmospheric Sciences 2024年第5期41卷 830-846页

作者： Qianhui MA Chunyan ZHANG Donghai WANG Zihao PANG School of Atmospheric Sciences Sun Yat-sen UniversityGuangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesKey Laboratory of Tropical Atmosphere-Ocean SystemMinistry of EducationZhuhai 519082China Southern marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519082China Guangdong Meteorological data Center Guangdong Meteorological ServiceGuangzhou 510080China national Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research InstituteMacao University of Science and TechnologyMacao SAR 999078China national Meteorological information Center China Meteorological AdministrationBeijing 100081China

This study compares the summer atmospheric water cycle,including moisture sources and consumption,in the upstream,midstream,and downstream regions of the Yarlung Zangbo River Basin in the southern Tibetan *** evolutions of moisture properties under the influence of the westerly and summer southerly monsoon are examined using 5-yr multi-source measurements and ERA5 reanalysis *** that moisture consumption in this study is associated with clouds,precipitation,and diabatic *** to the midstream and downstream regions,the upstream region has less moisture,clouds,and precipitation,where the moisture is brought by the *** early August,the vertical wet advection over this region becomes enhanced and generates more high clouds and *** midstream region has moisture carried by the westerly in June and by the southerly monsoon from July to *** higher vertical wet advection maximum here forms more high clouds,with a precipitation peak in early *** downstream region is mainly affected by the southerly-driven wet *** rich moisture and strong vertical wet advection here produce the most clouds and precipitation among the three regions,with a precipitation peak in late *** height of the maximum moisture condensation is different between the midstream region(325 hPa)and the other two regions(375 hPa),due to the higher upward motion maximum in the midstream *** diabatic heating structures show that stratiform clouds dominate the upstream region,stratiform clouds and deep convection co-exist in the midstream region,and deep convection systems characterize the downstream region.

关键词： Yarlung Zangbo River Basin atmospheric water cycle constrained variational analysis moisture source and consumption

New Record Ocean Temperatures and Related Climate Indicators in 2023

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Advances in Atmospheric Sciences 2024年第6期41卷 1068-1082页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH Tim BOYER Michael EMANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Viktor GOURETSKI Gengxin CHEN Alexey MISHONOV Jim REAGAN Karina VON SCHUCKMANN Yuying PAN Zhetao TAN Yujing ZHU Wangxu WEI Guancheng LI Qiuping REN Lijuan CAO Yayang LU International Center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF national Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 1010New Zealand national Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China national marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China Oceanographic data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China national marine data and information service Tianjin 300171China Italian national Agency for New Technologies Energy and Sustainable Economic DevelopmentS.Teresa Research CenterLerici 19032Italy Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD20740USA Mercator Ocean International Toulouse 31400France Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentPRCGuangzhou 510611China national Meteorological information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big data for Sustainable Development Goals Beijing 100094China

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human *** 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record *** 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the *** with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of *** density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

关键词： ocean heat content salinity stratification global warming climate

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction