The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes...The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts,resulting in significant progress.Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts,reducing the forecasting skills.To this end,we propose a deep residual-coupled model prediction(Res-CMP)model,which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction.The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model(CESM)for effective prediction of the Niño 3.4 index.We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data.After determining the optimal configuration,which included selecting a suitable transfer learning rate during training,along with input variables and CESM forecast lengths,Res-CMP demonstrated a high correlation ability for 19-month lead time predictions(correlation coefficients exceeding 0.5).The Res-CMP model also alleviated the spring predictability barrier(SPB).When validated against actual ENSO events,Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Niña events(1998/99 and 2020/21)and El Niño events(2009/10 and 2015/16).Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.展开更多
Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this st...Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this study,a Localized Error Subspace Transform Kalman Filter is employed in a coupled climate system model(the Flexible Global Ocean–Atmosphere–Land System Model,version f3-L(FGOALS-f3-L))to assimilate sea-ice concentration(SIC)and sea-ice thickness(SIT)data for melting-season ice predictions.The scheme is applied through the following steps:(1)initialization for generating initial ensembles;(2)analysis for assimilating observed data;(3)adoption for dividing ice states into five thickness categories;(4)forecast for evolving the model;(5)resampling for updating model uncertainties.Several experiments were conducted to examine its results and impacts.Compared with the control experiment,the continuous assimilation experiments(CTNs)indicate assimilations improve model SICs and SITs persistently and generate realistic initials.Assimilating SIC+SIT data better corrects overestimated model SITs spatially than when only assimilating SIC data.The continuous assimilation restart experiments indicate the initials from the CTNs correct the overestimated marginal SICs and overall SITs remarkably well,as well as the cold biases in the oceanic and atmospheric models.The initials with SIC+SIT assimilated show more reasonable spatial improvements.Nevertheless,the SICs in the central Arctic undergo abnormal summer reductions,which is probably because overestimated SITs are reduced in the initials but the strong seasonal cycle(summer melting)biases are unchanged.Therefore,since systematic biases are complicated in a coupled system,for FGOALS-f3-L to make better ice predictions,oceanic and atmospheric assimilations are expected required.展开更多
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for...For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.展开更多
Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this ana...Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.展开更多
The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luan...The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.展开更多
Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of indivi...Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.展开更多
The Multi-source spatial data distribution is based on WebGIS, and it is an important part of multi-source geographic information management system. a new multi-source spatial data distribution model is proposed on th...The Multi-source spatial data distribution is based on WebGIS, and it is an important part of multi-source geographic information management system. a new multi-source spatial data distribution model is proposed on the basis of multisource data storage model and by combining existing map distribution technology, The author developed a multi-source spatial data distribution system which based on MapGIS K9 by using this model and taking full advantage of interfacecode separating thinking and high efficiency characteristic of .net, so high-speed distribution of multi-source spatial data realized.展开更多
A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the ...A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.展开更多
Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses...Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.展开更多
The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation...The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.展开更多
In marine seismic exploration,ocean bottom cable technology can record multicomponent seismic data for multiparameter inversion and imaging.This study proposes an elastic multiparameter lease-squares reverse time migr...In marine seismic exploration,ocean bottom cable technology can record multicomponent seismic data for multiparameter inversion and imaging.This study proposes an elastic multiparameter lease-squares reverse time migration based on the ocean bottom cable technology.Herein,the wavefield continuation operators are mixed equations:the acoustic wave equations are used to calculate seismic wave propagation in the seawater medium,whereas in the solid media below the seabed,the wavefields are obtained by P-and S-wave separated vector elastic wave equations.At the seabed interface,acoustic–elastic coupling control equations are used to combine the two types of equations.P-and S-wave separated elastic migration operators,demigration operators,and gradient equations are derived to realize the elastic least-squares reverse time migration based on the P-and S-wave mode separation.The model tests verify that the proposed method can obtain high-quality images in both the P-and S-velocity components.In comparison with the traditional elastic least-squares reverse time migration method,the proposed method can readily suppress imaging crosstalk noise from multiparameter coupling.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
The 3D digitalization and documentation of ancient Chinese architecture is challenging because of architectural complexity and structural delicacy.To generate complete and detailed models of this architecture,it is be...The 3D digitalization and documentation of ancient Chinese architecture is challenging because of architectural complexity and structural delicacy.To generate complete and detailed models of this architecture,it is better to acquire,process,and fuse multi-source data instead of single-source data.In this paper,we describe our work on 3D digital preservation of ancient Chinese architecture based on multi source data.We first briefly introduce two surveyed ancient Chinese temples,Foguang Temple and Nanchan Temple.Then,we report the data acquisition equipment we used and the multi-source data we acquired.Finally,we provide an overview of several applications we conducted based on the acquired data,including ground and aerial image fusion,image and LiDAR(light detection and ranging)data fusion,and architectural scene surface reconstruction and semantic modeling.We believe that it is necessary to involve multi-source data for the 3D digital preservation of ancient Chinese architecture,and that the work in this paper will serve as a heuristic guideline for the related research communities.展开更多
The datasets for the tier-1 Scenario Model Intercomparison Project(ScenarioMIP)experiments from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System model,finite-volume version 3(CAS FGOALS...The datasets for the tier-1 Scenario Model Intercomparison Project(ScenarioMIP)experiments from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System model,finite-volume version 3(CAS FGOALS-f3-L)are described in this study.ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Considering future CO2,CH4,N2O and other gases’concentrations,as well as land use,the design of ScenarioMIP involves eight pathways,including two tiers(tier-1 and tier-2)of priority.Tier-1 includes four combined Shared Socioeconomic Pathways(SSPs)with radiative forcing,i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5,in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6,4.5,7.0 and 8.5 W m−2,respectively.This study provides an introduction to the ScenarioMIP datasets of this model,such as their storage location,sizes,variables,etc.Preliminary analysis indicates that surface air temperatures will increase by about 1.89℃,3.07℃,4.06℃ and 5.17℃ by around 2100 under these four scenarios,respectively.Meanwhile,some other key climate variables,such as sea-ice extension,precipitation,heat content,and sea level rise,also show significant long-term trends associated with the radiative forcing increases.These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.展开更多
Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to u...Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.展开更多
Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to pred...Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.展开更多
A hybrid coupled model （HCM） is constructed for El Nifio-Southern Oscillation （ENSO）-related modeling studies over almost the entire Pacific basin.An ocean general circulation model is coupled to a statistical atm...A hybrid coupled model （HCM） is constructed for El Nifio-Southern Oscillation （ENSO）-related modeling studies over almost the entire Pacific basin.An ocean general circulation model is coupled to a statistical atmospheric model for interannual wind stress anomalies to represent their dominant coupling with sea surface temperatures.In addition,various relevant forcing and feedback processes exist in the region and can affect ENSO in a significant way; their effects are simply represented using historical data and are incorporated into the HCM,including stochastic forcing of atmospheric winds,and feedbacks associated with freshwater flux,ocean biology-induced heating （OBH）,and tropical instability waves （TIWs）.In addition to its computational efficiency,the advantages of making use of such an HCM enable these related forcing and feedback processes to be represented individually or collectively,allowing their modulating effects on ENSO to be examined in a clean and clear way.In this paper,examples are given to illustrate the ability of the HCM to depict the mean ocean state,the circulation pathways connecting the subtropics and tropics in the western Pacific,and interannual variability associated with ENSO.As satellite data are taken to parameterize processes that are not explicitly represented in the HCM,this work also demonstrates an innovative method of using remotely sensed data for climate modeling.Further model applications related with ENSO modulations by extratropical influences and by various forcings and feedbacks will be presented in Part Ⅱ of this study.展开更多
Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for ...Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.展开更多
The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model,version f3-L(CAS FGOALS-f3-L),which is contributing to phase 6 of the Coupled Model Inter...The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model,version f3-L(CAS FGOALS-f3-L),which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6),are described in this study.The details of the CAS FGOALS-f3-L model,experiment settings and output datasets are briefly introduced.The datasets include monthly and daily outputs from the atmospheric,oceanic,land and sea-ice component models of CAS FGOALS-f3-L,and all these data have been published online in the Earth System Grid Federation(ESGF,https://esgf-node.llnl.gov/projects/cmip6/).The three ensembles are initialized from the 600th,650th and 700th model year of the preindustrial experiment(piControl)and forced by the same historical forcing provided by CMIP6 from 1850 to 2014.The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets.It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate,including the climatology of air surface temperature and precipitation,the long-term changes in global mean surface air temperature,ocean heat content and sea surface steric height,and the horizontal and vertical distribution of temperature in the ocean and atmosphere.Meanwhile,like other state-of-the-art coupled GCMs,there are still some obvious biases in the historical simulations,which are also illustrated.This paper can help users to better understand the advantages and biases of the model and the datasets。展开更多
The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributio...The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributions of the critical chloride concentration Cc, the chloride diffusion coefficient D, and the surface chloride concentration Cs were determined based on the collected natural exposure data. And the estimation of probability of corrosion initiation considering the coupling effects of influence factors is presented. It is found that the relative humidity and curing time are the most effective factors affecting the probability of corrosion initiation before and after 10 years of exposure time. At the same exposure time, the influence of load-induced crack and stress state on the probability of corrosion initiation is obvious, in which the effect of crack is the most one展开更多
基金The National Key Research and Development Program of China under contract Nos 2024YFF0808900,2023YFF0805300,and 2020YFA0608804the Civilian Space Programme of China under contract No.D040305.
文摘The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts,resulting in significant progress.Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts,reducing the forecasting skills.To this end,we propose a deep residual-coupled model prediction(Res-CMP)model,which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction.The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model(CESM)for effective prediction of the Niño 3.4 index.We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data.After determining the optimal configuration,which included selecting a suitable transfer learning rate during training,along with input variables and CESM forecast lengths,Res-CMP demonstrated a high correlation ability for 19-month lead time predictions(correlation coefficients exceeding 0.5).The Res-CMP model also alleviated the spring predictability barrier(SPB).When validated against actual ENSO events,Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Niña events(1998/99 and 2020/21)and El Niño events(2009/10 and 2015/16).Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.
基金jointly funded by the National Natural Science Foundation of China(NSFC)[grant number 42130608]the China Postdoctoral Science Foundation[grant number 2024M753169]。
文摘Arctic sea ice is an important component of the global climate system and has experienced rapid changes during in the past few decades,the prediction of which is a significant application for climate models.In this study,a Localized Error Subspace Transform Kalman Filter is employed in a coupled climate system model(the Flexible Global Ocean–Atmosphere–Land System Model,version f3-L(FGOALS-f3-L))to assimilate sea-ice concentration(SIC)and sea-ice thickness(SIT)data for melting-season ice predictions.The scheme is applied through the following steps:(1)initialization for generating initial ensembles;(2)analysis for assimilating observed data;(3)adoption for dividing ice states into five thickness categories;(4)forecast for evolving the model;(5)resampling for updating model uncertainties.Several experiments were conducted to examine its results and impacts.Compared with the control experiment,the continuous assimilation experiments(CTNs)indicate assimilations improve model SICs and SITs persistently and generate realistic initials.Assimilating SIC+SIT data better corrects overestimated model SITs spatially than when only assimilating SIC data.The continuous assimilation restart experiments indicate the initials from the CTNs correct the overestimated marginal SICs and overall SITs remarkably well,as well as the cold biases in the oceanic and atmospheric models.The initials with SIC+SIT assimilated show more reasonable spatial improvements.Nevertheless,the SICs in the central Arctic undergo abnormal summer reductions,which is probably because overestimated SITs are reduced in the initials but the strong seasonal cycle(summer melting)biases are unchanged.Therefore,since systematic biases are complicated in a coupled system,for FGOALS-f3-L to make better ice predictions,oceanic and atmospheric assimilations are expected required.
基金supported by the National Natural Science Foundation of China under Grant 51722406,52074340,and 51874335the Shandong Provincial Natural Science Foundation under Grant JQ201808+5 种基金The Fundamental Research Funds for the Central Universities under Grant 18CX02097Athe Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002the National Research Council of Science and Technology Major Project of China under Grant 2016ZX05025001-006111 Project under Grant B08028Sinopec Science and Technology Project under Grant P20050-1
文摘For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.
基金Supported by the National Natural Science Foundation of China(No.51379006 and No.51009106)the Program for New Century Excellent Talents in University of Ministry of Education of China(No.NCET-12-0404)the National Basic Research Program of China("973"Program,No.2013CB035903)
文摘Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.
文摘The development of 3D geological models involves the integration of large amounts of geological data,as well as additional accessible proprietary lithological, structural,geochemical,geophysical,and borehole data.Luanchuan,the case study area,southwestern Henan Province,is an important molybdenum-tungsten -lead-zinc polymetallic belt in China.
基金Under the auspices of Shenzhen Science and Technology Program(No.KQTD20180410161218820)Guangdong Basic and Applied Basic Research Foundation(No.2021A1515012600)。
文摘Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.
文摘The Multi-source spatial data distribution is based on WebGIS, and it is an important part of multi-source geographic information management system. a new multi-source spatial data distribution model is proposed on the basis of multisource data storage model and by combining existing map distribution technology, The author developed a multi-source spatial data distribution system which based on MapGIS K9 by using this model and taking full advantage of interfacecode separating thinking and high efficiency characteristic of .net, so high-speed distribution of multi-source spatial data realized.
基金supported by the National Natural Science Foundation of China(Grant Nos.41490644,41475101 and 41421005)the CAS Strategic Priority Project(the Western Pacific Ocean System+2 种基金Project Nos.XDA11010105,XDA11020306 and XDA11010301)the NSFC-Shandong Joint Fund for Marine Science Research Centers(Grant No.U1406401)the NSFC Innovative Group Grant(Project No.41421005)
文摘A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.
基金supported by the National Key Research&Development Program of China(Grant Nos.2017YFC1404100 and 2017YFC1404104)the National Natural Science Foundation of China(Grant Nos.41775100 and 41830964)。
文摘Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.
基金Under the auspices of Major State Basic Research Development Program of China (973 Program) (No. 2007CB714400)the Program of One Hundred Talents of the Chinese Academy of Sciences (No. 99T3005WA2)
文摘The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.
基金supported by National Natural Science Foundation of China(Nos.41904101,41774133)Natural Science Foundation of Shandong Province(ZR2019QD004)+1 种基金Fundamental Research Funds for the Central Universities(No.19CX02010A)the Open Funds of SINOPEC Key Laboratory of Geophysics(Nos.wtyjy-wx2019-01-03,wtyjywx2018-01-06)
文摘In marine seismic exploration,ocean bottom cable technology can record multicomponent seismic data for multiparameter inversion and imaging.This study proposes an elastic multiparameter lease-squares reverse time migration based on the ocean bottom cable technology.Herein,the wavefield continuation operators are mixed equations:the acoustic wave equations are used to calculate seismic wave propagation in the seawater medium,whereas in the solid media below the seabed,the wavefields are obtained by P-and S-wave separated vector elastic wave equations.At the seabed interface,acoustic–elastic coupling control equations are used to combine the two types of equations.P-and S-wave separated elastic migration operators,demigration operators,and gradient equations are derived to realize the elastic least-squares reverse time migration based on the P-and S-wave mode separation.The model tests verify that the proposed method can obtain high-quality images in both the P-and S-velocity components.In comparison with the traditional elastic least-squares reverse time migration method,the proposed method can readily suppress imaging crosstalk noise from multiparameter coupling.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
文摘The 3D digitalization and documentation of ancient Chinese architecture is challenging because of architectural complexity and structural delicacy.To generate complete and detailed models of this architecture,it is better to acquire,process,and fuse multi-source data instead of single-source data.In this paper,we describe our work on 3D digital preservation of ancient Chinese architecture based on multi source data.We first briefly introduce two surveyed ancient Chinese temples,Foguang Temple and Nanchan Temple.Then,we report the data acquisition equipment we used and the multi-source data we acquired.Finally,we provide an overview of several applications we conducted based on the acquired data,including ground and aerial image fusion,image and LiDAR(light detection and ranging)data fusion,and architectural scene surface reconstruction and semantic modeling.We believe that it is necessary to involve multi-source data for the 3D digital preservation of ancient Chinese architecture,and that the work in this paper will serve as a heuristic guideline for the related research communities.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA19060102 and XDB42000000)the National Natural Science Foundation of China(Grants Nos.41530426 and 91958201)。
文摘The datasets for the tier-1 Scenario Model Intercomparison Project(ScenarioMIP)experiments from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System model,finite-volume version 3(CAS FGOALS-f3-L)are described in this study.ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Considering future CO2,CH4,N2O and other gases’concentrations,as well as land use,the design of ScenarioMIP involves eight pathways,including two tiers(tier-1 and tier-2)of priority.Tier-1 includes four combined Shared Socioeconomic Pathways(SSPs)with radiative forcing,i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0 and SSP5-8.5,in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6,4.5,7.0 and 8.5 W m−2,respectively.This study provides an introduction to the ScenarioMIP datasets of this model,such as their storage location,sizes,variables,etc.Preliminary analysis indicates that surface air temperatures will increase by about 1.89℃,3.07℃,4.06℃ and 5.17℃ by around 2100 under these four scenarios,respectively.Meanwhile,some other key climate variables,such as sea-ice extension,precipitation,heat content,and sea level rise,also show significant long-term trends associated with the radiative forcing increases.These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.
文摘Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.
基金supported by the National Natural Science Foundation of China(41977215)。
文摘Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
基金supported by the CAS Strategic Priority Project (the Western Pacific Ocean System: Structure, Dynamics and Consequences, WPOS)a China 973 project (Grant No. 2012CB956000)+1 种基金the Institute of Oceanology, Chinese Academy of Sciences (IOCAS)the National Natural Science Foundation of China (No. 41206017)
文摘A hybrid coupled model （HCM） is constructed for El Nifio-Southern Oscillation （ENSO）-related modeling studies over almost the entire Pacific basin.An ocean general circulation model is coupled to a statistical atmospheric model for interannual wind stress anomalies to represent their dominant coupling with sea surface temperatures.In addition,various relevant forcing and feedback processes exist in the region and can affect ENSO in a significant way; their effects are simply represented using historical data and are incorporated into the HCM,including stochastic forcing of atmospheric winds,and feedbacks associated with freshwater flux,ocean biology-induced heating （OBH）,and tropical instability waves （TIWs）.In addition to its computational efficiency,the advantages of making use of such an HCM enable these related forcing and feedback processes to be represented individually or collectively,allowing their modulating effects on ENSO to be examined in a clean and clear way.In this paper,examples are given to illustrate the ability of the HCM to depict the mean ocean state,the circulation pathways connecting the subtropics and tropics in the western Pacific,and interannual variability associated with ENSO.As satellite data are taken to parameterize processes that are not explicitly represented in the HCM,this work also demonstrates an innovative method of using remotely sensed data for climate modeling.Further model applications related with ENSO modulations by extratropical influences and by various forcings and feedbacks will be presented in Part Ⅱ of this study.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41705082, 41475101, 41690122(41690120))a Chinese Academy of Sciences Strategic Priority Project-the Western Pacific Ocean System (Grant Nos. XDA11010105 and XDA11020306)+1 种基金the National Programme on Global Change and Air–Sea Interaction (Grant Nos. GASI-IPOVAI06 and GASI-IPOVAI-01-01)the China Postdoctoral Science Foundation, and a Qingdao Postdoctoral Application Research Project
文摘Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.
基金This study is jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant Nos.XDA19060102 and XDB42010400)the Natural Science Foundation of China(Grant Nos.41530426,91958201 and 41931183).
文摘The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model,version f3-L(CAS FGOALS-f3-L),which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6),are described in this study.The details of the CAS FGOALS-f3-L model,experiment settings and output datasets are briefly introduced.The datasets include monthly and daily outputs from the atmospheric,oceanic,land and sea-ice component models of CAS FGOALS-f3-L,and all these data have been published online in the Earth System Grid Federation(ESGF,https://esgf-node.llnl.gov/projects/cmip6/).The three ensembles are initialized from the 600th,650th and 700th model year of the preindustrial experiment(piControl)and forced by the same historical forcing provided by CMIP6 from 1850 to 2014.The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets.It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate,including the climatology of air surface temperature and precipitation,the long-term changes in global mean surface air temperature,ocean heat content and sea surface steric height,and the horizontal and vertical distribution of temperature in the ocean and atmosphere.Meanwhile,like other state-of-the-art coupled GCMs,there are still some obvious biases in the historical simulations,which are also illustrated.This paper can help users to better understand the advantages and biases of the model and the datasets。
基金Project(50925829) supported by the National Science Fund for Distinguished Young Scholars of ChinaProject(50908148) supported by the National Natural Science Foundation of ChinaProjects(2009-K4-23,2010-11-33) supported by the Research of Ministry of Housing and Urban Rural Development of China
文摘The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributions of the critical chloride concentration Cc, the chloride diffusion coefficient D, and the surface chloride concentration Cs were determined based on the collected natural exposure data. And the estimation of probability of corrosion initiation considering the coupling effects of influence factors is presented. It is found that the relative humidity and curing time are the most effective factors affecting the probability of corrosion initiation before and after 10 years of exposure time. At the same exposure time, the influence of load-induced crack and stress state on the probability of corrosion initiation is obvious, in which the effect of crack is the most one
