检索结果-南通市图书馆

The importance of data assimilation components for initial conditions and subsequent error growth

Science China Earth Sciences 2024年第1期67卷 105-116页

作者： Zhongrui WANG Haohao SUN Lili LEI Zhe-Min TAN Yi ZHANG Key Laboratory of Mesoscale Severe Weather Ministry of Educationand School of Atmospheric SciencesNanjing UniversityNanjing 210023China Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error covariances.In a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of interests.The forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 d.assimilation strategies that provide more accurate ICs can improve the predictability.Cycling assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static B.Instantaneous observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing systems.Moreover,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

关键词： data assimilation Atmospheric predictability Background error covariances Ensemble forecasts

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

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

引用

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 equation.Because of the directionality of the advection term,the discrete method needs to be chosen very carefully.The finite analytic method is an alternative scheme to solve the advection-diffusion equation.As a combination of analytical and numerical methods,it not only has high calculation accuracy but also holds the characteristic of the auto upwind.To demonstrate its ability,the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method.The more widely used upwind difference method is used as a control approach.The result indicates that the finite analytic method has higher accuracy than the upwind difference method.For the two-dimensional case,the finite analytic method still has a better performance.In 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 method.Moreover,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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

引用

Advances in Atmospheric Sciences 2024年第2期41卷 263-277页

作者： Rong KONG Ming XUE Edward R.MANSELL Chengsi LIU Alexandre O.FIERRO Center for Analysis and Prediction of Storms Norman Oklahoma 73072U.S.A University of Oklahoma Norman Oklahoma 73072U.S.A NOAA/National Severe Storms Laboratory NormanOklahoma 73072U.S A.4Zentralanstalt für Meteorologie und Geodynamik Department of Forecasting Models-ZAMGVienna 1190Austria

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

关键词： GOES-R lightning data assimilation EnKF EnVar

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application

引用

Science China Earth Sciences 2022年第10期65卷 1961-1971页

作者： Zengliang ZANG Yanfei LIANG Wei YOU Yi LI Xiaobin PAN Zhijin LI College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China No.32145 Unit of PLA Xinxiang 453000China Department of Atmospheric and Oceanic Sciences Fudan UniversityShanghai 200433China Joint Institute for Regional Earth System Science and Engineering University of California Los AngelesCalifornia 91109USA

With an increasing number of air quality monitoring stations installed around the Chinese mainland,high-resolution aerosol observations become available,allowing improvements in air pollution monitoring and aerosol forecasting.However,the multi scales(especially small-scale)information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method(3DVAR).This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps,two-scale-3DVAR(TS-3DVAR),to improve the effectiveness of assimilating high-resolution observations.In TS-3DVAR,the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples.The data assimilation(DA)analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field’s multi-scale characteristics,which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting.Particulate matter with an aerodynamic diameter of less than 2.5μm(PM_(2.5))and 10.0μm(PM_(10)) from the surface air quality monitoring stations from November 01 to November 30,2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy.The results showed that TS-3DVAR better constrained both large-scale and small-scale,especially the spatial wavelengths in a range of 54-216 km and those above 351 km.The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70%and 35.33%higher than those of 3DVAR.As a result,the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field,and thereby the forecasting capability for PM_(2.5).In the initial chemical field,the 30-

关键词： Multi-scale 3DVAR data assimilation Aerosol forecast WRF-Chem

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Research on data assimilation strategy of turbulent separated flow over airfoil

引用

Applied Mathematics and Mechanics(English Edition) 2022年第4期43卷 571-586页

作者： Ying ZHANG Lin DU Weiwei ZHANG Zichen DENG National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi'an 710072China Complex System Dynamics and Control Academic Exchange Center School of Mathematics and StatisticsNorthwestern Polytechnical UniversityXi'an 710119China Rotor Aerodynamics Key Laboratory China Aerodynamics Research and Development CenterMianyang 621000Sichuan ProvinceChina

In order to increase the accuracy of turbulence field reconstruction,this paper combines experimental observation and numerical simulation to develop and establish a data assimilation framework,and apply it to the study of S809 low-speed and high-angle airfoil flow.The method is based on the ensemble transform Kalman filter(ETKF)algorithm,which improves the disturbance strategy of the ensemble members and enhances the richness of the initial members by screening high flow field sensitivity constants,increasing the constant disturbance dimensions and designing a fine disturbance interval.The results show that the pressure distribution on the airfoil surface after assimilation is closer to the experimental value than that of the standard Spalart-Allmaras(S-A)model.The separated vortex estimated by filtering is fuller,and the eddy viscosity field information is more abundant,which is physically consistent with the observation information.Therefore,the data assimilation method based on the improved ensemble strategy can more accurately and effectively describe complex turbulence phenomena.

关键词： turbulence data assimilation ensemble disturbance strategy large angle of attack separation ensemble transform Kalman filter(ETKF)

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation

引用

Science China Earth Sciences 2023年第11期66卷 2546-2562页

作者： Jiaxin TIAN Hui LU Kun YANG Jun QIN Long ZHAO Jianhong ZHOU Yaozhi JIANG Xiaogang MA Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China National Tibetan Plateau data Center Key Laboratory of Tibetan Environmental Changes and Land Surfaces ProcessesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouth-west UniversityChongqing 400715China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China

Soil moisture plays a crucial role in drought monitoring,flood forecasting,and water resource management.data assimilation methods can integrate the strengths of land surface models(LSM)and remote sensing data to generate highprecision and spatio-temporally continuous soil moisture products.However,one of the challenges of the land data assimilation system(LDAS)is how to accurately estimate model and observation errors.To address this,we had previously proposed a dualcycle assimilation algorithm that can simultaneously estimate the model and observation errors,LSM parameters,and observation operator parameters.However,this algorithm requires a large ensemble size to guarantee stable parameter estimates,resulting in low efficiency and limiting its large-scale applications.To address this limitation,the authors employed the following approaches:(1)using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM,and(2)replacing the ensemble Kalman filter framework with the extended Kalman filter(EKF)framework to improve the efficiency of parameter optimization for the dual-cycle algorithm.The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h.To evaluate the dual-cycle LDAS at the regional-scale,it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau,and soil moisture estimates were validated using in situ observations from four different climatic areas.The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture,land surface temperature,and evapotranspiration than did the open loop with default parameters.Furthermore,the spatial distribution of soil parameters(sand content,clay content,and porosity)obtained from the LDAS was more reasonable than those of default values.The EKF-based dual-cycle algorithm developed in this study is

关键词： Soil moisture data assimilation Parameter optimization Bias correction Error estimation Automatic differentiation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

引用

Petroleum Science 2022年第2期19卷 707-719页

作者： Kai Zhang Hai-Qun Yu Xiao-Peng Ma Jin-Ding Zhang Jian Wang Chuan-Jin Yao Yong-Fei Yang Hai Sun Jun Yao Jian Wang School of Petroleum Engineering China University of Petroleum(East China)QingdaoShandong266580China School of Science China University of Petroleum(East China)QingdaoShandong266580China Shengli Oil Field Exploration and Development Research Institute DongyingShandong266071China

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.

关键词： Multi-source information Automatic history matching Deep learning data assimilation Generative model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A data assimilation-based forecast model of outer radiation belt electron fluxes

引用

Earth and Planetary Physics 2023年第6期7卷 620-630页

作者： Yuan Lei Xing Cao BinBin Ni Song Fu TaoRong Luo XiaoYu Wang Department of Space Physics School of Electronic InformationWuhan UniversityWuhan 430072China Chinese Academy of Sciences Center for Excellence in Comparative Planetology Hefei 230026China

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

关键词： Earth’s outer radiation belt data assimilation electron flux forecast model performance evaluation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Multi-point temperature measurements in packed beds using phosphor thermometry and ray tracing simulations

引用

Particuology 2024年第2期85卷 77-88页

作者： Guangtao Xuan Mirko Ebert Simson Julian Rodrigues Nicole Vorhauer-Huget Christian Lessig Benoit Fond Institute of Fluid Dynamics and Thermodynamics OVGU MagdeburgUniversitaetsplatz 2Magdeburg39106Saxony-AnhaltGermany Institute of Simulations and Graphics OVGU MagdeburgUniversitaetsplatz 2Magdeburg39106Saxony-AnhaltGermany Institute of Process Engineering OVGU MagdeburgUniversitaetsplatz 2Magdeburg39106Saxony-AnhaltGermany ONERA The French Aerospace LabDepartment of AerodynamicsAeroelasticity and Aeroacoustics(DAAA)Paris-Saclay University8rue des VertugadinsMeudon92190France

Packed bed reactors are commonly found in the process industry,for example in flame-assisted calci-nation for cement production.Understanding the heat transfer inside the bed is essential for process control,product quality and energy efficiency.Here we propose a technique to determine the internal temperature distribution of packed beds based on a combination of lifetime-based phosphor ther-mometry,ray tracing simulations,and assimilation of temperature data using finite element heat transfer simulations.To establish and validate the technique,we considered a reproducible regular packing of 6 mm diameter aluminum spheres,with one of the spheres in the top layer being electrically heated.If a sphere inside the packing is coated with thermographic phosphors and excitation light is directed to-wards the packing,luminescence from the coated sphere exits the packed bed after multiple reflection and the sphere's temperature can be determined.Isothermal measurements showed that the temper-ature obtained by phosphor thermometry is independent of the luminescent sphere location.When imaging the luminescence on a camera,the luminescence distribution in recorded image depended,however,on the position of the sphere.Therefore,in setups with multiple phosphor-coated spheres,their signals can be separated using a least squares fit.We demonstrate the approach using a setup with three luminescent spheres and validated the temperature readings against thermocouple measurements.To obtain the spatial signatures for individual sphere positions required for the least squares fit,ray tracing simulations were used.These provide an efficient alternative to single sphere measurements that are only practical for regular spherical packed beds.Multi-point measurements were used as input to a finite element heat transfer simulations to determine parameters such as particle-to-particle air gap distance.With these,the full temperature distribution inside the bed could be assimilated from the measured values.

关键词： Packed beds Phosphor thermometry Ray tracing Heat transfer simulation data assimilation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Prediction of landslide block movement based on Kalman filtering data assimilation method

引用

山地科学学报(英文) 2023年第9期20卷 2680-2691页

作者： LIU Yong XU Qing-jie LI Xing-rui YANG Ling-feng XU Hong Technology Innovation Center of Geohazards Automatic Monitoring Ministry of Natural Resources(Chongqing Institute of Geology and Mineral Resources)Chongqing 401120China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China

Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.

关键词： Landslide block Movement state 6-degree of freedom pose Kalman filtering data assimilation Baishuihe landslide

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：