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

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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

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

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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

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

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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

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

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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

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

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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

Research on data assimilation strategy of turbulent separated flow over airfoil

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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)

Contributions of Fuqing ZHANG to Predictability,data assimilation,and Dynamics of High Impact Weather:A Tribute

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Advances in Atmospheric Sciences 2022年第5期39卷 676-683,I0002-I0016页

作者： Zhiyong MENG Eugene E.CLOTHIAUX Department of Atmospheric and Oceanic Sciences School of PhysicsPeking UniversityBeijing 100871China Department of Meteorology and Atmospheric Science The Pennsylvania State UniversityUniversity ParkPennsylvania 16802United States

This article reviews Fuqing ZHANG’s contributions to mesoscale atmospheric science,from research to mentoring to academic service,over his 20-year career.His fundamental scientific contributions on predictability,data assimilation,and dynamics of high impact weather,especially gravity waves and tropical cyclones,are highlighted.His extremely generous efforts to efficiently transmit to the community new scientific knowledge and ideas through mentoring,interacting,workshop organizing,and reviewing are summarized.Special appreciation is given to his tremendous contributions to the development of mesoscale meteorology in China and the education of Chinese graduate students and young scientists.

关键词： predictability dynamics data assimilation high impact weather Fuqing ZHANG contributions tribute

A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

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Science China Earth Sciences 2020年第9期63卷 1366-1380页

作者： Daichun WANG Wei YOU Zengliang ZANG Xiaobin PAN Hongrang HE Yanfei LIANG Institute of Meteorology and Oceanography National University of Defense TechnologyNanjing 211101China No.94595 Unit of PLA Weifang 216500China No.32145 Unit of PLA Xinxiang 453000China

A three-dimensional variational(3DVAR)data assimilation(DA)system is presented here based on a size-resolved sectional aerosol model,the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC)within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem)model.The use of this approach means that both gaseous pollutants such as SO2,NO2,CO,and O3 as well as particulate matter(PM2.5,PM10)observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted,one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC)and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts.Results show that,in the first place,use of the DA system can provide a more accurate model initial field.The root-mean-square error of PM2.5,PM10,SO2,NO2,CO,and O3 mass concentrations in analysis field fell by 29.27μg m-3(53.5%),34.5μg m-3(50.9%),30.36μg m-3(64.2%),8.91μg m-3(39.5%),0.46 mg m-3(47.4%),and 15.11μg m-3(51.0%),respectively,compared to a background field without assimilation.At the same time,mean fraction error was reduced by 42.6%,53.1%,45.2%,43.1%,69.9%,and 48.8%,respectively,while the correlation coefficient increased by 0.51,0.55,0.48,0.38,0.47,0.65,respectively.Secondly,the results of this analysis reveal variable benefits from assimilation on different pollutants.DA significantly improves PM2.5,PM10,and CO forecasts leading to positive effects that last more than 48 h.The positive effects of DA on SO2 and O3 forecasts last up to 8 h but that remains relatively poor for NO2 forecasts.Thirdly,the influence of assimilation varies in different areas.It is possible that the positive effects of DA on PM2.5 and PM10 forecasts can last more than 48 h across most regions of China.Indeed,DA significantly improves SO2 forecasts within 48 h over north China,and much longer CO assimilation benefits(4

关键词： WRF-Chem Aerosol Gaseous pollutant 3DVAR data assimilation

Insights into the Microwave Instruments Onboard the Fengyun 3D Satellite:data Quality and assimilation in the Met Office NWP System

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Advances in Atmospheric Sciences 2021年第8期38卷 1379-1396页

作者： Fabien CARMINATI Nigel ATKINSON Brett CANDY Qifeng LU Met Office Exeter EX13PBUK National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China

This paper evaluates the microwave instruments onboard the latest Chinese polar-orbiting satellite, Fengyun 3D (FY- 3D). Comparing three months of observations from the Microwave Temperature Sounder 2 (MWTS-2), the Microwave Humidity Sounder 2 (MWHS-2), and the Microwave Radiation Imager (MWRI) to Met Office short-range forecasts, we characterize the instrumental biases, show how those biases have changed with respect to their predecessors onboard FY- 3C, and how they compare to the Advanced Technology Microwave Sounder (ATMS) onboard NOAA-20 and the Global Precipitation Measurement Microwave Imager (GMI). The MWTS-2 global bias is much reduced with respect to its predecessor and compares well to ATMS at equivalent channel frequencies, differing only by 0.36 ± 0.28 K (1σ) on average. A suboptimal averaging of raw digital counts is found to cause an increase in striping noise and an ascending- descending bias. MWHS-2 benefits from a new calibration method improving the 183-GHz humidity channels with respect to its predecessor and biases for these channels are within ± 1.9 K to ATMS. MWRI presents the largest improvements, with reduced global bias and standard deviation with respect to FY-3C;although, spurious, seemingly transient, brightness temperatures have been detected in the observations at 36.5 GHz (vertical polarization). The strong solar-dependent bias that affects the instrument on FY-3C has been reduced to less than 0.2 K on average for FY-3D MWRI. Experiments where radiances from these instruments were assimilated on top of a full global system demonstrated a neutral to positive impact on the forecasts, as well as on the fit to the background of independent instruments.

关键词： microwave remote sensing numerical weather prediction data assimilation