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A meteorologically adjusted ensemble kalman filter approach for inversing daily emissions:A case study in the Pearl River Delta,China

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Journal of Environmental Sciences 2022年第4期34卷 233-248页

作者： Guanglin Jia Zhijiong Huang Xiao Tang Jiamin Ou Menghua Lu Yuanqian Xu Zhuangmin Zhong Qing’e Sha Huangjian Wu Chuanzeng Zheng Tao Deng Duohong Chen Min He Junyu Zheng School of Environment and Energy South China University of TechnologyUniversity Town CampusGuangzhou 510006China Institute for Environmental and Climate Research Jinan UniversityGuangzhou 511486China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Faculty of Social and Behavioural Sciences Utrecht UniversityUtrechtCH 3584the Netherlands Guangzhou Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510640China State Environmental Protection Key Laboratory of Regional Air Quality Monitoring Guangdong Environmental Monitoring CenterGuangzhou 510308China Department of Environmental Science and Engineering College of Architecture and EnvironmentSichuan UniversityChengdu 610065China

The conventional ensemble kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accurate updates of high temporal-resolution emission inventories challenging.In this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission estimations.The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs.For demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,China.In the case study,60%of the total CO simulation biases were associated with sensitive meteorological inputs,which would lead to the overestimation of daily variations of posterior emissions.Using the new inversion method,daily variations of emissions shrank dramatically,with the percentage change decreased by 30%.Also,the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%,indicating that posterior CO emissions might be overestimated using the conventional EnKF.Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration,with a higher correlation coefficient(R,+37.0%)and lower normalized mean bias(NMB,-17.9%).Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants,the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

关键词： Emission inversion Daily emissions Meteorological adjustment ensemble kalman filter

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Application of ensemble H-infinity filter in aquifer characterization andcomparison to ensemble kalman filter

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Water Science and Engineering 2017年第1期10卷 25-35页

作者： Tong-chao Nan Ji-chun Wu State Key Laboratory of Pollution Control and Resources Reuse Nanjing UniversityNanjing 210093China Department of Hydrosciences School of Earth Sciences and EngineeringNanjing UniversityNanjing 210093China

Though the ensemble kalman filter （EnKF） has been successfully applied in many areas, it requires explicit and accurate model and measurement error information, leading to difficulties in practice when only limited information on error mechanisms of observational in-struments for subsurface systems is accessible. To handle the uncertain errors, we applied a robust data assimilation algorithm, the ensemble H-infinity filter （EnHF）, to estimation of aquifer hydraulic heads and conductivities in a flow model with uncertain/correlated observational errors. The impacts of spatial and temporal correlations in measurements were analyzed, and the performance of EnHF was compared with that of the EnKF. The results show that both EnHF and EnKF are able to estimate hydraulic conductivities properly when observations are free of error; EnHF can provide robust estimates of hydraulic conductivities even when no observational error information is provided. In contrast, the estimates of EnKF seem noticeably undermined because of correlated errors and inaccurate error statistics, and filter divergence was observed. It is concluded that EnHF is an efficient assimilation algorithm when observational errors are unknown or error statistics are inaccurate.

关键词： Data assimilation Hydraulic parameter estimation ensemble H-Infinity filter ensemble kalman filter Hydraulic conductivity Robustness

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Impact of Coastal Radar Observability on the Forecast of the Track and Rainfall of Typhoon Morakot(2009)Using WRF-based ensemble kalman filter Data Assimilation

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Advances in Atmospheric Sciences 2017年第1期34卷 66-78页

作者： Jian YUE Zhiyong MENG Cheng-Ku YU Lin-Wen CHENG Laboratory for Climate and Ocean–Atmosphere Studies Department of Atmospheric and Oceanic SciencesSchool of PhysicsPeking University Department of Atmospheric Sciences NTU

This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot （2009） using a WRF-based ensemble kalman filter （EnKF） data assimilation （DA） system. The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone （TC）. It was found that assimilating radial velocity （Vr） data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall. The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled. Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment. Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line. However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts. Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.

关键词： radial velocity ensemble kalman filter observability tropical cyclone track rainfall

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Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble kalman filter and uncertainty assessment for improving winter wheat yield estimation

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Journal of Integrative Agriculture 2017年第10期16卷 2283-2299页

作者： LI He JIANG Zhi-wei CHEN Zhong-xin REN Jian-qiang LIU Bin Hasituya Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences National Meteorological Information Center China Meteorological Administration

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

关键词： winter wheat yield estimates crop model data assimilation ensemble kalman filter uncertainty leaf area index

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Assimilating temperature and salinity profiles using ensemble kalman filter with an adaptive observation error and T-S constraint

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Acta Oceanologica Sinica 2016年第1期35卷 30-37页

作者： LIU Danian SHI Ping SHU Yeqiang YAO Jinglong WANG Dongxiao SUN Lu State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou 510301 China South China Sea Monitoring Center South China Sea Branch State Oceanic Administration Guangzhou 510301 China

Temperature （T） and salinity （S） profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using ensemble kalman filter （EnKF）. An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data （satellite remote sensing sea level anomaly （SLA）, the OSCAR velocity product and shipboard ADCP）. The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error （RMSE） of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.

关键词： ensemble kalman filter adaptive observation error T-S constraint

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Using Analysis State to Construct a Forecast Error Covariance Matrix in ensemble kalman filter Assimilation

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Advances in Atmospheric Sciences 2013年第5期30卷 1303-1312页

作者：郑小谷吴国灿张树鹏梁晓戴永久李勇 College of Global Change and Earth System Science Beijing Normal University National Meteorological Information Center China Meteorological Administration School of Mathematical Sciences Beijing Normal University

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed. The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error eovariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.

关键词： data assimilation ensemble kalman filter error covariance inflation adaptive estimation maximum likelihood estimation

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Nonlinear Measurement Function in the ensemble kalman filter

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Advances in Atmospheric Sciences 2014年第3期31卷 551-558页

作者： Youmin TANG Jaison AMBANDAN Dake CHEN Environmental Science and Engineering University of Northern BritishColumbiaPrince GeorgeCanadaV2N 4Z9 State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyState Oceanic Administration International Max Planck Research School on Earth System Modelling Max Planck Institute for MeteorologyHamburgGermany 20146

ABSTRACT The optimal kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble kalman filter algorithm seems to contain implicit assumptions： the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized. While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent. On the above basis, we present two modified kalman gain algorithms. Compared to the current kalman gain algorithm, the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.

关键词： ensemble kalman filter measurement function data assimilation.

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Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an ensemble kalman filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

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Acta meteorologica Sinica 2013年第3期27卷 379-399页

作者：薛明董继立 Center for Analysis and Prediction of Storms University of Oklahoma School of Meteorology University of Oklahoma Key Laboratory for Mesoscale Severe Weather/Ministry of Education and School of Atmospheric ScienceNanjing University

Extending an earlier study, the best track minimum sea level pressure （MSLP） data are assimilated for landfalling Hurricane Ike （2008） using an ensemble kalman filter （EnKF）, in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS （Global Forecast System） analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity （Z） only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind （Vr） data （assimilated at 10-min intervals）, but assimilating MSLP at 60-rain intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly （noticeably） larger positive impact than Vr （Z） data. When V~ or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1 h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.

关键词： hurricane data assimilation ensemble kalman filter best track

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A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial ensemble Generation of an ensemble kalman filter

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Atmospheric and Oceanic Science Letters 2010年第3期3卷 165-169页

作者： Zheng Fei Zhu Jiang International Center for Climate and Environment Science (ICCES) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.

关键词： ensemble kalman filter initial ensemble generation multivariate empirical orthogonal function

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Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the ensemble kalman filter (EnKF)

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Advances in Atmospheric Sciences 2005年第6期22卷 936-945页

作者：张述文李吴睿张卫东邱崇践李新 School of Atmospheric Sciences Lanzhou University Lanzhou 730000 Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province Lanzhou 7300203 Key Laboratory of Arid Climatic Changing and Reducing Disaster of Gansu Province Lanzhou 730020 School of Physical Sciences and Technology Lanzhou University Lanzhou 730000 Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000

The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble kalman filter （EnKF） assimilation scheme, including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The ＂true＂ soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d^-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

关键词： soil moisture ensemble kalman filter insertion land data assimilation

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