An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-...An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year(1960–2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the qualitycontrolled subsurface ocean temperature objective analyses(EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square(RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m(OHT100 m)is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT100 m convergent to one value after several times iteration,indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.展开更多
By sampling perturbed state vectors from each ensemble prediction run at properly selected time levels in the vicinity of the analysis time, the recently proposed time-expanded sampling approach can enlarge the ensemb...By sampling perturbed state vectors from each ensemble prediction run at properly selected time levels in the vicinity of the analysis time, the recently proposed time-expanded sampling approach can enlarge the ensemble size without increasing the number of prediction runs and, hence, can reduce the computational cost of an ensemble-based filter. In this study, this approach is tested for the first time with real radar data from a tornadic thunderstorm. In particular, four assimilation experiments were performed to test the time-expanded sampling method against the conventional ensemble sampling method used by ensemble- based filters. In these experiments, the ensemble square-root filter （EnSRF） was used with 45 ensemble members generated by the time-expanded sampling and conventional sampling from 15 and 45 prediction runs, respectively, and quality-controlled radar data were compressed into super-observations with properly reduced spatial resolutions to improve the EnSRF performances. The results show that the time-expanded sampling approach not only can reduce the computational cost but also can improve the accuracy of the analysis, especially when the ensemble size is severely limited due to computational constraints for real-radar data assimilation. These potential merits are consistent with those previously demonstrated by assimilation experiments with simulated data.展开更多
This work investigates an uncertainty quantification(UQ)framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance.The framework involves solving four problem...This work investigates an uncertainty quantification(UQ)framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance.The framework involves solving four problems:identifying uncertain parameters,propagating uncertainty to the quantity of interest,data assimilation and making decisions under quantified uncertainties.A specific group of UQ approaches,known as the ensemble-based methods,are adopted to solve these problems.This UQ framework is applied to coordinating a group of thermostatically controlled loads,which relies on simulating a second-order equivalent thermal parameter model with some uncertain parameters.How this uncertainty affects the prediction and the control of total power is examined.The study shows that uncertainty can be effectively reduced using the measurement of air temperatures.Also,the control objective is achieved fairly accurately with a quantification of the uncertainty.展开更多
In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionall...In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.展开更多
Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is ...Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.展开更多
Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Developm...Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction） projects, it is found that the prediction of the South China Sea summer monsoon （SCSSM） has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.展开更多
Adjoint-free calculation method is proposed to compute conditional nonlinear optimal perturbations(CNOP) combined with initial perturbations and model parameter perturbations. The new approach avoids the use of adjoin...Adjoint-free calculation method is proposed to compute conditional nonlinear optimal perturbations(CNOP) combined with initial perturbations and model parameter perturbations. The new approach avoids the use of adjoint technique in the optimization process. CNOPs respectively generated by ensemble-based and adjoint-based methods are compared based on a simple theoretical model.展开更多
基金The National Key R&D Program of China under contract No. 2017YFA0604201the National Natural Science Foundation of China under contract Nos 41876012 and 41861144015.
文摘An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year(1960–2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the qualitycontrolled subsurface ocean temperature objective analyses(EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square(RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m(OHT100 m)is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT100 m convergent to one value after several times iteration,indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.
基金supported by ONR Grants N000140410312 and N000141010778 to CIMMS,the University of Oklahomaby the radar data assimilation projects No. 2008LASW-A01 and No.GYHY200806003 at the Institute of Atmospheric Physics,Chinese Academy of SciencesProvided to CIMMS by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Coopera-tive Agreement #NA17RJ1227,U.S. Department of Commerce
文摘By sampling perturbed state vectors from each ensemble prediction run at properly selected time levels in the vicinity of the analysis time, the recently proposed time-expanded sampling approach can enlarge the ensemble size without increasing the number of prediction runs and, hence, can reduce the computational cost of an ensemble-based filter. In this study, this approach is tested for the first time with real radar data from a tornadic thunderstorm. In particular, four assimilation experiments were performed to test the time-expanded sampling method against the conventional ensemble sampling method used by ensemble- based filters. In these experiments, the ensemble square-root filter （EnSRF） was used with 45 ensemble members generated by the time-expanded sampling and conventional sampling from 15 and 45 prediction runs, respectively, and quality-controlled radar data were compressed into super-observations with properly reduced spatial resolutions to improve the EnSRF performances. The results show that the time-expanded sampling approach not only can reduce the computational cost but also can improve the accuracy of the analysis, especially when the ensemble size is severely limited due to computational constraints for real-radar data assimilation. These potential merits are consistent with those previously demonstrated by assimilation experiments with simulated data.
基金the Control of Complex Systems Initiative at Pacific Northwest National Laboratory(PNNL).PNNL is operated by Battelle for the U.S.Department of Energy under contract[DE-AC05-76RL01830].
文摘This work investigates an uncertainty quantification(UQ)framework that analyses the uncertainty involved in modelling control systems to improve control strategy performance.The framework involves solving four problems:identifying uncertain parameters,propagating uncertainty to the quantity of interest,data assimilation and making decisions under quantified uncertainties.A specific group of UQ approaches,known as the ensemble-based methods,are adopted to solve these problems.This UQ framework is applied to coordinating a group of thermostatically controlled loads,which relies on simulating a second-order equivalent thermal parameter model with some uncertain parameters.How this uncertainty affects the prediction and the control of total power is examined.The study shows that uncertainty can be effectively reduced using the measurement of air temperatures.Also,the control objective is achieved fairly accurately with a quantification of the uncertainty.
基金the support of EPIC - Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP - Sao Paulo Research Foundation (2021/04878- 7 and 2017/15736-3)financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior Brasil (CAPES) - Financing Code 001
文摘In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
基金supported by the China Meteorological Administration for the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY(QX)201406015)the Southern China Monsoon Rainfall Experiment (SCMREX)
文摘Various ensemble-based schemes are employed in data assimilation because they can use the ensemble to estimate the flow-dependent background error covariance. The most common way to generate the real-time ensemble is to use an ensemble forecast; however, this is very time-consuming. The historical sampling approach is an alternative way to generate the ensemble,by picking some snapshots from historical forecast series.With this approach, many ensemble-based assimilation schemes can be used in a deterministic forecast environment. Furthermore, considering the time that it saves, the method has the potential for operational application.However, the historical sampling approach carries with it a special kind of sampling error because, in a historical forecast, the way to integrate the ensemble members is different from the way to integrate the initial conditions at the analysis time(i.e., forcing and lateral boundary condition differences, and ‘warm start' or ‘cold start' differences). This study analyzes the results of an experiment with the Global Regional Assimilation Prediction System-Global Forecast System(GRAPES-GFS), to evaluate how the different integration configurations influence the historical sampling error for global models. The results show that the sampling error is dominated by diurnal cycle patterns as a result of the radiance forcing difference.Although the RMSEs of the sampling error are small, in view of the correlation coefficients of the perturbed ensemble, the sampling error for some variables on some levels(e.g., low-level temperature and humidity, stratospheric temperature and geopotential height and humidity), is non-negligible. The results suggest some caution must be applied, and advice taken, when using the historical sampling approach.
基金Supported by the National Natural Science Foundation of China(41421004,41325018,and 41575079)State Administration for Foreign Expert Affairs of the Chinses Academy of Sciences(CAS/SAFEA)
文摘Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models （CGCMs） from the ENSEMBLES （Ensemble-based Predictions of Climate Changes and Their Impacts） and DEME- TER （Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction） projects, it is found that the prediction of the South China Sea summer monsoon （SCSSM） has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.
基金supported by National Natural Science Foundation of China(Grant No.11201265)Shandong Natural Science Foundation(Grant No.ZR2012AM003)China Postdoctoral Science Foundation(Grant No.20110490564)
文摘Adjoint-free calculation method is proposed to compute conditional nonlinear optimal perturbations(CNOP) combined with initial perturbations and model parameter perturbations. The new approach avoids the use of adjoint technique in the optimization process. CNOPs respectively generated by ensemble-based and adjoint-based methods are compared based on a simple theoretical model.
