耦合多目标遗传算法的数据同化方法参数优化研究
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摘要
针对影响数据同化系统性能的数据同化方法中多参数优化问题,设计了一种耦合多目标优化非支配排序遗传算法(Non-dominated Sorting Genetic Algorithms,NSGA-2)的优化框架。以Lorenz-96模型为研究对象,局地化集合转换卡尔曼滤波(Local Ensemble Transform Kalman Filter,LETKF)为实验算法,对影响其同化性能的分析膨胀因子和协方差膨胀因子进行联合优化实验。同时针对计算时间过长问题,对该框架进行并行改进。研究结果表明:该框架针对变量范围较大的参数空间,具有良好的多变量寻优效果,鲁棒性较强,并行设计节省运行时间,耦合多目标遗传算法的数据同化方法整体寻优框架设计实现简单,进一步将验证其在陆面数据同化中的应用前景。
To solve the problem of multi-parameter optimization in data assimilation method which affects the performance of data assimilation system,an optimization framework was proposedcoupled with objective optimization Non-dominated Sorting Genetic Algorithms(NSGA-2).To choose Lorenz-96 model as the research object,the Local Ensemble Transform Kalman Filter(LETKF)was used as the experimental algorithm.The analysis inflation factor and the covariance inflation factor that affect data assimilation performance were studied together in new framework.At the same time,the problem of over-long computing time was solved by paralleling the framework.The results showed that this framework had a good multivariable optimization for parameter space with large variable range and strong robustness.Parallel design could save the running time.Data assimilation coupled with multi-objective genetic algorithm and overall optimization framework design are easy to use.Further verification of its land data assimilation use were explored in the future.
To solve the problem of multi-parameter optimization in data assimilation method which affects the performance of data assimilation system,an optimization framework was proposedcoupled with objective optimization Non-dominated Sorting Genetic Algorithms(NSGA-2).To choose Lorenz-96 model as the research object,the Local Ensemble Transform Kalman Filter(LETKF)was used as the experimental algorithm.The analysis inflation factor and the covariance inflation factor that affect data assimilation performance were studied together in new framework.At the same time,the problem of over-long computing time was solved by paralleling the framework.The results showed that this framework had a good multivariable optimization for parameter space with large variable range and strong robustness.Parallel design could save the running time.Data assimilation coupled with multi-objective genetic algorithm and overall optimization framework design are easy to use.Further verification of its land data assimilation use were explored in the future.
引文
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[23] Gong D,Sun J,Miao Z.A Set-based Genetic Algorithm for Interval Many-Objective Optimization Problems[J].IEEETransactions on Evolutionary Computation,2018,22(1):47-60.
[24] Kumar M,Saravanakumar K,Balakrishnan S,et al.Multi-Objective Optimization of CNC Turning Parameters Using Genetic Algorithm and Performance Evaluation of Nanocomposite Coated Carbide Inserts[J].International Journal of Data and Network Science,2018,2(4):99-108.
[2] Moradkhani H,Hsu K L,Gupta H,et al.Uncertainty Assessment of Hydrologic Model States and Parameters:Sequential Data Assimilation Using the Particle Filter[J].Water Resources Research,2005,41(5):237-246.
[3] Moradkhani H,Sorooshian S,Gupta H V,et al.Dual StateParameter Estimation of Hydrological Models Using Ensemble Kalman Filter[J].Advances in Water Resources,2005,28(2):135-147.
[4] Dechant C M,Moradkhani H.Examining the Effectiveness and Robustness of Sequential Data Assimilation Methods for Quantification of Uncertainty in Hydrologic Forecasting[J].Water Resources Research,2012,48(4):4518.doi:10.1029/2011WRO11011.
[5] Wang D B,Cai X M.Robust Data Assimilation in Hydrological Modeling-A Comparison of Kalman and-Infinity Filters[J].Advances in Water Resources,2008,31(3):455-472.
[6] Zhang F,Zhang M,Hansen J A.Coupling Ensemble Kalman Filter with Four-Dimensional Variational Data Assimilation[J].Advances in Atmospheric Sciences,2009,26(1):1-8.
[7] Reggiani P,Renner M,Weerts A H,et al.Uncertainty Assessment Via Bayesian Revision of Ensemble Streamflow Predictions in the Operational River Rhine Forecasting System[J].Water Resources Research,2009,45(2):335-345.
[8] Houtekamer P L,Mitchell H L.Data Assimilation Using an Ensemble Kalman Filter Technique[J].Monthly Weather Review,1998,126(3):796-811.
[9] Anderson J,Van Den D H,Barnston A,et al.Present-Day Capabilities of Numerical and Statistical Models for Atmospheric Extratropical Seasonal Simulation and Prediction[J].Bulletin of the American Meteorological Society,1999,80(7):1349-1362.
[10] Anderson J L,Anderson S L.A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts[J].Monthly Weather Review,1999,127(12):2741-2758.
[11] Anderson J L.Exploring the Need for Localization in Ensemble Data Assimilation Using A Hierarchical Ensemble Filter[J].Physica D Nonlinear Phenomena,2007,230(1):99-111.
[12] Hamill T M,Snyder C.Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations[J].Monthly Weather Review,2010,130(6):1552-1572.
[13] Kirchgessner P,Nerger L,Bunse G A.On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods[J].Monthly Weather Review,2014,142(6):2165-2175.
[14] Hoteit I,Pham D T,Gharamti M E,et al.Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKf[J].Monthly Weather Review,2015,143(7):2918-2936.
[15] Bai Y L,Li Xin.Evolutionary Algorithm-based Error Parameterization Methods for Data Assimilation[J].Monthly Weather Review,2011,139(8):2668-2685.
[16] Bai Y L,Li X,Huang C L.Handling Error Propagation in Sequential Data Assimilation Using an Evolutionary Strategy[J].Advances in Atmospheric Sciences,2013,30(4):1096-1105.
[17] Deb K,Pratap A,Agarwal S,et al.A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[18] Shen Zhangquan,Yu Zhoulu.Mapping with Partheno-Genetic Algorithm and Sub-Pixel/Pixel Spatial Attraction Model[J].Remote Sensing Technology and Application,2015,30(1):129-134.[沈掌泉,虞舟鲁.基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究[J].遥感技术与应用,2015,30(1):129-134.]
[19] Kadri R L,Boctor F F.An Efficient Genetic Algorithm to Solve the Resource-Constrained Project Scheduling Problem with Transfer Times:The Single Mode Case[J].European Journal of Operational Research,2018,265(2):454-462.
[20] Hunt B R,Kostelich E J,Szunyogh I.Efficient Data Assimilation for Spatiotemporal Chaos:A Local Ensemble Transform Kalman Filter[J].Physica D Nonlinear Phenomena,2005,230(1):112-126.
[21] Ott E,Hunt B R,Szunyogh I,et al.A Local Ensemble Kalman Filter for Atmospheric Data Assimilation[J].Tellus A:Dynamic Meteorology and Oceanography,2011,56(5):415-428.
[22] Yang S C,Kalnay E,Hunt B,et al.Weight Interpolation for Efficient Data Assimilation with the Local Ensemble Transform Kalman Filter[J].Quarterly Journal of the Royal Meteorological Society,2009,135(638):251-262.
[23] Gong D,Sun J,Miao Z.A Set-based Genetic Algorithm for Interval Many-Objective Optimization Problems[J].IEEETransactions on Evolutionary Computation,2018,22(1):47-60.
[24] Kumar M,Saravanakumar K,Balakrishnan S,et al.Multi-Objective Optimization of CNC Turning Parameters Using Genetic Algorithm and Performance Evaluation of Nanocomposite Coated Carbide Inserts[J].International Journal of Data and Network Science,2018,2(4):99-108.