采用FNL再分析资料和美国联合台风警报中心(Joint Typhoon Warning Center,JTWC)资料,运用中尺度WRF(Weather Research and Forecasting)模式,分别使用增长模繁殖法(Breeding of Growing Mode,BGM)和集合卡尔曼变换方法(Ensemble Transf...采用FNL再分析资料和美国联合台风警报中心(Joint Typhoon Warning Center,JTWC)资料,运用中尺度WRF(Weather Research and Forecasting)模式,分别使用增长模繁殖法(Breeding of Growing Mode,BGM)和集合卡尔曼变换方法(Ensemble Transform Kalman Filter,ETKF),对1209号台风"苏拉"进行了台风路径的集合预报试验,并对预报效果进行对比分析。结果表明:采用BGM或ETKF初始扰动的集合预报系统,集合平均预报对风场、温度场、位势高度场的预报效果均优于控制预报;ETKF方法的预报改进程度较BGM方法更大,且对风场和温度场预报技巧的优势尤为明显。BGM方法所得到的集合成员离散度小于ETKF方法,对大气真实状态的表征能力不及后者;两种扰动方法的集合平均都明显改善了台风"苏拉"的路径预报结果,尤其是控制预报在福建沿海第二次登陆后移速过快的问题,但对台风登陆位置预报的改进不明显;此外,采用ETKF方法的集合平均对台风"苏拉"路径预报的改进效果远优于采用BGM方法的集合平均预报。展开更多
利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换...利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换卡尔曼滤波(ensemble transform Kalman filter)得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把"流依赖"的集合协方差信息引入到变分目标函数中去,在3DVAR框架基础下与观测数据进行融合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF-3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置比3DVAR更加接近"真实场",对台风路径预报也有明显改进。通过对比Hybrid S试验与Hybrid F试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的"流依赖"信息,集合平均场代替确定性背景场带来的效果并不显著。展开更多
理论上,集合卡尔曼变换(ETKF——The Ensemble Trans form Kalman filter)方法产生的集合扰动在观测空间具有等概率分布的特征,这一特点恰好可以弥补业务集合预报中常用的初值生成方法——Breeding方法的不足。依据ETKF理论和方法,利用G...理论上,集合卡尔曼变换(ETKF——The Ensemble Trans form Kalman filter)方法产生的集合扰动在观测空间具有等概率分布的特征,这一特点恰好可以弥补业务集合预报中常用的初值生成方法——Breeding方法的不足。依据ETKF理论和方法,利用GRAPES-Meso中尺度模式建立了简单的基于ETKF的中尺度集合预报试验平台,并选取2005年11月8—12日出现的一次降雨过程进行集合预报试验,旨在研究ETKF方法用于集合预报时,对随时间变化的区域非线性系统,在有限集合数的条件下的特征。试验结果揭示了利用ETKF方法进行实际区域模式下集合预报的可行性及其一些基本性质,并指出试验当中存在的不足及今后研究的重点。展开更多
Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper, ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System （GRAPES） global ensemb...Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper, ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System （GRAPES） global ensemble prediction is developed in terms of the ensemble transform Kalman filter （ETKF） method. A new GRAPES global ensemble prediction system （GEPS） is also constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based inflation, are carried out for about two months. The structure characters and perturbation amplitudes of the ETKF initial perturbations and the perturbation growth characters are analyzed, and their qualities and abilities for the ensemble initial perturbations are given. The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect the perturbation amplitudes. The initial perturbations and the spread are reasonable. The initial perturbation variance, which is approximately equal to the forecast error variance, is found to respond to changes in the observational spatial variations with simulated observational network density. The perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained up to 96-h lead time. The statistical results for 52-day ensemble forecasts show that the forecast scores of ensemble average for the Northern Hemisphere are higher than that of the control forecast. Provided that using more ensemble members, a real-time observational network and a more appropriate inflation factor, better effects of the ETKF-based initial scheme should be shown.展开更多
The impacts of AMSU-A and IASI（Infrared Atmospheric Sounding Interferometer） radiances assimilation on the prediction of typhoons Vicente and Saola（2012） are studied by using the ensemble transform Kalman filter/t...The impacts of AMSU-A and IASI（Infrared Atmospheric Sounding Interferometer） radiances assimilation on the prediction of typhoons Vicente and Saola（2012） are studied by using the ensemble transform Kalman filter/three-dimensional variational（ETKF/3DVAR） Hybrid system for the Weather Research and Forecasting（WRF） model. The experiment without assimilating radiance data in 3DVAR is compared with two experiments using the 3DVAR and ETKF/3DVAR hybrid systems to assimilate AMSU-A radiance,respectively. The results show that AMSU-A radiance data have slight positive impacts on track forecasts of the 3DVAR system. When the ETKF/3DVAR hybrid system is employed, typhoon track forecast skills are greatly improved. For 36-h forecasts, the hybrid system has a lower root-mean-square error for wind and temperature at most levels, and specific humidity at low levels, compared to 3DVAR. It is also found that, on average, the use of the IASI radiance data along with AMSU-A radiance data in the hybrid system further increases the track, wind, and specific humidity forecast accuracy compared to the experiment without IASI radiance assimilation.展开更多
Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturb...Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)methods.Results show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time steps.Due to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow subsystem.Due to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables.Regarding the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs overall.The NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow margin.In particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble prediction.These results are simply implied in a real coupled air–sea model.For the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.展开更多
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 stu...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.展开更多
文摘采用FNL再分析资料和美国联合台风警报中心(Joint Typhoon Warning Center,JTWC)资料,运用中尺度WRF(Weather Research and Forecasting)模式,分别使用增长模繁殖法(Breeding of Growing Mode,BGM)和集合卡尔曼变换方法(Ensemble Transform Kalman Filter,ETKF),对1209号台风"苏拉"进行了台风路径的集合预报试验,并对预报效果进行对比分析。结果表明:采用BGM或ETKF初始扰动的集合预报系统,集合平均预报对风场、温度场、位势高度场的预报效果均优于控制预报;ETKF方法的预报改进程度较BGM方法更大,且对风场和温度场预报技巧的优势尤为明显。BGM方法所得到的集合成员离散度小于ETKF方法,对大气真实状态的表征能力不及后者;两种扰动方法的集合平均都明显改善了台风"苏拉"的路径预报结果,尤其是控制预报在福建沿海第二次登陆后移速过快的问题,但对台风登陆位置预报的改进不明显;此外,采用ETKF方法的集合平均对台风"苏拉"路径预报的改进效果远优于采用BGM方法的集合平均预报。
文摘理论上,集合卡尔曼变换(ETKF——The Ensemble Trans form Kalman filter)方法产生的集合扰动在观测空间具有等概率分布的特征,这一特点恰好可以弥补业务集合预报中常用的初值生成方法——Breeding方法的不足。依据ETKF理论和方法,利用GRAPES-Meso中尺度模式建立了简单的基于ETKF的中尺度集合预报试验平台,并选取2005年11月8—12日出现的一次降雨过程进行集合预报试验,旨在研究ETKF方法用于集合预报时,对随时间变化的区域非线性系统,在有限集合数的条件下的特征。试验结果揭示了利用ETKF方法进行实际区域模式下集合预报的可行性及其一些基本性质,并指出试验当中存在的不足及今后研究的重点。
基金Supported by the National Natural Science Foundation of China under Grant Nos.40675064,40518001, and 40675062CMA NWP Innovational Research Project-"Key Technology of Global Operational Data Assimilation System"
文摘Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper, ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System （GRAPES） global ensemble prediction is developed in terms of the ensemble transform Kalman filter （ETKF） method. A new GRAPES global ensemble prediction system （GEPS） is also constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based inflation, are carried out for about two months. The structure characters and perturbation amplitudes of the ETKF initial perturbations and the perturbation growth characters are analyzed, and their qualities and abilities for the ensemble initial perturbations are given. The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect the perturbation amplitudes. The initial perturbations and the spread are reasonable. The initial perturbation variance, which is approximately equal to the forecast error variance, is found to respond to changes in the observational spatial variations with simulated observational network density. The perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained up to 96-h lead time. The statistical results for 52-day ensemble forecasts show that the forecast scores of ensemble average for the Northern Hemisphere are higher than that of the control forecast. Provided that using more ensemble members, a real-time observational network and a more appropriate inflation factor, better effects of the ETKF-based initial scheme should be shown.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2013CB430102)National Natural Science Foundation of China(41375025)Innovation Scientific Research Program for College Graduates of Jiangsu Province(CXZZ12-0490)
文摘The impacts of AMSU-A and IASI（Infrared Atmospheric Sounding Interferometer） radiances assimilation on the prediction of typhoons Vicente and Saola（2012） are studied by using the ensemble transform Kalman filter/three-dimensional variational（ETKF/3DVAR） Hybrid system for the Weather Research and Forecasting（WRF） model. The experiment without assimilating radiance data in 3DVAR is compared with two experiments using the 3DVAR and ETKF/3DVAR hybrid systems to assimilate AMSU-A radiance,respectively. The results show that AMSU-A radiance data have slight positive impacts on track forecasts of the 3DVAR system. When the ETKF/3DVAR hybrid system is employed, typhoon track forecast skills are greatly improved. For 36-h forecasts, the hybrid system has a lower root-mean-square error for wind and temperature at most levels, and specific humidity at low levels, compared to 3DVAR. It is also found that, on average, the use of the IASI radiance data along with AMSU-A radiance data in the hybrid system further increases the track, wind, and specific humidity forecast accuracy compared to the experiment without IASI radiance assimilation.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos. 42225501, 42105059)
文摘Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)methods.Results show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time steps.Due to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow subsystem.Due to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables.Regarding the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs overall.The NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow margin.In particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble prediction.These results are simply implied in a real coupled air–sea model.For the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.
基金Project supported by the Foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research of China(No.614220119040101)the National Natural Science Foundation of China(No.91852115)。
文摘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.
