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1.
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 in ation, 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 ofensemble 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. 相似文献
2.
为描述GRAPES全球模式初始条件的不确定性,基于适合集合预报应用的GRAPES全球奇异向量技术,依据大气初始误差符合正态分布的特征,采用高斯取样奇异向量来构造全球集合预报初始扰动,在此基础上建立了GRAPES全球集合预报系统(GRAPES-GEPS)。利用GRAPES全球同化分析场,对采用初始扰动的GRAPES-GEPS连续试验预报结果进行检验和分析。结果表明:GRAPES-GEPS中高度场、风场及温度场预报的集合离散度能有效快速增加,集合平均均方根误差与集合离散度的关系合理;相对控制预报的均方根误差,集合平均的预报优势在预报中期非常显著。为进一步体现GRAPES-GEPS中模式物理过程的不确定性,发展了模式物理过程倾向随机扰动技术(SPPT)。试验结果表明:SPPT方案的应用有效提高了GRAPES-GEPS在南、北半球和热带地区等压面要素预报的集合离散度,同时一定程度减小了集合平均误差,进而改进了集合平均误差与集合离散度的关系,其中SPPT方案在热带地区的改进最为显著。本文发展的基于奇异向量的初始扰动方法和模式扰动SPPT方案在中国气象局2018年12月业务化运行的GRAPES-GEPS中得到了应用。 相似文献
3.
ZHONG Ke WANG Yegui MA Huanyu DONG Peiming CAI Qif KANG Jianwei 《Acta Meteorologica Sinica》2010,24(3):307-317
In ensemble forecast,by summing up ensemble members,filtering the uncertainty,and retaining the common component,the ensemble mean with a better result can be achieved.However,the filtering works only when the initial perturbation develops nonlinearly.If the initial perturbation propagates in a linear space,the positive and negative members will counteract,leading to little difference between ensemble mean and control forecast and finally insignificant ensemble result.In 1 2-day ensemble forecast,based on singular vector (SV) calculations,to avoid this insignificance,the counteracting members originated from the same SV are advised not to put into the ensemble system together;the only candidate should be the one with the better forecast.Based on the ingredient analysis of initial perturbation development,a method to select ensemble members is presented in this paper,which can fulfill the above requirement.The regional model MM5V1 of NCAR/PSU (National Center for Atmosphere Research/Pennsylvania State University) and its corresponding tangent adjoint model are used.The ensemble spread and forecast errors are calculated with dry energy norm.Two mesoscale lows on the Meiyu front along the Yangtze River are examined.According to the analysis of the perturbation ingredient,among couples of counteracting members from different SVs,those members performing better always have smaller or greater spread compared with other members.Following this thinking,an optimized ensemble and an inferior ensemble are identified.The ensemble mean of the optimized ensemble is more accurate than that of the inferior ensemble,and the former also performs better than the traditional ensemble with positive and negative members simultaneously.As for growth of the initial perturbation,those initial perturbations originated from the summed SVs grow more quickly than those from the single SV,and they enlarge the range of spread of the ensemble effectively,thus leading to better performance of ensemble members. 相似文献
4.
The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors(BVs) are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector(NLLV) scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram–Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more components in analysis errors than the BVs.In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model.The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random perturbation(RP) technique, and the BV method, as well as its improved version—the ensemble transform Kalman filter(ETKF)method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme. 相似文献
5.
集合卡尔曼滤波同化多普勒雷达资料的数值试验 总被引：25，自引：10，他引：25
利用集合卡尔曼滤波(EnKF)在云数值模式中同化模拟多普勒雷达资料,并考察了不同条件下EnKF同化方法的性能.结果显示,经过几个同化周期后,EnKF分析结果非常接近真值.单多普勒雷达资料EnKF同化对雷达位置不太敏感,双雷达资料同化结果在同化的初期阶段比单雷达资料同化结果准确.同化由反射率导出的雨水比直接同化反射率资料更有效,联合同化径向速度和雨水有利于提高同化分析效果.协方差对EnKF同化效果起着非常重要的作用,考虑模式全部预报变量与径向速度协方差的同化效果比仅考虑速度场与径向速度协方差的同化效果好.雷达资料缺值降低了同化效果,此时增加地面常规观测资料的同化可以明显提高同化分析效果.EnKF同化技术对雷达观测资料误差不太敏感.初始集合对同化分析有较大影响.EnKF同化受集合大小和观测资料影响半径.同化对模式误差较敏感.利用EnKF同化双多普勒雷达资料,分析了一次梅雨锋暴雨过程的中尺度结构.结果表明,EnKF同化技术能够从双多普勒雷达资料反演暴雨中尺度系统的动力场、热力场和微物理场,反演的风场是较准确的,反演的热力场和微物理场分布也是基本合理的.中低层切变线是此次暴雨的主要动力特征,对流云表现为低层辐合、高层辐散并有垂直上升运动伴随,其热力特征表现为低层是低压区,高层为高压区,中部为暖区而上、下部为冷区,水汽、云水和雨水分别集中在对流云体内、上升气流区和强回波区. 相似文献
6.
集合预报初始扰动准确描述大气运动的不确定性是集合预报研究的核心问题,合理的扰动结构及振幅应能更好地反映大气运动状态的预报误差特征。随着集合扰动和资料同化的深入研究和理解,集合初始扰动方案与集合同化紧密结合协同发展。本文基于中国气象局数值预报中心自主研发的GRAPES-REPS集合预报系统,针对其初始扰动的结构和振幅与预报误差一致性较差的不合理问题,结合不同空间尺度天气系统预报误差特征,将表征预报不确定性的集合扰动与表达观测和预报不确定性的资料同化分析增量有效结合,研究提出了一种改善集合初始扰动质量的分析约束方案,以实现对集合初始扰动质量进一步改善。分析约束方案充分考虑资料同化分析增量的空间结构和量值特征,分别构造了单一定常和具有一定适应能力的两种分析约束函数,实现对初始扰动中不合理信息的识别和约束调整。试验结果表明,具有适应能力的分析约束方案对集合初始扰动具有良好的调整能力,约束后集合扰动的结构和振幅与中小尺度天气系统的预报误差更为吻合,其集合离散度和扰动能量的空间结构与演变特征更加趋于合理。分析约束方案可有效改善集合初始扰动质量及其预报性能。 相似文献
7.
DENG Guo TIAN Hu LI Xiaoli CHEN Jing GONG Jiandong JIAO Meiyan 《Acta Meteorologica Sinica》2012,26(1):52-61
To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors,three ensemble prediction systems using both initial perturbation methods but with different ensembl... 相似文献
8.
在完善WRF集合均方根滤波同化系统的双向嵌套预报方案、雷达资料质量控制方案和多微物理过程积分方案的基础上,引入了一种扰动势函数、流函数的尺度较小、物理意义更明显的集合初始扰动新方案.通过台风“桑美”登陆过程中的多普勒雷达资料直接同化试验,对比新扰动方案与目前应用较多的背景误差协方差随机扰动和全场随机扰动方案,对集合滤波同化效果的影响.试验结果表明,3种初始扰动方案都有效改善了台风内部结构,但新方案由于尺度更小、结构更为合理,其同化效果也更为显著,提高了对台风路径、中心最低气压、最大风速及降水的预报质量. 相似文献
9.
GRAPES全球集合预报系统不同随机物理扰动方案影响分析 总被引：1，自引：0，他引：1
为了更好地理解不同随机物理扰动方案对全球中期集合预报的影响差异,本研究基于GRAPES全球集合预报系统（GRAPES-GEPS）对比分析了随机物理倾向扰动（Stochastically Perturbed Parameterization Tendencies,SPPT）、随机动能补偿（Stochastic Kinetic Energy Backscatter,SKEB）及联合使用SPPT与SKEB三种模式扰动方案所产生的扰动特征及其对集合预报的影响。为避免初值扰动影响,考察随机物理方案所产生的扰动特征时,不使用初值扰动。通过扰动与误差相关性分析（PECA）发现,不同随机物理扰动方案所产生的扰动对预报误差均具有一定的描述能力,而且联合使用SPPT与SKEB方案时,扰动对误差的描述能力最好。对所有扰动方案来说,扰动总能量最初主要集中在热带地区对流层中高层以及平流层低层。随着预报时效的延长,扰动总能量不断增大,其大值区不断向热带外地区转移。从扰动总能量的谱结构来看,扰动能量均呈现升尺度发展的特征。在基于奇异向量初值扰动的GRAPES-GEPS中,随机物理扰动方案的使用均能够显著增加不同地区等压面要素的集合离散度,并在一定程度上改善集合平均误差。由于集合离散度的增大,预报失误率显著减小。连续分级概率评分也有所减小,尤其是在热带地区,改进更为明显。此外,中国地区不同量级（小雨、中雨、大雨和暴雨）降水概率预报技巧在一定程度上得到改善。上述改进均在联合使用SPPT与SKEB方案时最好,这与扰动总能量、扰动与误差相关分析结果一致。 相似文献
10.
This study examines the performance of coupling the deterministic four-dimensional
variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a
superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR)
benefits from using the state-dependent uncertainty provided by EnKF while taking advantage
of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum
likelihood solutions through minimization of a cost function about which the ensemble
perturbations are transformed, and the resulting ensemble analysis can be propagated forward
both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility
and effectiveness of this coupled approach are demonstrated in an idealized model with
simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR
and the EnKF under both perfect- and imperfect-model scenarios. The performance of the
coupled scheme is also less sensitive to either the ensemble size or the assimilation window
length than those for standard EnKF or 4DVAR implementations. 相似文献
11.
利用自主构建的基于风暴尺度的WRF-En SRF系统同化模拟多普勒雷达资料,讨论了微物理方案及其参数的不确定性对同化效果的影响。试验采用组合微物理方案以及扰动微物理方案中的参数的方法,结果表明,模式误差非常小甚至可以忽略时,使用单个微物理方案并扰动参数能够使真实风暴的主要特征在分析场中较未扰动参数得到更好地反映;存在模式误差时,使用单个微物理方案并扰动参数后,分析场中的各要素的分布较未扰动参数更加接近真实风暴,同化效果得到改进,且改进效果比模式误差非常小时更为明显;存在模式误差时,组合微物理方案并扰动参数后,分析场中对流云团的形态较未组合方案或未扰动参数更接近真实风暴,主要要素场的配置最能反映真实风暴的特征,同化效果最为理想。结果也表明,扰动参数时、参数扰动范围较小时,同化效果较优。 相似文献
12.
以MM5模式为试验模式，分别用增长模繁殖法（BGM）和模式物理过程扰（MPP）形成12个集合成员，对2005年登陆我国的8个热带气旋进行了52次路径集合预报试验，以寻找适合西北太平洋热带气旋路径的集合预报方法。结果表明:BGM方法的集合预报结果总体上好于控制试验结果，其中对强度较弱的热带气旋的集合预报效果更好。MPP方法的集合预报结果总体上不如控制试验结果，但对初始时刻强度达到台风的热带气旋的集合预报效果较好，集合预报相对于控制试验的改进效果明显。基于BGM方法和MPP方法集合预报的不同特点，将两种方法相结合形成扰动成员，这一综合方法的集合预报效果好于单一方法的集合预报效果。BGM方法和MPP方法的系统发散度总体上都偏小。 相似文献
13.
基于动力降尺度的区域集合预报初值扰动构建方法研究 总被引：4，自引：3，他引：1
利用全球集合预报系统资料(Global Ensemble Forecast System,GEFS),基于WRF中尺度模式构建了区域集合预报系统,区域集合初值的构建采用两种方案,一种是GEFS全球集合预报初值场直接动力降尺度(称为DOWN集合),另一种是提取GEFS全球集合降尺度后的扰动场,并叠加到区域数值预报系统(北京快速更新循环数值预报系统:Beijing Rapid Update Cycle System,BJ-RUC)分析场上构建集合初值场(称为D-RUC集合)。进行了批量试验,通过对比发现D-RUC集合的中小尺度扰动增长优于DOWN集合,而大尺度扰动分量的增长两者相当,说明与高分辨率分析场叠加可以促进动力降尺度扰动的中小尺度扰动分量的增长。集合预报扰动准确性检验结果显示,短预报时效内DOWN集合扰动明显低估了预报误差,在预报误差较大的位置扰动较小,而D-RUC集合能够更好地识别预报场中哪些位置预报误差较大,而哪些位置预报误差较小。集合预报检验结果表明,D-RUC方法能显著改善短时效预报效果,集合离散度有所增加、均方根误差有所减少,概率预报评分显示D-RUC集合比DOWN集合在短预报时效占优。降水个例分析结果表明D-RUC方法能显著改善短时效内的降水概率预报效果。 相似文献
14.
风暴尺度集合预报中的混合初始扰动方法及其在北京2012年“7.21”暴雨预报中的应用 总被引：5，自引：1，他引：4
风暴尺度集合预报系统（Storm-Scale Ensemble Forecast system,简称SSEFs）中集合成员之间发散度不足一直都是研究的难点。本文尝试了将Barnes空间滤波融入到集合转换卡尔曼滤波（ETKF）更新预报系统中的混合初值扰动法。该方案将ETKF方法的小尺度信息与来自于侧边界条件扰动的大尺度信息相结合,缓解了扰动在侧边界不匹配的问题。通过2012年北京“7.21”暴雨并使用邻位方法对比分析了不同初值扰动方案在不同时间尺度与空间尺度上的特征,在此基础上进一步探讨了构造混合初始扰动法的可行性。结果表明:ETKF试验所构造的初始扰动无法与侧边界条件扰动相匹配,混合后的初始扰动可以有效缓解SSEFs中由于初始扰动与侧边界扰动不匹配产生的虚假波动,其中大尺度信息保留较多的混合试验（ETKF80）和动力降尺度方案（Down）在减少虚假波动方面的效果最优;从集合离散度来看,在前期暖区降水阶段ETKF的离散度在小尺度上最大,随着锋面降水的开始,Down的离散度逐渐超过ETKF,而使用各滤波波段构造的混合试验同时具备ETKF与Down二者的特征。选择合理的滤波波段可以获得最为合理的离散度表现（ETKF180）,说明仅考虑侧边界匹配（Down和ETKF80）并不能获得最合理的集合离散度,应综合考虑其他因素。从降水概率预报结果来看,选取合适的滤波波段所构造的混合扰动试验同样获得了较好的效果。 相似文献
15.
Qian ZOU Quanjia ZHONG Jiangyu MAO Ruiqiang DING Deyu LU Jianping LI Xuan LI 《大气科学进展》2023,40(3):501-513
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 ave... 相似文献
16.
Heavy Rainfall Ensemble Prediction： Initial Condition Perturbation vs Multi-Physics Perturbation 总被引：4，自引：0，他引：4 
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下载免费PDF全文 Mesoscale ensemble is an encouraging technology for improving the accuracy of heavy rainfall predictions. Occurrences of heavy rainfall are closely related to convective instability and topography. In mid-latitudes, perturbed initial fields for medium-range weather forecasts are often configured to focus on the baroclinic instability rather than the convective instability. Thus, alternative approaches to generate initial perturba- tions need to be developed to accommodate the uncertainty of the convective instability. In this paper, an initial condition perturbation approach to mesoscale heavy rainfall ensemble prediction, named as Different Physics Mode Method （DPMM）, is presented in detail. Based on the PSU/NCAR mesoscale model MM5, an ensemble prediction experiment on a typical heavy rainfall event in South China is carried out by using the DPMM, and the structure of the initial condition perturbation is analyzed. Further, the DPMM ensem- ble prediction is compared with a multi-physics ensemble prediction, and the results show that the initial perturbation fields from the DPMM have a reasonable mesoscale circulation structure and could reflect the prediction uncertainty in the sensitive regions of convective instability. An evaluation of the DPMM ini- tial condition perturbation indicates that the DPMM method produces better ensemble members than the multi-physics perturbation method, and can significantly improve the precipitation forecast than the control non-ensemble run. 相似文献
17.
利用T63L9全球谱模式和NCEP/NCAR再分析资料, 对BGM方法中增长模的繁殖长度对集合预报效果的影响进行研究。结果表明:与控制预报相比, 不同繁殖长度的集合预报都能使预报效果得到一定程度的改进, 特别是第4天预报以后, 改进程度随预报时效而稳步提高。三组不同繁殖长度的集合预报对控制预报的改进存在差别, 分析结果表明:繁殖长度为2 d的集合预报明显效果最差, 而繁殖3 d和4 d的集合预报差别并不明显。对集合Talagrand分布以及离散度的初步分析表明, 繁殖长度取为3 d似乎最为合理。 相似文献
18.
基于集合Kalman滤波数据同化的热带气旋路径集合预报研究 总被引：1，自引：2，他引：1
构建了一个基于集合Kalman滤波数据同化的热带气旋集合预报系统，通过积云参数化方案和边界层参数化方案的9个不同组合，采用MM5模式进行了不同时间的短时预报。对预报结果使用“镜像法”得到18个初始成员，为同化提供初始背景集合。将人造台风作为观测场，同化后的结果作为集合预报的初值，通过不同参数组合的MM5模式进行集合预报。对2003～2004年16个台风个例的分析表明，初始成员产生方法能够对热带气旋的要素场、中心强度和位置进行合理扰动。同化结果使台风强度得到加强，结构更接近实际。基于同化的集合路径预报结果要优于未同化的集合预报。使用“镜像法”增加集合成员提高了预报准确度，路径预报误差在48小时和72小时分别低于200 km和250 km。 相似文献
19.
Formulating model uncertainties for a convection-allowing ensemble prediction system(CAEPS) is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting. A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model, due to the fast developing character and strong nonlinearity of convective events. The Conditional Nonlinear Optimal Perturbation related to Parameters(CNOP-P) is applied in this study. Also, an ensemble approach is adopted to solve the CNOP-P problem. By using five locally developed strong convective events that occurred in pre-rainy season of South China, the most sensitive parameters were detected based on CNOP-P, which resulted in the maximum variations in precipitation. A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive parameters. Through comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017, the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies(SPPT) scheme. The results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS. 相似文献
20.
This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error. 相似文献