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本研究发展了一个全球海洋资料同化系统ZFL_GODAS。该系统是一个短期气候数值预测业务系统的子系统,为短期气候预测海气耦合模式提供全球海洋初始场。系统能够同化的观测资料包括卫星高度计资料、卫星海表温度(SST)资料,以及Argo、XBT、TAO等各种不同来源的现场温盐廓线资料。系统使用的海洋模式为中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室开发的气候系统海洋模式LICOM1.0,同化方案为集合最优插值(EnOI)方案。系统使用一个由海洋模式自由积分得到的静态样本来估计背景场误差协方差。这样的基于集合样本的背景场误差协方差具有多变量协变、各向异性的特征,且能反映海洋物理过程固有的空间尺度特征。针对EnOI同化程序的特点,开发了一套特色鲜明、负载均衡、高效的并行化同化程序。本文通过与不同类型观测资料的比较,对同化系统的性能进行了评估。通过比较海表温度和海面高度的年际变率,海表温度异常随时间的变化,SST、海面高度异常(SLA)以及次表层温盐预报产品的均方根误差,5年平均温度偏差廓线、平均盐度廓线、平均纬向流速廓线等发现:系统工作正常、同化效果较好;经过同化以后,各变量都更加接近观测,误差更小,与观测场的相关性更好,可以为短期气候预测系统提供较好的海洋初始场,也可以为物理海洋学的研究提供有效的再分析资料。 相似文献
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目前多数快速更新循环同化系统在各分析时刻常使用固定的背景场误差协方差。为在快速更新循环同化系统中采用日变化的背景场误差协方差,基于RMAPS-ST系统分析了其夏季和冬季日变化背景场误差协方差特征,并进行了同化及预报对比试验。结果表明,该系统夏、冬两季的背景场误差协方差均呈现出明显的日变化特征,且夜间各变量(U、V、T、RH)的误差标准差与特征值均大于日间,反映模式系统夜间的预报误差大于日间;而夏季各变量误差标准差和特征值大于冬季,也说明系统在夏季的模式预报误差比冬季大;连续3 d的循环同化试验初步表明,采用日变化背景场误差协方差可以提高同化及预报效果。 相似文献
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Near real-time ocean circulation assimilation and prediction in the Intra-Americas Sea with ROMS 总被引：2，自引：0，他引：2
B.S. Powell A.M. Moore H.G. Arango E. Di Lorenzo R.F. Milliff R.R. Leben 《Dynamics of Atmospheres and Oceans》2009,48(1-3):46
We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting. 相似文献
4.
《Dynamics of Atmospheres and Oceans》2010,49(1-3):46-68
We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting. 相似文献
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在集合卡尔曼滤波方法中, 根据预报集合统计提供的依流型而变的预报误差协方差对同化起到决定性的作用。但在集合样本容量不足及模式存在系统误差时, 由预报集合估计的预报误差协方差会出现明显偏差。既要减小这种估计偏差对同化产生的影响而又不增加计算量, 一种可供选择的方法是将定常或准定常的高斯型预报误差协方差和由预报集合估计的预报误差协方差加权平均用于集合卡尔曼滤波同化。利用浅水方程模式, 通过观测系统模拟试验检验在不同的模式误差、 集合成员数以及观测密度条件下, 将这种混合预报误差协方差矩阵用于在集合平方根滤波的效果。试验结果表明, 当预报集合成员数较多而模式又无误差时, 不必采用混合的预报误差协方差矩阵, 否则, 采用混合的预报误差协方差矩阵都有可能改进分析和预报。混合预报误差协方差的最优的权重系数与模式误差关系密切, 模式误差越大, 定常预报误差协方差的权重越大。最优的权重系数与集合成员数及观测密度也有一定关系。 相似文献
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全球资料同化中误差协方差三维结构的准确估计与应用Ⅰ:观测空间协方差的准确估计 总被引：3，自引：0，他引：3
观测误差与背景误差协方差在四维资料同化和业务资料分析系统中起到决定性作用,它决定着观测信息与背景初猜值信息的相对重要性以及这些信息在空间及不同变量间的扩展方式。由于实际大气的真值并不知道,需要发展不同的技巧来估计观测误差与背景误差协方差,其中在观测空间利用观测与背景初猜值之差来分离观测误差与背景误差协方差的方法估计出的结果较为准确,其估计出的观测误差可直接用于资料分析系统中,背景误差可作为标尺来度量其他方法估计结果的可靠性。文章采用国家气象中心T213L31全球中期分析预报系统的6 h预报作为背景初猜场及同时段冬夏两个季节的北半球探空,利用贝塞尔函数拟合方法来分离观测误差与背景误差协方差,并比较了东亚区、北美区、欧洲区3个探空资料均匀密集区的区域与季节变化结果。结果表明,观测空间拟合方法所要求的水平均质、各向同性在欧洲区和北美区成立程度较好,在东亚区略差,使用时需要斟酌。此外均方差区域间差别较大,在冬季明显大于夏季,温度场偏大0.2 K,风场偏大0.9 m/s。温度场在400 hPa以下与150 hPa以上,背景误差略小于观测误差,而在200—300 hPa,背景误差略大一些。风场的特点与温度场比较一致。温度与风场背景误差主要集中在前40波,并在20波左右达到最大,水平相关季节区域差别不大,而温度垂直相关比风场窄,两者相关范围比较大的波数主要集中在前20波。此外利用贝塞尔函数拟合方法获得结果的分析表明,在质量场中不同区域季节间温度误差的稳定性要明显好于高度场,涡度散度的稳定性要明显好于流函数和势函数,特别是对于特征长度更为明显。这表明利用贝塞尔函数拟合方法获得的结果对校准在全球资料同化中采用温度、涡度散度作为资料同化的分析变量具有一定的优势。 相似文献
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集合变分混合同化背景误差协方差流依赖性分析 总被引：4，自引：2，他引：2
通过单点观测试验的方法,对集合变分混合同化背景误差协方差的流依赖特征、流依赖性影响因子、产生原因,以及集合预报方法对流依赖性的影响进行了研究。结果表明:由于引入了集合信息,集合变分混合同化的分析增量与天气系统的分布有关,具有非均匀、各向异性的特征;这种流依赖特征对混合系数敏感,当集合协方差所占权重很小时,分析增量仍呈现出均匀、各向同性特征;混合同化背景误差协方差的流依赖特征不仅与集合样本有关,还与构造集合协方差的ETKF方法有关,只引入与环流形势密切相关的集合样本并不能使分析增量表现出显著的流依赖性,集合样本和ETKF方法共同作用才能将流依赖信息引入到混合协方差中,使分析增量出现流依赖特征;不同集合预报方法对混合协方差的流依赖特征有显著影响,考虑初值和物理过程的超级集合,以及在超级集合样本上再进行ETKF更新扰动后样本构造的混合协方差流依赖特征更加显著。 相似文献
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正1Swedish Meteorological and Hydrological Institute, Folkborgsv?gen 17, 60361 Norrk?ping, Sweden2Norwegian Meteorological Institute, Henrik Mohns Plass 1, 0371 Oslo, Norway 相似文献
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M. Široká C. Fischer V. Cassé R. Brožková J.-F. Geleyn 《Meteorology and Atmospheric Physics》2003,82(1-4):227-244
Summary ?The paper deals with an alternative formulation of the so-called NMC (National Meteorological Center, now National Centers
for Environmental Prediction) statistics to compute the background error covariance matrix to be used in a mesoscale variational
analysis. While the standard method uses differences of forecasts valid for the same time, but starting from different analysis
times, the new formulation required the recomputation of the short-term forecast with the initial and lateral boundary data
that come from the long-term run. In the frame of a limited-area model, this approach forces the error variances at large
scales to decrease drastically, because those scales are controlled by the (constant data) lateral boundary coupling. As a
result, the background cost function acts more scale selectively, with an emphasis on medium scales. The analysis increments
obtained from the 3D-VAR system show that the analysis increments are sharper and more concentrated with the new formulation,
both in single observation and in full observation experiments. This work is part of a wider project for building a variational
assimilation system inside the ALADIN model. The complete system should concentrate on mesoscale features and it should not
reanalyse those scales that were already treated by the global model (ARPEGE). Some difficulties and perspectives are drawn
in the concluding discussion.
Received February 12, 2001; revised July 24, 2001 相似文献
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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
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敏感性试验表明集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)方法在混合(Hybrid)同化过程中易受观测资料数量变化的影响而产生较大程度的协方差震荡,从而可能导致系统不稳定。为设计一种简便、稳定的Hybrid同化系统,构建了一种基于物理控制变量扰动及多物理参数化方案的Hybrid同化及预报系统。本系统随着循环的进行,不断对Hybrid同化分析场进行控制变量扰动得到集合成员初始场,并且对各集合成员采用不同物理参数化方案以更合理地表征背景场的误差特征。连续10 d的循环同化及预报试验表明,本文同化方案效果明显优于三维变分方案,动力场的整体同化和预报效果与ETKF方案基本相当。本方案相比于ETKF方法不受观测波动影响,在没有经任何参数调试情况下,取得了良好同化和预报效果,为Hybrid同化的便捷运行提供了一种稳定可靠的手段。 相似文献
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The ensemble Kalman filter(En KF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence.Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called En CR, to estimate the inflation parameter of the forecast error variance of the En KF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods. 相似文献
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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
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MM5三维变分系统在北京地区冷暖季背景场误差的对比分析 总被引：2，自引：2，他引：2
NMC方法是目前较广泛采用的一种对模式背景场误差协方差进行统计分析的一种方法。本文根据积累的2002年8月份和2003年2月份各一个月模式预报结果,采用NMC方法,计算了中尺度模式MM5V3在北京地区的冷暖季背景场误差,详细给出其气候统计特征。通过对比分析发现,背景场误差特征对于不同的模式变量、水平分辨率、垂直层各不相同,冷暖季背景场误差也有不同的特征,其差别主要表现在风场。这些特征与模式模拟区域的平均天气状况相对应,同化应该在各模式区域分别进行。MM5三维变分系统在北京地区的实际应用中,应发展根据实际季节变换背景场误差协方差矩阵的方法。 相似文献
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The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved. 相似文献
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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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下载免费PDF全文 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. 相似文献
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该文简要介绍了中国气象局国家气象中心研发的区域中尺度集合预报系统主要技术特点:在初值扰动技术方面,通过研究中国地区中尺度模式预报误差快速增长特点、中国地形地貌特征与观测资料的分布情况,研发适合于中尺度模式的增长模繁殖法扰动技术构造初值场;分析数值模式物理过程参数化方案内在的不确定性以及对强对流天气和近地面要素预报的差异,确定多物理过程扰动技术方案。解决全球集合预报扰动信息向中尺度集合预报输入的关键技术,实现中尺度区域集合预报系统与全球中期集合预报系统的嵌套。在模式后处理方面,解决中尺度集合预报结果的偏差订正技术;开发满足多种需求的多要素、多层次概率预报产品和概率预报检验产品。在世界天气研究计划"2008年北京奥运会中尺度集合预报研究开发项目"3年实时预报试验比较评价中,中国气象局国家气象中心区域中尺度集合预报系统总体预报能力与国外同类系统相当。 相似文献