共查询到20条相似文献，搜索用时 831 毫秒
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传统的四维变分数据同化方法在同化观测资料的同时可以对数值模式参数进行优化,然而传统的四维变分方法需要针对不同的数值模式编写特有的伴随模式,因此算法的可移植性差,同时计算时耗费大量资源。本文提出了一种新的基于解析四维集合变分的参数优化方法,该方法以迭代搜索得到的模式参数为基准展开扰动并构建样本集合,由此显式地计算协方差矩阵,并得到代价函数极小值的解析解,从而避免了伴随模式的使用。基于Lorenz-63模型对该方法进行单参数和多参数数值试验和优化效果检验,并在不同的同化时间窗口长度和观测采样间隔情况下,采用传统四维变分方法与之进行对比,结果显示,新方法表现出与传统四维变分相同的优化性能,都能有效收敛到真值,而新方法不需要计算伴随模式,可移植性好。本文还测试了不同的集合成员个数和模式参数真值的情况下新方法的同化效果,结果表明,新方法对集合样本个数及模型参数真值不敏感,采用较少的集合样本即可完成数据同化。 相似文献
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利用区域海洋模式ROMS(Regional Ocean Modelling System)及其四维变分同化模块,建立了具有资料同化能力的东中国海风暴潮数值模式,通过将海洋站水位观测资料同化到风暴潮模式中,提高了模式对风暴潮的模拟精度。四维变分同化技术能够在整个同化时间窗口保持动力协调,使模拟结果在该时间窗口内最大程度的靠近观测,同时,得到了最优预报初始场。利用该模式,对两次温带风暴潮过程进行了数值模拟,结果表明:在同化窗口内,同化对模拟精度有明显的提高;结束同化之后,得到的最优预报初始场对临近预报精度也有一定提高。 相似文献
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本文考虑一维抛物型偏微分方程的反问题,在已知某一点的局部观测资料下利用变分方法对方程中的未知参数进行反演.在特定的初始条件下,得到了最优的反演参数,研究了测量误差对于参数的影响,并进一步对所得参数的收敛性和收敛精度进行了分析. 相似文献
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中国海及邻近海域卫星观测资料同化试验 总被引：4，自引：0，他引：4
利用1个基于POMgcs海洋模式和多重网格三维变分同化方法建立的中国海及邻近海域海面高与三维温盐流数值预报模型,通过一系列数值试验,研究了同化卫星测高和卫星遥感海面温度观测资料对该模型预报能力的影响。试验结果表明,同化卫星测高资料可明显改善海面高度与三维温度和盐度的分析预报效果,使1 200 m以上的温度预报误差减小0.16℃,并能有效提高对海洋中尺度现象的预报能力;同化卫星遥感海面温度对100 m以上的温度和盐度的预报效果有所改善,可使海面温度的预报误差减小10%。 相似文献
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利用相临过去时段预报结果中同一时刻不同时效的模式预报场差异,计算预报误差协方差,并基于集合-变分混合同化系统将其与静态背景场误差协方差结合,从而在同化系统中构建了具有各向异性和一定流依赖特征的背景场误差协方差。单点观测理想试验显示本方案改善了静态模型化背景场误差协方差的各向同性和流依赖性问题。“凡亚比”台风的一系列同化及模拟试验表明,从台风路径、强度等方面本文方案的效果都要优于三维变分法。本文方案在不需要集合预报,计算量与三维变分法相当的情况下,给同化系统引入了各向异性、一定流依赖特征的背景误差协方差,因此本方案适于在计算资源较为紧缺情况下,对时效要求较高的预报业务中应用。 相似文献
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基于背景误差分析中的观测法,利用Jason-1卫星高度计沿轨有效波高数据并结合Wave Watch Ⅲ海浪模式预报结果,进行北印度洋海域海浪背景误差分析,得到海浪场背景误差方差和各向同性假设下背景误差相关长度的时空分布特征。按经验函数拟合该海域有效波高背景误差协方差时总残差平方和最小的原则给出了更为适用于该海域的描述公式。在上述工作基础上,采用最优插值同化方法将Jason-1和Jason-2卫星高度计有效波高数据连续同化到海浪模式Wave Watch Ⅲ,按业务化标准对2013年1月北印度洋海域的海浪场进行了同化预报试验,经浮标数据检验发现同化可使海浪24 h预报得到明显改进。 相似文献
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三维斜压台风模式 Ⅱ.预报试验 总被引：1，自引：0，他引：1
一种斜压多重移动套网格台网模式在国家海洋环境预报中心已被应用于海洋环境预报。本文第一部分已描述了模式方程组和数值方法。本文继续概述模式网格、变分调整初始化和预报试验结果。最外粗网格域固定,内部细网格域随台风中心轨迹移动。模式中,粗细网格变量采用双向耦合。平衡方程和方程,理想台风场和变分调整方案被用于台风模式初始化。一种简单而有效的资料同化方法,即用第6h台风报和弱约束变分原理调整初始场,被应用于提高预报结果。最后本文给出预报试验结果。预报误差统计显示本模式对台风路径预报具有相当能力,而且可以提供海面风和气压场较好的预报。本模式已经与海浪模式联结,得到满意的波高预报结果。 相似文献
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多普勒雷达资料同化在台风“桑美”预报中的应用研究 总被引：4，自引：2，他引：2
本文以2006年超强台风"桑美"为个例,考察了同化雷达径向风观测资料对台风初始场和预报场的改进作用。首先对沿海新一代多普勒天气雷达的径向风观测资料进行了去噪音、退模糊等一系列的质量控制,进一步利用美国国家大气研究中心开发的中尺度数值模式WRFV3.5及其三维变分同化系统WRF-3DVAR,每30min循环同化雷达径向风观测资料。结果表明:同化多普勒雷达径向风观测资料后,对台风在模式中的初始位置进行了很好的修正,同时对台风区的动力和热力结构均有较好的调整。两组同化试验对于台风的路径、强度、降水等预报要优于控制试验,并且对背景误差协方差尺度化因子优化调整可以更有效地吸收雷达观测资料并提供更多的中小尺度信息。 相似文献
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建立了一个功能较为完善的四维同化系统,解决了温度调查资料中存在的同步性问题。为了检验同化方法和模式程序的正确性,以及讨论同步性误差的校正效果,从同化结果与锚系调查数据、同化结果与遥感数据、比对断面均方根误差和海表平均温度4个方面进行了探讨,结果表明:1)同化结果与锚系调查数据对比显示,改进参数的NMC方法(来自美国国家气象中心)能够适用于长江口外海域中的温度四维同化,同化温度结果与锚系调查数据在变化趋势上一致,在数值上最大差异不超过0.4℃;2)同化结果与遥感数据对比显示,同化结果能够将调查资料推演到完整的时空范围,并具有较好的变化趋势和精度;3)根据比对断面均方根误差分析,调查资料中两个比对断面温度间的均方差误差为3.8℃,而同化结果与实测数据的均方根误差低于0.6℃,说明同化方法有效地降低了调查资料中的同步性误差;4)海表平均温度分析显示,同化结果能够避免调查不同步引起的各种温度斑块,并且其温度分布和锋面结构能够更如实地反映实际情况。 相似文献
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The Cycling Representer Method, which is a technique for solving 4D-variational data assimilation problems, has been demonstrated to improve the assimilation accuracy with simpler nonlinear models. In this paper, the Cycling Representer Method will be used to assimilate an array of ADCP velocity observations with the Navy Coastal Ocean Model (NCOM). Experiments are performed in a high-resolution Mississippi Bight domain for the entire month of June, 2004 and demonstrate the usefulness of this assimilation technique in a realistic application.The Representer Method is solved by minimizing a cost function containing the weighted squared errors of velocity measurements, initial conditions, boundary conditions, and model dynamics. NCOM, however, is a highly nonlinear model and in order to converge towards the global minimum of this cost function, NCOM is linearized about a background state using tangent linearization. The stability of this tangent linearized model (TLM) is a very sensitive function of the background state, the level of nonlinearity of the model, open boundary conditions, and the complexity of the bathymetry and flow field. For the Mississippi Bight domain, the TLM is stable for only about a day. Due to this short TLM stability time period, the Representer Method is cycled by splitting the time period of the assimilation problem into short intervals. The interval time period needs to be such that it is short enough for the TLM to be stable, but long enough to minimize the loss of information due to reducing the temporal correlation of the dynamics and data. For each new cycle, a background is created as a nonlinear forecast from the previous cycle’s assimilated solution. This background, along with the data that falls within this new cycle, is then used to calculate a new assimilated solution. The experiments presented in this paper demonstrate the improvement of the assimilated solution as the time window of the cycles is reduced to 1 day. The 1-day cycling, however, was only optimal for the first half of the experiment. This is because there was a strong wind event near the middle of June that significantly reduced the stability of the 1-day cycling and caused substantial errors in the assimilation. Therefore, the 12-h cycling worked best for the second half of the experiment. This paper also demonstrates that the forecast skill is improved as the assimilation system progresses through the cycles. 相似文献
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通过数值试验验证卫星高度计波高数据同化对西北太平洋3 d海浪预报的改进效果。驱动海浪模式的强迫场采用国家海洋环境预报中心基于MM5模式预报的风场,波高数据同化使用的观测数据是Jason-1卫星高度计有效波高。用最优插值数据同化方法获得海浪有效波高的最优估计并重构相应的海浪方向谱,以此为初始场进行为期3 d的数值预报试验。与没有同化的预报进行了比较和分析,结果表明卫星高度计海浪数据同化对0~72 h预报有不同程度的明显改善,改进程度随预报时效的增加而减少。 相似文献
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在悬沙输运的数值模拟中,初始场的准确给定至关重要。目前诸多确定初始场的方案均存在一定的缺陷,初始场的准确性有待进一步提高。本文基于一个三维悬沙输运伴随同化模型,通过孪生实验和实际实验,对模型初始场进行了伴随法反演研究。在孪生实验中,首先验证了初始场的相对重要性;其次,探讨了初始场的反演结果对优化算法、初始猜测值、卫星遥感数据数量、同化时间窗口宽度和背景流场误差的敏感性;最后,比较了伴随法和插值法重构初始场的能力。孪生实验结果表明:最速下降法对初始场的优化反演效果要优于三种共轭梯度法和有限记忆BFGS法;初始场的反演效果对初始猜测值、卫星遥感数据数量和背景流场误差不敏感,而对同化窗口宽度较为敏感;与插值法相比,伴随法是重构模型初始场更有效的手段。实际实验中,在杭州湾海域同化典型的小潮时期和大潮时期的GOCI卫星遥感资料所得表层悬沙浓度数据,优化反演了初始场。实际实验结果表明:数据同化后,得到了更符合实际的最优初始场,表明伴随法是实现初始场优化反演的有效手段。该研究对进一步改进悬沙输运模型的初始化方案具有一定的参考价值,也对其他数值模型的初始化方案具有一定的借鉴价值。 相似文献
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This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the “truth” and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2–4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method. 相似文献
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《Ocean Modelling》2011,38(3-4):85-111
We assess and compare four sequential data assimilation methods developed for HYCOM in an identical twin experiment framework. The methods considered are Multi-variate Optimal Interpolation (MVOI), Ensemble Optimal Interpolation (EnOI), the fixed basis version of the Singular Evolutive Extended Kalman Filter (SEEK) and the Ensemble Reduced Order Information Filter (EnROIF). All methods can be classified as statistical interpolation but differ mainly in how the forecast error covariances are modeled. Surface elevation and temperature data sampled from an 1/12° Gulf of Mexico HYCOM simulation designated as the truth are assimilated into an identical model starting from an erroneous initial state, and convergence of assimilative runs towards the truth is tracked. Sensitivity experiments are first performed to evaluate the impact of practical implementation choices such as the state vector structure, initialization procedures, correlation scales, covariance rank and details of handling multivariate datasets, and to identify an effective configuration for each assimilation method. The performance of the methods are then compared by examining the relative convergence of the assimilative runs towards the truth. All four methods show good skill and are able to enhance consistency between the assimilative and truth runs in both observed and unobserved model variables. Prediction errors in observed variables are typically less than the errors specified for the observations, and the differences between the assimilated products are small compared to the observation errors. For unobserved variables, RMS errors are reduced by 50% relative to a non-assimilative run and differ between schemes on average by about 5%. Dynamical consistency between the updated state space variables in the data assimilation algorithm, and the data adequately sampling significant dynamical features are the two crucial components for reliable predictions. The experiments presented here suggest that practical implementation details can have at least as much an impact on the accuracy of the assimilated product as the choice of assimilation technique itself. We also present a discussion of the numerical implementation and the computational requirements for the use of these methods in large scale applications. 相似文献
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Kalman滤波风暴潮数值预报四维同化模式研究进展 总被引：1，自引：1，他引：1
本文首先介绍了Kalman滤波在风暴潮数值预报中的应用，特别介绍了近年来国际上发展的一些在实际中可行的次优化Kalman滤波算法。并通过一个稳态Kalman滤波风暴潮数值预报模式的实例表明，使用资料同化可以明显改进风暴潮后报结果；资料同化能够提供更为合理的预报初始场，对风暴潮的短期预报有较明显的改进。一旦没有资料同化到模式中去，预报结果很快接近确定性模式。 相似文献
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Emanuele Di Lorenzo Andrew M. Moore Hernan G. Arango Bruce D. Cornuelle Arthur J. Miller Brian Powell Boon S. Chua Andrew F. Bennett 《Ocean Modelling》2007,16(3-4):160-187
We describe the development and preliminary application of the inverse Regional Ocean Modeling System (ROMS), a four dimensional variational (4DVAR) data assimilation system for high-resolution basin-wide and coastal oceanic flows. Inverse ROMS makes use of the recently developed perturbation tangent linear (TL), representer tangent linear (RP) and adjoint (AD) models to implement an indirect representer-based generalized inverse modeling system. This modeling framework is modular. The TL, RP and AD models are used as stand-alone sub-models within the Inverse Ocean Modeling (IOM) system described in [Chua, B.S., Bennett, A.F., 2001. An inverse ocean modeling system. Ocean Modell. 3, 137–165.]. The system allows the assimilation of a wide range of observation types and uses an iterative algorithm to solve nonlinear assimilation problems. The assimilation is performed either under the perfect model assumption (strong constraint) or by also allowing for errors in the model dynamics (weak constraints). For the weak constraint case the TL and RP models are modified to include additional forcing terms on the right hand side of the model equations. These terms are needed to account for errors in the model dynamics.Inverse ROMS is tested in a realistic 3D baroclinic upwelling system with complex bottom topography, characterized by strong mesoscale eddy variability. We assimilate synthetic data for upper ocean (0–450 m) temperatures and currents over a period of 10 days using both a high resolution and a spatially and temporally aliased sampling array. During the assimilation period the flow field undergoes substantial changes from the initial state. This allows the inverse solution to extract the dynamically active information from the synthetic observations and improve the trajectory of the model state beyond the assimilation window. Both the strong and weak constraint assimilation experiments show forecast skill greater than persistence and climatology during the 10–20 days after the last observation is assimilated.Further investigation in the functional form of the model error covariance and in the use of the representer tangent linear model may lead to improvement in the forecast skill. 相似文献
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风暴潮是一种复杂的对众多因素敏感又备受关注的海洋现象。本文基于协方差局地化的集合卡尔曼滤波方法（EnKF）,选择201810号台风“安比”登陆上海的风暴潮过程,首次将海洋站和FVCOM数值模拟的不同来源、不同误差信息、不同时空分辨率的风暴潮进行数据同化融合,获得了逐72 h的上海海域风暴潮的最优解,进行了同化结果评估验证,并给出了集合样本数和Schur半径设置范围。结果表明,实测计算和数值模拟的风暴增减水之间均方根误差为0.20 m,实测和同化计算的风暴增减水之间均方根误差为0.07 m,准确度提高了65%;独立观测和同化计算的风暴增减水均方根误差为0.09 m,集合离散度与均方根误差比值为0.90,同化效果较好且可信;同化后的风暴增减水能够较好地刻画双峰增水、台风眼增水、增水锋面等特征,对于风暴潮研究、数值模拟结果订正、海洋防灾减灾等有重要意义。 相似文献