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1.
背景误差相关结构的确定是影响海浪同化效果的关键因素之一。集合Kalman滤波是一种较为成熟的同化方法,其可以对背景误差进行实时更新和动态估计,现已广泛应用于海洋和大气领域的研究。本文基于MASNUM-WAM海浪模式,分别采用静态样本集合Kalman滤波和EAKF方法,针对2014年全球海域开展海浪数据同化实验,同化资料为Jason-2卫星高度计数据,利用Saral卫星高度计资料对同化实验结果进行检验。结果表明,两组同化方案均有效提高了海浪模式的模拟水平,EAKF方案在风场变化较大的西风带区域表现显著优于静态样本集合Kalman滤波方案,但总体上两者相差不大。综合考虑计算成本和同化效果,静态样本集合Kalman滤波方案更适用于海浪业务化预报。 相似文献
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针对水下目标跟踪非线性跟踪精度问题,假设目标机动模型为恒转速运动模型,贝叶斯框架下,因扩展卡尔曼滤波跟踪方法进行模型在估计点的泰勒展开,忽略一阶以上高阶项,存在模型误差,比较了扩展卡尔曼滤波、无迹卡尔曼滤波、容积卡尔曼滤波在高斯噪声干扰下滤波误差均方根,以及3种方法运行时间。仿真证明,非线性系统下状态维度为5,容积卡尔曼滤波跟踪的精度高于无迹卡尔曼滤波,无迹卡尔曼滤波高于扩展卡尔曼滤波。该研究为海上目标非线性测量系统提供仿真实例,为进一步滤波算法改进提供基础。 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
4.
在集合数据同化中,协方差局地化（covariance localization,CL）方法的使用存在限制。集合转换卡尔曼滤波（ensemble transform Kalman filter,ETKF）作为集合平方根滤波的变种方法,是一种应用较广、计算高效的数据同化方法。本文分析了CL方法应用于ETKF方法的困难,从而改进CL方法使其可以适用于ETKF方法。另外,结合浅水方程,利用Askey函数作为多元局地化函数,提出了一种适用于多元数值模型的CL方法。通过具体实验验证,得到了较好的分析结果。 相似文献
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Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters. 相似文献
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Different data assimilation methods such as an extended Kalman filter, the optimal interpolation method, and a method based on the Fokker-Planck equation applications are considered. Data from the ARGO drifters are assimilated into the HYCOM shallow water model (University of Miami, USA). Throughout the study, the schemes and methods of parallel computations with an MPI library are used. The results of the computations with assimilations are compared between themselves and with independent observations. The method based on the Fokker-Planck equation and the extended Kalman filter are preferable because they give better results than the optimal interpolation scheme. The various model characteristics of the ocean, such as the heat content fields and others, are analyzed after the data assimilation. 相似文献
7.
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
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粒子滤波器(PF)是一种非常具有应用前景的非线性资料同化方法。但由于其算法本身存在的粒子退化问题,目前尚未被广泛地应用于大型地球物理模式。目前主流的集合同化系统仍然倾向于使用集合卡尔曼滤波器(EnKF)及其衍生方法。一种新近被提出的局地化粒子滤波器(LPF)在经典的粒子滤波器算法中引入局地化技术,可以使用较小的计算成本有效地避免退化问题,具有非常大的业务应用潜力。本文在全耦合的通用地球系统模式中开展了LPF和EnKF的同化实验,同化资料为模拟的卫星海表温度资料。着重考察了不同局地化参数对两种方法的不同影响,对比了局地化粒子滤波器与集合卡尔曼滤波器的同化效果差异。比较的结果表明,LPF的同化效果对于局地化参数的选择非常敏感,在使用最优局地化参数的条件下,LPF能达到与EnKF相当甚至优于后者的同化效果,并具有较大的改进空间。 相似文献
9.
This paper proposes a simple approach to estimating multiplicative model parameters using the ensemble square root filter. The basic idea, following previous studies, is to augment the state vector by the model parameters. While some success with this approach has been reported if the model parameters enter as additive terms in the tendency equations, this approach is problematic if the model parameters are multiplied by the state variables. The reason for this difficulty is that multiplicative parameters change the dynamical properties of the model, and in particular can cause the model to become dynamically unstable. This paper shows that model instability can be avoided if the usual persistence model for parameter update is replaced by a temporally smoothed version of the update model. In addition, the augmentation approach can be interpreted as two simultaneously decoupled ensemble Kalman filters for the model state and the parameter state, respectively. Implementation of the parameter estimation does not require changing the data assimilation algorithm—it just has to be supplemented by a parameter estimation step that is similar to the state estimation step. Covariance localization is found to be necessary not only for the model state, but also for augmented model parameters, if they are spatially dependent. The new formulation is illustrated with the Lorenz-96 model and shown to be capable of estimating additive and multiplicative model parameters, as well as the state, under relatively challenging conditions (e.g. using 20 observations to estimate 120 unknown variables). 相似文献
10.
EnKF和SIR-PF在贝叶斯滤波框架下的比较和结合 总被引：3，自引：0，他引：3
贝叶斯估计理论为非线性、非高斯系统的数据同化提供了一个统一的框架。在本文中,我们利用著名的洛伦茨吸引子(Lorenz'63)模式对两种基于贝叶斯滤波理论的数据同化方法——集合卡尔曼滤波器(EnKF)和重取样粒子滤波器(SIR-PF)——进行了较为全面的比较。比较的结果揭示了两种方法的优缺点:即当集合成员数目较多时,SIR-PF的同化效果优于EnKF;反之,则EnKF的表现较好。进一步地,我们使用统计方法分析了两者表现的差异和原因。最近提出的一种集合卡尔曼粒子滤波器(EnKPF)通过使用一个可控的参数整合EnKF和SIR-PF的分析格式,可以结合两者的优点。本文在充分比较两种方法的前提下,重新阐释并改进了原有的EnKPF算法,使之适用于非线性的观测算子。通过使用相同的洛伦茨模式实验,我们揭示了EnKPF实质上提供了关于EnKF和SIR-PF的连续插值,使得后两者可以视为其特殊情况。并且,在集合成员数目有限的前提下,EnKPF可以在一定程度上避免滤波退化的发生,取得优于EnKF和SIR-PF的同化效果。 相似文献
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This study proposes a method for identification of the nonlinear dynamic model of an AUV while some states are unmeasured; hence, it concentrates on a nonlinear “state and parameter estimation” issue. In this method, a local linearization is used for solving the nonlinear dynamics based on the extended Kalman filter (EKF), and a particle filter (PF) is used to minimize errors and variances of the nonlinear system. In other words, the PF is combined with the EKF in the form of the extended Kalman particle filter (EKPF). The EKPF method is independent of the initial values and satisfies the limits of the parameters and also the assumption that the hydrodynamic coefficients are constant. Hence, it is shown when the ranges or signs of some parameters are known, the EKPF is a more accurate estimator than the EKF. Moreover, a new simulation is done using the model estimated by the EKPF and the results are compared and validated with the measured data of a new experimental test. It is shown that the obtained model can predict the trajectory path with the total normalized root-mean-square error (NRMSE) of 14% and the surge mean speed with the NRMSE of 5%; and it describes the 6DOF motion of the AUV more accurate than the EKF model. 相似文献
13.
The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS. 相似文献
14.
《Oceanic Engineering, IEEE Journal of》2009,34(4):586-602
15.
利用资料同化方法优化观测系统的空间布局:以泰国湾为例 总被引：1，自引：0，他引：1
海洋观测费用高昂，设计科学高效的观测系统可以充分发挥观测的效能。本文以泰国湾高频地波雷达观测系统为例，利用数据同化方法对观测系统进行了最优布局。首先基于FVCOM海洋数值模式建立了泰国湾海域高分辨率三维斜压水动力模式，在此基础上利用一种改进的高效集合卡曼滤波同化方法对岸基高频地波雷达表层海流观测系统开展观测效能评估数值实验。通过观测区域的不同组合方式将3个区域的雷达表层海流数据同化到数值模式中，实验结果表明，岸基高频地波雷达表层海流观测系统可有效降低高分辨数值模式的海流模拟误差。但不同观测区域的组合提供的观测数据对改善海流模拟的作用存在明显差别，泰国湾现有观测系统雷达站位布设方式应进一步优化。本文最后给出了研究区域最优观测站位的布局方案，可作为下一步观测系统进行布局调整的指导。 相似文献
16.
基于适应性观测误差和温盐偏差控制的EnKF温盐廓线资料同化试验 总被引：1，自引：0，他引：1
通过开展2008年夏季南海北部开放航次CTD的温盐廓线数据资料同化试验,本文采取了观测误差适应的方法来防止EnKF滤波发散问题;同时,从背景误差协方差和温盐模式偏差关系入手,在同化中引入温盐控制来减小模式偏差对同化结果的影响。对于改进的同化方案进行了试验验证,并用卫星高度计观测数据,OSCAR流速数据,走航ADCP数据作为独立观测数据检验。结果证明新的EnKF同化策略能够有效地减小温盐均方根误差。同时整个同化系统能有效地改善高度场和流场的模拟。 相似文献
17.
一个稳态Kalman滤波风暴潮数值预报模式 总被引：4，自引：1，他引：4
利用Kalman滤波资料同化技术将海洋站水位观测资料融入二维线性风暴潮模式中,研制具有资料同化能力的风暴潮预报模式,改进风暴潮模式计算结果.通过在风暴潮模式的动量方程中加入模式噪声项来修正模式本身和气象强迫力的不确定性.确定性模式的输出通过带有观测噪声的观测方程与可利用的海洋站的潮位观测资料联系起来.假定初始的模式噪声和观测噪声满足均值为0的高斯分布,用迭代法得到计算区域的状态向量的稳态Kalman滤波,进而得到风暴潮模式输出的最优线性校正结果.利用这种资料同化技术,对1956年发生在东海的一次强风暴潮过程进行了后报试验,结果表明,该同化方法对短期风暴潮水位后(预)报有一定的改进. 相似文献
18.
ImUcrIONThe deterministic storm stirge nurnrical fOrecast Tnedel has played an imPOrtant role inroutine storm surge real-time fOrecast. But somtimes the error of forecast is still large by usingdeterministic medels (Je1esnianshi et al., l992). The source of these errors mainly comesfrom (1 ) errors of wind stress and medel's open boundary, (2) non--optimized medel param-eter, (3) error of model equations, (4) error of medel's numrical methed, etc. The effec-ti ve methed to solve this probl… 相似文献
19.
A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared. 相似文献
20.
现代海洋/大气资料同化方法的统一性及其应用进展 总被引：9，自引：3，他引：9
海洋／大气资料同化的理论基础是用数值模式作为动力学强迫对观测信息进行提炼，或者说，从包含观测误差(噪声)的空间分布不均匀的实测资料中依据动力系统自身的演化规律(动力学方程或模式)来确定海洋／大气系统状态的最优估计。本文对主要的现代海洋／大气资料同化方法，包括最优插值(()ptimal Interpolation，简称()Ⅰ)、变分方法(3—Dimensional Variational和4—Dimensional Variational，分别简称3DVAR和4DVAR)和滤波方法(Filtering)的原理、算法设计和实际应用进行系统地回顾，并对这些资料同化方法的优缺点进行分析和讨论。在滤波框架下，所有的现代资料同化方法都被统一了：()Ⅰ和3DVAR是不随时间变化的滤波器，4DVAR和卡曼滤波是线性滤波器，即非线性滤波的退化情形；而集合滤波能构建非线性的滤波器，因为集合在某种程度上体现了系统的非高斯信息。一个非线性滤波器的主要优点是能计算和应用随时间变化的各阶误差统计距，如误差协方差矩阵。将非线性滤波器计算的随时间变化的误差协方差矩阵引入到（）Ⅰ或4DVAR中，也许能实质性地改进这些传统方法。在实际应用中，方法的优劣可能取决于所选用的数值模式和可获得的计算资源，因此需针对不同的问题选取不同的资料同化方法。由于各种资料同化方法具有统一性，因此可建立测试系统来评价这些方法，从而对各种方法获得更深入的理解，改进现有的资料同化技术，并提高人们对海洋／大气环境的预测能力。 相似文献