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本文给出了一个基于Gauss-Markov卡尔曼滤波的电离层数据同化系统的初步构建和试验结果.我们选择中国及周边地区部分涉及电离层观测的台站(包括子午工程台站、中国地壳形变网和部分IGS台站)作为观测系统进行模拟试验,背景场利用IRI模式,观测值则由NeQuick模式计算得到.我们的同化结果表明,采用Kalman滤波算法,把部分斜TEC同化到背景模式当中,能够获得较好的同化结果,说明我们设计的算法可行、所选择的各种参数比较合理,采用Gauss-Markov假设进行短期预报也取得了较合理的结果.本项研究经过进一步的改进和完善,可以用来对中国地区的电离层进行现报和短期预报,一方面满足相关空间工程应用,另一方面可以提升现有观测系统的科学意义. 相似文献
3.
Tidal flow forecasting using reduced rank square root filters 总被引：1，自引：0，他引：1
M. Verlaan A. W. Heemink 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(5):349-368
The Kalman filter algorithm can be used for many data assimilation problems. For large systems, that arise from discretizing
partial differential equations, the standard algorithm has huge computational and storage requirements. This makes direct
use infeasible for many applications. In addition numerical difficulties may arise if due to finite precision computations
or approximations of the error covariance the requirement that the error covariance should be positive semi-definite is violated.
In this paper an approximation to the Kalman filter algorithm is suggested that solves these problems for many applications.
The algorithm is based on a reduced rank approximation of the error covariance using a square root factorization. The use
of the factorization ensures that the error covariance matrix remains positive semi-definite at all times, while the smaller
rank reduces the number of computations and storage requirements. The number of computations and storage required depend on
the problem at hand, but will typically be orders of magnitude smaller than for the full Kalman filter without significant
loss of accuracy.
The algorithm is applied to a model based on a linearized version of the two-dimensional shallow water equations for the prediction
of tides and storm surges.
For non-linear models the reduced rank square root algorithm can be extended in a similar way as the extended Kalman filter
approach. Moreover, by introducing a finite difference approximation to the Reduced Rank Square Root algorithm it is possible
to prevent the use of a tangent linear model for the propagation of the error covariance, which poses a large implementational
effort in case an extended kalman filter is used. 相似文献
4.
Verification of ARMA identification for modelling temporal correlations of GNSS observations using the ARMASA toolbox 总被引：1，自引：0，他引：1
The classical least-squares (LS) algorithm is widely applied in practice of processing observations from Global Satellite Navigation Systems (GNSS). However, this approach provides reliable estimates of unknown parameters and realistic accuracy measures only if both the functional and stochastic models are appropriately specified. One essential deficiency of the stochastic model implemented in many available GNSS software products consists in neglecting temporal correlations of GNSS observations. Analysing time series of observation residuals resulting from the LS evaluation, the temporal correlation behaviour of GNSS measurements can be efficiently described by means of socalled autoregressive moving average (ARMA) processes. For a given noise realisation, a well-fitting ARMA model can be automatically estimated and identified using the ARMASA toolbox available free of charge in MATLAB® Central.In the preliminary stage of applying the ARMASA toolbox to residual-based modelling of temporal correlations of GNSS observations, this paper presents an empirical performance analysis of the automatic ARMA estimation tool using a large amount of simulated noise time series with representative temporal correlation properties comparable to the GNSS residuals. The results show that the rate of unbiased model estimates increases with data length and decreases with model complexity. For large samples, more than 80% of the identified ARMA models are unbiased. Additionally, the model error representing the deviation between the true data-generating process and the model estimate converges rapidly to the associated asymptotical value for a sufficiently large sample size with respect to the correlation length. 相似文献
5.
Calibration is typically used for improving the predictability of mechanistic simulation models by adjusting a set of model parameters and fitting model predictions to observations. Calibration does not, however, account for or correct potential misspecifications in the model structure, limiting the accuracy of modeled predictions. This paper presents a new approach that addresses both parameter error and model structural error to improve the predictive capabilities of a model. The new approach simultaneously conducts a numeric search for model parameter estimation and a symbolic (regression) search to determine a function to correct misspecifications in model equations. It is based on an evolutionary computation approach that integrates genetic algorithm and genetic programming operators. While this new approach is designed generically and can be applied to a broad array of mechanistic models, it is demonstrated for an illustrative case study involving water quality modeling and prediction. Results based on extensive testing and evaluation, show that the new procedure performs consistently well in fitting a set of training data as well as predicting a set of validation data, and outperforms a calibration procedure and an empirical model fitting procedure. 相似文献
6.
Alex Apotsos Mark Buckley Guy Gelfenbaum Bruce Jaffe Deepak Vatvani 《Pure and Applied Geophysics》2011,168(11):2097-2119
Model predictions from a numerical model, Delft3D, based on the nonlinear shallow water equations are compared with analytical
results and laboratory observations from seven tsunami-like benchmark experiments, and with field observations from the 26
December 2004 Indian Ocean tsunami. The model accurately predicts the magnitude and timing of the measured water levels and
flow velocities, as well as the magnitude of the maximum inundation distance and run-up, for both breaking and non-breaking
waves. The shock-capturing numerical scheme employed describes well the total decrease in wave height due to breaking, but
does not reproduce the observed shoaling near the break point. The maximum water levels observed onshore near Kuala Meurisi,
Sumatra, following the 26 December 2004 tsunami are well predicted given the uncertainty in the model setup. The good agreement
between the model predictions and the analytical results and observations demonstrates that the numerical solution and wetting
and drying methods employed are appropriate for modeling tsunami inundation for breaking and non-breaking long waves. Extension
of the model to include sediment transport may be appropriate for long, non-breaking tsunami waves. Using available sediment
transport formulations, the sediment deposit thickness at Kuala Meurisi is predicted generally within a factor of 2. 相似文献
7.
Multilayer shallow water flow using lattice Boltzmann method with high performance computing 总被引：1，自引：0，他引：1
A multilayer lattice Boltzmann (LB) model is introduced to solve three-dimensional wind-driven shallow water flow problems. The multilayer LB model avoids the expensive Navier–Stokes equations and obtains stratified horizontal flow velocities as vertical velocities are relatively small and the flow is still within the shallow water regime. A single relaxation time BGK method is used to solve each layer coupled by the vertical viscosity forcing term. To increase solution stability, an implicit step is suggested to obtain flow velocities. The main advantage of using the LBM is that after selecting appropriate equilibrium distribution functions, the LB algorithm is only slightly modified for each layer and retains all the simplicities of the LBM within the high performance computing (HPC) environment. The performance of the parallel LB model for the multilayer shallow water equations is investigated on CPU-based HPC environments using OpenMP. We found that the explicit loop control with cache optimization in LBM gives better performance on execution time, speedup and efficiency than the implicit loop control as the number of processors increases. Numerical examples are presented to verify the multilayer LB model against analytical solutions. We demonstrate the model’s capability of calculating lateral and vertical distributions of velocities for wind-driven circulation over non-uniform bathymetry. 相似文献
8.
While tomographic inversion has been successfully applied to laboratory- and field-scale tests, here we address the new issue of scale that arises when extending the method to a basin. Specifically, we apply the hydraulic tomography (HT) concept to jointly interpret four multiwell aquifer tests in a synthetic basin to illustrate the superiority of this approach to a more traditional Theis analysis of the same tests. Transmissivity and storativity are estimated for each element of a regional numerical model using the geostatistically based sequential successive linear estimator (SSLE) inverse solution method. We find that HT inversion is an effective strategy for incorporating data from potentially disparate aquifer tests into a basin-wide aquifer property estimate. The robustness of the SSLE algorithm is investigated by considering the effects of noisy observations, changing the variance of the true aquifer parameters, and supplying incorrect initial and boundary conditions to the inverse model. Ground water flow velocities and total confined storage are used as metrics to compare true and estimated parameter fields; they quantify the effectiveness of HT and SSLE compared to a Theis solution methodology. We discuss alternative software that can be used for implementing tomography inversion. 相似文献
9.
Ground shaking intensity varies spatially in earthquakes, and many studies have estimated correlations of intensity from past earthquake data. This paper presents a framework for quantifying uncertainty in the estimation of correlations and true variability in correlations from earthquake to earthquake. A procedure for evaluating estimation uncertainty is proposed and used to evaluate several methods that have been used in past studies to estimate correlations. The results indicate that a weighted least squares algorithm is most effective in estimating spatial correlation models and that earthquakes with at least 100 recordings are needed to produce informative earthquake-specific estimates of spatial correlations. The proposed procedure is also used to distinguish between estimation uncertainty and the true variability in model parameters that exist in a given data set. The estimation uncertainty is seen to vary between well-recorded and poorly recorded earthquakes, whereas the true variability is more stable. 相似文献
10.
Numerical modeling of free-surface flow over a mobile bed with predominantly bedload sediment transport can be done by solving the shallow water and Exner equations using coupled and splitting approaches.The coupled method uses a coupling of the governing equations at the same time step leading to a non-conservative solution.The splitting method solves the Exner and the shallow water equations in a separate manner,and is only capable of modeling weak free-surface and bedload interactions.In the current study,an extended version of a Godunov-type wave propagation algorithm is presented for modeling of morphodynamic systems using both coupled and splitting approaches.In the introduced coupled method the entire morphodynamic system is solved in the form of a conservation law.For the splitting technique,a new wave Riemann decomposition is defined which enables the scheme to be utilized for mild and strong interactions.To consider the bedload sediment discharge within the Exner equation,the Smart and Meyer-Peter&Müller formulae are used.It was found that the coupled solution gives accurate predictions for all investigated flow regimes including propagation over a dry-state using a Courant-Friedrichs-Lewy(CFL)number equal to 0.6.Furthermore,the splitting method was able to model all flow regimes with a lower CFL number of 0.3. 相似文献
11.
A combinatorial optimization scheme for parameter structure identification in ground water modeling 总被引：5，自引：0，他引：5
This research develops a methodology for parameter structure identification in ground water modeling. For a given set of observations, parameter structure identification seeks to identify the parameter dimension, its corresponding parameter pattern and values. Voronoi tessellation is used to parameterize the unknown distributed parameter into a number of zones. Accordingly, the parameter structure identification problem is equivalent to finding the number and locations as well as the values of the basis points associated with the Voronoi tessellation. A genetic algorithm (GA) is allied with a grid search method and a quasi-Newton algorithm to solve the inverse problem. GA is first used to search for the near-optimal parameter pattern and values. Next, a grid search method and a quasi-Newton algorithm iteratively improve the GA's estimates. Sensitivities of state variables to parameters are calculated by the sensitivity-equation method. MODFLOW and MT3DMS are employed to solve the coupled flow and transport model as well as the derived sensitivity equations. The optimal parameter dimension is determined using criteria based on parameter uncertainty and parameter structure discrimination. Numerical experiments are conducted to demonstrate the proposed methodology, in which the true transmissivity field is characterized by either a continuous distribution or a distribution that can be characterized by zones. We conclude that the optimized transmissivity zones capture the trend and distribution of the true transmissivity field. 相似文献
12.
MAST-2D diffusive model for flood prediction on domains with triangular Delaunay unstructured meshes
A new methodology for the solution of the 2D diffusive shallow water equations over Delaunay unstructured triangular meshes is presented. Before developing the new algorithm, the following question is addressed: it is worth developing and using a simplified shallow water model, when well established algorithms for the solution of the complete one do exist?The governing Partial Differential Equations are discretized using a procedure similar to the linear conforming Finite Element Galerkin scheme, with a different flux formulation and a special flux treatment that requires Delaunay triangulation but entire solution monotonicity. A simple mesh adjustment is suggested, that attains the Delaunay condition for all the triangle sides without changing the original nodes location and also maintains the internal boundaries. The original governing system is solved applying a fractional time step procedure, that solves consecutively a convective prediction system and a diffusive correction system. The non linear components of the problem are concentrated in the prediction step, while the correction step leads to the solution of a linear system of the order of the number of computational cells. A semi-analytical procedure is applied for the solution of the prediction step. The discretized formulation of the governing equations allows to handle also wetting and drying processes without any additional specific treatment. Local energy dissipations, mainly the effect of vertical walls and hydraulic jumps, can be easily included in the model.Several numerical experiments have been carried out in order to test (1) the stability of the proposed model with regard to the size of the Courant number and to the mesh irregularity, (2) its computational performance, (3) the convergence order by means of mesh refinement. The model results are also compared with the results obtained by a fully dynamic model. Finally, the application to a real field case with a Venturi channel is presented. 相似文献
13.
针对Mogi模型垂直位移与水平位移联合反演中的病态问题,改进火山形变总体最小二乘(Total Least Squares,TLS)联合反演的虚拟观测法,并使用方差分量估计(Variance Components Estimation,VCE)方法确定病态问题的正则化参数.将附有先验信息的参数作为观测方程,与垂直位移和水平位移的观测方程联合解算,推导了三类观测方程联合反演的求解公式及基于总体最小二乘方差分量估计确定正则化参数的表达式,给出了算法的迭代流程.通过算例实验,研究了总体最小二乘联合反演的虚拟观测法在火山Mogi模型形变反演中的应用;算例结果表明,三类数据的联合平差及方差分量估计方法可以确定权比因子并得到修正后的压力源参数,具有一定的实际参考价值. 相似文献
14.
In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother(DEn KS) and simple biosphere model(Si B2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter(En KF), ensemble Kalman smoother(En KS), and dual En KF(DEn KF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as En KF and En KS. The estimation accuracy of the model parameters decreased with increasing error sources. DEn KS outperformed DEn KF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEn KS approach is a useful and practical way to improve soil moisture estimation. 相似文献
15.
The system of normal equations associated with the discrete Wiener filter is sometimes ill-conditioned. The purpose of this paper is to show that in such cases the solutions obtained vary drastically with the particular choice of an algorithm and of the computer used for its implementation. A review of the basic mathematical theory behind an ill-conditioned matrix is first presented. Numerical examples are then given to illustrate that the solutions of the normal equations are sensitive to the word length of a given computer. Finally, two possible remedies are described: (1) The well-known method of prewhitening and (2) the use of the conjugate-gradient algorithm for solving the normal equations. 相似文献
16.
This study proposes an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem fuzzy c-means (FCM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through an optimization model. The meta-heuristic harmony search (HS) algorithm, which is conceptualized using the musical process of searching for a perfect state of harmony, is used as an optimization technique. The optimum parameter zone structure is identified based on three criteria which are the residual error, parameter uncertainty, and structure discrimination. A numerical example given in the literature is solved to demonstrate the performance of the proposed algorithm. Also, a sensitivity analysis is performed to test the performance of the HS algorithm for different sets of solution parameters. Results indicate that the proposed solution algorithm is an effective way in the simultaneous identification of aquifer parameters and their corresponding zone structures. 相似文献
17.
P. G. J. ten Brummelhuis A. W. Heemink 《Stochastic Environmental Research and Risk Assessment (SERRA)》1990,4(3):193-208
In this paper a parameter estimation algorithm is developed to estimate uncertain parameters in two dimensional shallow water flow models. Since in practice the open boundary conditions of these models are usually not known accurately, the uncertainty of these boundary conditions has to be taken into account to prevent that boundary errors are interpreted by the estimation procedure as parameter fluctuations. Therefore the open boundary conditions are embedded into a stochastic environment and a constant gain extended Kalman filter is employed to identify the state of the system. Defining a error functional that measures the differences between the filtered state of the system and the measurements, a quasi Newton method is employed to determine the minimum of this functional. To reduce the computational burden, the gradient of the criterium that is required using the quasi Newton method is determined by solving the adjoint system. 相似文献
18.
A. H. ELSheikh C. C. Pain F. Fang J. L. M. A. Gomes I. M. Navon 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(4):877-897
A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian process regression is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative regularized EnKF algorithm. The filter is converted to an optimization algorithm by using a pseudo time-stepping technique such that the model output matches the time dependent data. The EnKF Kalman gain matrix is regularized using truncated SVD to filter out noisy correlations. Numerical results show that the proposed algorithm is a promising approach for parameter estimation of subsurface flow models. 相似文献
19.
A finite difference implicit scheme is presented in this paper for solution of the shallow water equations in one dimensional (1D) form. The present model has many advantages like, handling of discontinuous and complex bed topography, satisfying C-property (preservation of motionless water surface over a wet or dry bed) and capability of handling large value of temporal step etc. Another very important feature of the present model is that, no special treatment of the source vector of the governing equations is required here to deal with very less water depth. To investigate the performance of the present model in diverse situations, it is used to replicate four different problems of known analytical solution, and the model is found to be quite capable for varied situations. 相似文献
20.
1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach 
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下载免费PDF全文 Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step. 相似文献