Reduced rank static error covariance for high‐dimensional applications

A method for creating static (e.g., stationary) error covariance of reduced rank for potential use in hybrid variational‐ensemble data assimilation is presented. The choice of reduced rank versus full rank static error covariance is made in order to allow the use of an improved Hessian preconditioning in high‐dimensional applications. In particular, this method relies on using block circulant matrices to create a high‐dimensional global covariance matrix from a low‐dimensional local sub‐matrix. Although any covariance used in variational data assimilation would be an acceptable choice for the pre‐defined full‐rank static error covariance, for convenience and simplicity, we use a symmetric Topelitz matrix as a prototype of static error covariance. The methodology creates a square root covariance, which has a practical advantage for Hessian preconditioning in reduced rank, ensemble‐based data assimilation. The experiments conducted examine multivariate covariance that includes the impact of cross‐variable correlations, in order to have a more realistic assessment of the value of the constructed static error covariance approximation. The results show that it may be possible to reduce the rank of matrix to O(10) and still obtain an acceptable approximation of the full‐rank static covariance matrix. Copyright © 2016 John Wiley & Sons, Ltd.     

Data assimilation of forecasted errors in hydrodynamic models using inter‐model correlations

Data‐assimilation techniques of the Kalman filter type are considered to be the state‐of‐the‐art approach for combining data information and deterministic numerical models with the objective of operational forecasting. This paper introduces, as an alternative, a faster and simpler data‐assimilation technique that exploits inter‐model correlations to distribute predicted errors. This scheme is performed in two steps: (i) prediction of the deterministic model errors at observation points using so‐called local linear models and (ii) distribution of the forecasted errors over the computational domain employing a scheme based on deterministic inter‐model correlations which describe the spatial nature of error structure. The method's advantage is that systematic error can be predicted by the error correction scheme, while the dynamics remain described by the deterministic model, which also establishes a basis for the spatial error distribution scheme. This relatively simple approach is inspired by original Kalman filter techniques but distinguishes error prediction and distribution in two different stages, hence allowing for data‐driven error forecasting and off‐line correction. In order to test the scheme's performance, a deterministic model of an artificial bay was constructed and run. The system was driven by specific forcing conditions and characterized by physical parameters that, in subsequent simulations, were deliberately manipulated to introduce errors into the model and test the scheme's capability. Copyright © 2007 John Wiley & Sons, Ltd.     

A dual‐weighted trust‐region adaptive POD 4‐D Var applied to a finite‐volume shallow water equations model on the sphere

In this paper we study solutions of an inverse problem for a global shallow water model controlling its initial conditions specified from the 40‐yr ECMWF Re‐analysis (ERA‐40) data sets, in the presence of full or incomplete observations being assimilated in a time interval (window of assimilation) with or without background error covariance terms. As an extension of the work by Chen et al. (Int. J. Numer. Meth. Fluids 2009), we attempt to obtain a reduced order model of the above inverse problem, based on proper orthogonal decomposition (POD), referred to as POD 4D‐Var for a finite volume global shallow water equation model based on the Lin–Rood flux‐form semi‐Lagrangian semi‐implicit time integration scheme. Different approaches of POD implementation for the reduced inverse problem are compared, including a dual‐weighted method for snapshot selection coupled with a trust‐region POD adaptivity approach. Numerical results with various observational densities and background error covariance operator are also presented. The POD 4‐D Var model results combined with the trust‐region adaptivity exhibit similarity in terms of various error metrics to the full 4D Var results, but are obtained using a significantly lesser number of minimization iterations and require lesser CPU time. Based on our previous and current work, we conclude that POD 4‐D Var certainly warrants further studies, with promising potential of its extension to operational 3‐D numerical weather prediction models. Copyright © 2011 John Wiley & Sons, Ltd.     

4D variational data assimilation for locally nested models: Complementary theoretical aspects and application to a 2D shallow water model

We consider the application of a four‐dimensional variational data assimilation method to a numerical model, which employs local mesh refinement to improve its solution. We focus on structured meshes where a high‐resolution grid is embedded in a coarser resolution one, which covers the entire domain. The formulation of the nested variational data assimilation algorithm was derived in a preliminary work (Int. J. Numer. Meth. Fluids 2008; under review). We are interested here in complementary theoretical aspects. We present first a model for the multi‐grid background error covariance matrix. Then, we propose a variant of our algorithms based on the addition of control variables in the inter‐grid transfers in order to allow for a reduction of the errors linked to the interactions between the grids. These formulations are illustrated and discussed in the test case experiment of a 2D shallow water model. Copyright © 2010 John Wiley & Sons, Ltd.     

Adjoint Methods in Data Assimilation for Estimating Model Error

Data assimilation aims to incorporate measured observations into a dynamical system model in order to produce accurate estimates of all the current (and future) state variables of the system. The optimal estimates minimize a variational principle and can be found using adjoint methods. The model equations are treated as strong constraints on the problem. In reality, the model does not represent the system behaviour exactly and errors arise due to lack of resolution and inaccuracies in physical parameters, boundary conditions and forcing terms. A technique for estimating systematic and time-correlated errors as part of the variational assimilation procedure is described here. The modified method determines a correction term that compensates for model error and leads to improved predictions of the system states. The technique is illustrated in two test cases. Applications to the 1-D nonlinear shallow water equations demonstrate the effectiveness of the new procedure. This revised version was published online in July 2006 with corrections to the Cover Date.     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

MIXED FINITE ELEMENT METHODS FOR THE SHALLOW WATER EQUATIONS INCLUDING CURRENT AND SILT SEDIMENTATION (Ⅰ)-THE CONTINUOUS-TIME CASE

An initial-boundary value problem for shallow equation system consisting of water dynamics equations, silt transport equation, the equation of bottom topography change, and of some boundary and initial conditions is studied, the existence of its generalized solution and semidiscrete mixed finite element (MFE) solution was discussed, and the error estimates of the semidiscrete MFE solution was derived. The error estimates are optimal.     

MIXED FINITE ELEMENT METHODS FOR THE SHALLOW WATER EQUATIONS INCLUDING CURRENT AND SILT SEDIMENTA-TION (Ⅰ)-THE CONTINUOUS-TIME CASE

IntroductionTheshallowwaterequationsareanimportantmathematicalmodelforavarietyofprobleminhydraulicengineering .Inrecentyears,therehasbeeninterestinthenumericalsolutionfortheshallowwaterequations.Thenumericalsimulationsfortheshallowwaterequationsystemcanbeappliedtomanypurposes .First,itcanserveasameansformodelingtidalfluctuationsforthosenterestedincapturingtidalenergyforcommercialpurposes.Secondly ,thesesimulationscanbeusedtocomputetidalrangesandsurgessuchashurricanesandtsunamiscausedbyextreme…     

Optimal error bound in a Sobolev space of regularized approximation solutions for a sideways parabolic equation

The inverse heat conduction problem （IHCP） is a severely ill-posed problem in the sense that the solution （ if it exists） does not depend continuously on the data. But now the results on inverse heat conduction problem are mainly devoted to the standard inverse heat conduction problem. Some optimal error bounds in a Sobolev space of regularized approximation solutions for a sideways parabolic equation, i. e. , a non-standard inverse heat conduction problem with convection term which appears in some applied subject are given.     

Data assimilation of local model error forecasts in a deterministic model

One of the most popular data assimilation techniques in use today are of the Kalman filter type, which provide an improved estimate of the state of a system up to the current time level, based on actual measurements. From a forecasting viewpoint, this corresponds to an updating of the initial conditions. The standard forecasting procedure is to then run the model uncorrected into the future, driven by predicted boundary and forcing conditions. The problem with this methodology is that the updated initial conditions quickly ‘wash‐out’, thus, after a certain forecast horizon the model predictions are no better than from an initially uncorrected model. This study demonstrates that through the assimilation of error forecasts (in the present case made using so‐called local models) entire model domains can be corrected for extended forecast horizons (i.e. long after updated initial conditions have become washed‐out), thus demonstrating significant improvements over the conventional methodology. Some alternate uses of local models are also explored for the re‐distribution of error forecasts over the entire model domain, which are then compared with more conventional Kalman filter type schemes. Copyright © 2002 John Wiley & Sons, Ltd.     

Treatment of interface problems with Godunov-type schemes

A correction is proposed of Godunov-type schemes, yielding a perfect capture of contact discontinuities in hydrodynamic flows. The correction method is based upon the following simple idea: If an Euler scheme is employed starting from a non-degraded solution at a certain instant of time, the presence of a discontinuity will entail, at the next instant, the degradation of the solution at the two points adjacent to the discontinuity only. On the other hand, an exact solution of the Riemann problem yields the state variables at the nodes affected by numerical diffusion can be corrected. The method is applied to problems involving a gas-liquid interface. The liquid is supposed to be compressible, obeying an equation of state of the “Stiffened Gas” type, for which a solution to Riemann's problem is readily obtained.     

Staggering error control of a class of inelastic processes in random microheterogeneous solids

In this work a staggering solution strategy for the simulation of the time-dependent inelastic mechanical deformation of a class of solids, possessing irregular heterogeneous microstructure, is developed. The system of coupled equations involved consists of (1) a dynamic equation of momentum balance, where the primary field variable is the displacement, (2) an evolution equation for material degradation, where the primary field variable is a state damage function, and (3) an evolution equation for the inelastic strains in the solid where the primary field variable is a plastic strain field. Clearly, the damage and plasticity variables are implicit functions of the displacement, however, for the staggering scheme strategy, it is convenient to formulate them as individual fields during the solution process. The key concept for the strategy to operate efficiently is to estimate and control the so-called staggering error, i.e. the error due to incompletely resolving the coupling between the field equations in a staggering process. This error is a function of the time step size. However, because the coupling is temporally variable, possibly becoming stronger, weaker, or oscillatory, it is extremely difficult to ascertain a priori the time step size needed for prespecified error control. In the present work, to induce desired staggering rates of convergence within each time step, thus controlling the staggering error, an adaptive strategy is developed whereby the time step size is manipulated, enlarged or reduced, to control the intrinsic contraction mapping constant of the staggering system operator. The overall goal is to deliver accurate solutions where temporal discretization error control dictates the upper limits on the time step size, while the iterative staggering strategy refines the step size further to control the staggering error. Three-dimensional numerical experiments are performed to illustrate the solution strategy.     

A NECESSARY AND SUFFICIENT FORMULATION FOR TORSION PROBLEMS OF ANISOTROPIC BODIES

Non-dimensionalized equations and boundary conditions are presentedfor the torsion problem of an anisotropic body.The error of the fundamental solutioncited in some boundary element books is pointed out after an examination of thefundamental solution.Furthermore,a necessary and sufficient boundary integralequation is given for the problem and compared with the conventional boundaryintegral equation.Numerical results show that great errors of the boundary shearstresses obtained by the conventional boundary integral equation appear with a smallerror of torsion stiffness.Meanwhile,the necessary and sufficient boundary integralequation always gives accurate results.     

旋转加速度计重力梯度仪误差分析

安装误差和加速度计标度系数的不匹配对旋转加速度计重力梯度仪的分辨率影响很大。为此,用线性微扰方法对旋转加速度计重力梯度仪的测量方程求取变分,得到重力梯度仪的误差方程;对误差方程进行分析,得到安装误差各项对重力梯度仪输出的影响;同时,给出含有加速度计性能参数的重力梯度仪的误差方程。以澳大利亚FALCON旋转加速度计重力梯度仪为例,具体给出径向距离误差、切向误差角等大小值。     

Numerical control of two‐dimensional shock waves in dual solution domain by instant temperature disturbances

The search for the temperature disturbance causing transition between regular and Mach reflections in the dual solution domain is addressed in an optimization statement. The gradient of the discrepancy between the current and target flow fields was calculated using adjoint equations. The control was determined by gradient‐based optimization. The flow field simulation is verified via a posteriori error estimates using the solution of an additional adjoint problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal control of attitude for coupled-rigid-body spacecraft via Chebyshev-Gauss pseudospectral method

The attitude optimal control problem(OCP) of a two-rigid-body spacecraft with two rigid bodies coupled by a ball-in-socket joint is considered. Based on conservation of angular momentum of the system without the external torque, a dynamic equation of three-dimensional attitude motion of the system is formulated. The attitude motion planning problem of the coupled-rigid-body spacecraft can be converted to a discrete nonlinear programming(NLP) problem using the Chebyshev-Gauss pseudospectral method(CGPM). Solutions of the NLP problem can be obtained using the sequential quadratic programming(SQP) algorithm. Since the collocation points of the CGPM are Chebyshev-Gauss(CG) points, the integration of cost function can be approximated by the Clenshaw-Curtis quadrature, and the corresponding quadrature weights can be calculated efficiently using the fast Fourier transform(FFT). To improve computational efficiency and numerical stability, the barycentric Lagrange interpolation is presented to substitute for the classic Lagrange interpolation in the approximation of state and control variables. Furthermore, numerical float errors of the state differential matrix and barycentric weights can be alleviated using trigonometric identity especially when the number of CG points is large. A simple yet efficient method is used to avoid sensitivity to the initial values for the SQP algorithm using a layered optimization strategy from a feasible solution to an optimal solution. Effectiveness of the proposed algorithm is perfect for attitude motion planning of a two-rigid-body spacecraft coupled by a ball-in-socket joint through numerical simulation.     

A reduced‐order simulated annealing approach for four‐dimensional variational data assimilation in meteorology and oceanography

Four‐dimensional variational data assimilation in meteorology and oceanography suffers from the presence of local minima in the cost function. These local minima arise when the system under study is strongly nonlinear. The number of local minima further dramatically increases with the length of the assimilation period and often renders the solution to the problem intractable. Global optimization methods are therefore needed to resolve this problem. However, the huge computational burden makes the application of these sophisticated techniques unfeasible for large variational data assimilation systems. In this study, a Simulated Annealing (SA) algorithm, complemented with an order‐reduction of the control vector, is used to tackle this problem. SA is a very powerful tool of combinatorial minimization in the presence of several local minima at the cost of increasing the execution time. Order‐reduction is then used to reduce the dimension of the search space in order to speed up the convergence rate of the SA algorithm. This is achieved through a proper orthogonal decomposition. The new approach was implemented with a realistic eddy‐permitting configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) of the tropical Pacific Ocean. Numerical results indicate that the reduced‐order SA approach was able to efficiently reduce the cost function with a reasonable number of function evaluations. Copyright © 2008 John Wiley & Sons, Ltd.     

A posteriori error estimation and anisotropy detection with the dual‐weighted residual method

In this work we develop a new framework for a posteriori error estimation and detection of anisotropies based on the dual‐weighted residual (DWR) method by Becker and Rannacher. The common approach for anisotropic mesh adaptation is to analyze the Hessian of the solution. Eigenvalues and eigenvectors indicate dominant directions and optimal stretching of elements. However, this approach is firmly linked to energy norm error estimation. Here, we extend the DWR method to anisotropic finite elements allowing for the direct estimation of directional errors with regard to given output functionals. The resulting meshes reflect anisotropic properties of both the solution and the functional. For the optimal measurement of the directional errors, the coarse meshes need some alignment with the dominant anisotropies. Numerical examples will demonstrate the efficiency of this method on various three‐dimensional problems including a well‐known Navier–Stokes benchmark. Copyright © 2009 John Wiley & Sons, Ltd.     

几何精确NURBS有限元中边界条件施加方式对精度影响的三维计算分析

非均匀有理B样条(NURBS)有限元法把计算机辅助几何设计(CAGD)中的NURBS几何构形方法与有限元方法有机结合起来,有效消除了有限元离散模型的几何误差,提高了计算精度。但是由于NURBS基函数不是插值函数,直接在控制节点上施加位移边界条件会引起较大误差。本文详细讨论了NURBS基函数的插值特性,在NURBS有限元分析中采用罚函数法施加位移边界条件,提高了收敛率和计算精度。结合典型三维弹性力学问题,对两种施加位移边界条件的方法进行了对比和分析。计算结果表明,直接施加位移边界条件会导致收敛率和精度的明显降低,而基于罚函数法的NURBS有限元分析则能达到最优收敛率,并具有更高的精度。