资料变分同化中的若干理论问题

对变分同化中的若干理论问题进行了研究,具体讨论了一类简单模式在整体和局部观测资料下的变分同化问题．对于整体观测资料下的变分同化问题,利用变分同化方法对预报模式中的初值、参数以及模式进行了修正,从理论上作出了变分同化方法的误差估计及收敛精度的估计,证明了变分同化方法的有效性．对于局部观测资料下的变分同化问题,由于得到的解往往不适定,因而通常的变分同化方法失效．为了克服问题的不适定性所带来的困难,利用变分同化结合正则化方法对预报模式中的初值、参数以及模式进行修正,同样作出了变分同化方法的误差估计及收敛精度估计,证明了变分同化与正则化方法结合的必要性和有效性,并对正则化参数的选择提供了理论判据．最后,举了一个实例说明所提出的方法的有效性．     

基于局地和非局地观测算子的GPS 掩星资料后向映射四维变分同化研究

本文通过在区域暴雨预报模式(AREM) 的后向映射四维变分同化系统(AREM-B4DVar) 中引入GPS 掩星折射率局地和非局地两种观测算子, 使得该系统具备了同化全球定位系统(GPS) 掩星折射率资料的能力, 并采用台湾地区与美国联合执行的气象、电离层和气候星座观测系统计划(COSMIC计划) 探测得到的GPS 掩星折射率资料和常规探空资料, 对2007 年7 月4 日至5 日发生在我国江淮流域的暴雨个例进行了同化预报试验. 结果表明, 在同化系统中采用局地和非局地两种观测算子, 加入GPS 掩星折射率资料后, 均可以提高观测资料附近初值的分析质量, 从而在改进24 小时的降水预报中起到正效果; 基于非局地观测算子的掩星折射率资料同化可以通过大气非局地的约束, 进一步改进基于局地观测算子掩星折射率资料同化的不足; 在常规资料的基础上加入掩星折射率资料, 可以使同化系统进一步改进初值分析质量和24 小时预报效果, 尤其能更好地发挥非局地观测算子的作用.     

局部资料下变分同化方法的稳定性研究

对于简化的一维扩散方程,在局部观测资料下,研究变分同化方法的稳定性.在变分同化中结合正则化方法,选择合适的正则化参数和稳定泛函,对预报模式进行修正,通过对预报精度进行先验估计,证明了该方法对于一维扩散方程的解的稳定性.修正补充相关计算结果,最后举出一个反例说明稳定性泛函的选取对于改进的变分同化方法实施的重要性.     

电动力学电磁场边值问题的广义变分原理

给出了线性各项异性电磁场边值问题的广义虚功原理表达式,运用钱伟长教授提出的方法建立了该问题的广义变分原理,可直接反映该问题的全部特征,即4个Maxwell方程、2个场强-位势方程、2个本构方程和8个边界条件．继而导出了一族有先决条件的广义变分原理．作为例证,导出了两个退化形式的广义变分原理,和已知的广义变分原理等价．此外还导出了两个修正的广义变分原理,可为该问题提供杂交有限元模型．建立的各广义变分原理可为电磁场边值问题的有限元应用提供更为完善的理论基础．     

聚类抽样情形下基于广义线性模型的风险比估计

Liu等人在聚类抽样的情形下基于贝塔二项分布模型讨论了风险比的区间估计问题.本文以他们的研究为背景,建立了一类聚类抽样样本的广义线性模型,并采用拟似然方法导出相应的广义估计方程,从而获得风险比的直接估计.这种方法不需要假定分布的具体形式,因而比Liu等人的方法具有更好的适用性.最后在一定正则条件下证明了估计的相合性和渐近正态性质.     

基于Lévy过程混杂微分方程的随机部分镇定

本文基于L′evy噪声镇定混杂微分系统,推广Brown运动镇定的情形.同时,利用Markov链状态空间分为两集合方法,将混杂系统分为两部分:可观测部分和不可观测部分,基于可观测部分镇定整个混杂系统,给出该混杂系统镇定和失稳的充分条件.最后,讨论系统保守性问题.     

弹性接触问题的一种新的混合变分形式

1．引言用混合有限元方法求解弹性力学问题,其优点在于可同时求解位移和应力．力学问题的混合变分形式是混合有限元方法的基础．对于弹性接触问题,文献问给出了一种混合变分形式,以及相应的混合有限元分析（也可见[6]）．本文考虑了弹性接触问题的一种新的混合变分形式,它是构造弹性接触问题的另一种混合有限元方法的基础．对于通常的静态弹性力学方程组的边界值（等式情形）问题,熟知可以有二种不同的混合变分形式（例如见门）．第一种混合变分形式中,对位移的求解空间为H‘（刚,对应力的求解空间为L‘（刚;而第二种混合变分形式…     

局部G-凸空间内的广义矢量拟平衡问题

在局部G-凸空间内引入和研究了几类广义矢量拟平衡问题(GVQEP)．包含了大多数广义矢量平衡问题,广义矢量变分不等式问题,拟平衡问题和拟变分不等式问题作为特殊情形．首先在局部G-凸空间内对一人对策证明了一个平衡存在性定理．作为应用,在非紧局部G-凸空间内对GVQEP的解建立了某些新的存在定理．这些结果和论证方法与最近文献中的结果和论证方法相比较是新的和完全不同的．     

广义Minty 向量似变分不等式解的性质

在拓扑向量空间中讨论下Dini方向导数形式的广义Minty向量似变分不等式问题. 可微形式的Minty变分不等式、Minty似变分不等式和Minty向量变分不等式是其特殊形式. 该文分别讨论了Minty向量似变分不等式的解与径向递减函数, 与向量优化问题的最优解或有效解之间的关系问题, 以及Minty向量似变分不等式的解集的仿射性质. 这些定理推广了文献中Minty变分不等式的一些重要的已知结果.     

离散广义Emden-Fowler方程的边值问题

应用临界点理论, 得到离散广义Emden-Fowler方程边值问题解的存在性的若干充分条件．对一类特殊情形, 其解的存在性条件是最佳的．对线性情形, 应用离散变分理论, 给出了上述方程边值问题解的存在性、唯一性以及多重性的充分必要条件.     

Numerical analysis for the approximation of optimal control problems with pointwise observations

In this paper, we study the numerical methods for optimal control problems governed by elliptic PDEs with pointwise observations of the state. The first order optimality conditions as well as regularities of the solutions are derived. The optimal control and adjoint state have low regularities due to the pointwise observations. For the finite dimensional approximation, we use the standard conforming piecewise linear finite elements to approximate the state and adjoint state variables, whereas variational discretization is applied to the discretization of the control. A priori and a posteriori error estimates for the optimal control, the state and adjoint state are obtained. Numerical experiments are also provided to confirm our theoretical results. Copyright © 2013 John Wiley & Sons, Ltd.     

Automatic differentiation in meteorology and oceanography

The physically consistent integration of observational data in dynamical circulation models of the atmosphere/ocean, e.g. for the purpose of predicting the future evolution of the climate system relies on variational data assimilation algorithms. These algorithms are based on the adjoint method of optimal control theory and implemented by Automatic Differentiation (AD) tools. The presence of turbulent phenomena imposes a challenge on AD based methods of adjoint flow control and highlights the role of the computational turbulence model. For the Lagrangian Averaged turbulence model, applied to the 3D Navier-Stokes equations, we establish well-posedness of the data assimilation problem. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Spectral estimates for saddle point matrices arising in weak constraint four‐dimensional variational data assimilation

We consider the large sparse symmetric linear systems of equations that arise in the solution of weak constraint four‐dimensional variational data assimilation, a method of high interest for numerical weather prediction. These systems can be written as saddle point systems with a 3 × 3 block structure but block eliminations can be performed to reduce them to saddle point systems with a 2 × 2 block structure, or further to symmetric positive definite systems. In this article, we analyse how sensitive the spectra of these matrices are to the number of observations of the underlying dynamical system. We also obtain bounds on the eigenvalues of the matrices. Numerical experiments are used to confirm the theoretical analysis and bounds.     

A truncated Newton optimization algorithm in meteorology applications with analytic Hessian/vector products

A modified version of the truncated-Newton algorithm of Nash ([24], [25], [29]) is presented differing from it only in the use of an exact Hessian vector product for carrying out the large-scale unconstrained optimization required in variational data assimilation. The exact Hessian vector products is obtained by solving an optimal control problem of distributed parameters. (i.e. the system under study occupies a certain spatial and temporal domain and is modeled by partial differential equations) The algorithm is referred to as the adjoint truncated-Newton algorithm. The adjoint truncated-Newton algorithm is based on the first and the second order adjoint techniques allowing to obtain a better approximation to the Newton line search direction for the problem tested here. The adjoint truncated-Newton algorithm is applied here to a limited-area shallow water equations model with model generated data where the initial conditions serve as control variables. We compare the performance of the adjoint truncated-Newton algorithm with that of the original truncated-Newton method [29] and the LBFGS (Limited Memory BFGS) method of Liu and Nocedal [23]. Our numerical tests yield results which are twice as fast as these obtained by the truncated-Newton algorithm and are faster than the LBFGS method both in terms of number of iterations as well as in terms of CPU time.     

Conservation laws for one-layer shallow water wave systems

The problem of correspondence between symmetries and conservation laws for one-layer shallow water wave systems in the plane flow, axisymmetric flow and dispersive waves is investigated from the composite variational principle of view in the development of the study [N.H. Ibragimov, A new conservation theorem, Journal of Mathematical Analysis and Applications, 333(1) (2007) 311–328]. This method is devoted to construction of conservation laws of non-Lagrangian systems. Composite principle means that in addition to original variables of a given system, one should introduce a set of adjoint variables in order to obtain a system of Euler–Lagrange equations for some variational functional. After studying Lie point and Lie–Bäcklund symmetries, we obtain new local and nonlocal conservation laws. Nonlocal conservation laws comprise nonlocal variables defined by the adjoint equations to shallow water wave systems. In particular, we obtain infinite local conservation laws and potential symmetries for the plane flow case.     

Coderivative Analysis of Variational Systems

The paper mostly concerns applications of the generalized differentiation theory in variational analysis to Lipschitzian stability and metric regularity of variational systems in infinite-dimensional spaces. The main tools of our analysis involve coderivatives of set-valued mappings that turn out to be proper extensions of the adjoint derivative operator to nonsmooth and set-valued mappings. The involved coderivatives allow us to give complete dual characterizations of certain fundamental properties in variational analysis and optimization related to Lipschitzian stability and metric regularity. Based on these characterizations and extended coderivative calculus, we obtain efficient conditions for Lipschitzian stability of variational systems governed by parametric generalized equations and their specifications.     

The Adjoint Newton Algorithm for Large-Scale Unconstrained Optimization in Meteorology Applications

A new algorithm is presented for carrying out large-scale unconstrained optimization required in variational data assimilation using the Newton method. The algorithm is referred to as the adjoint Newton algorithm. The adjoint Newton algorithm is based on the first- and second-order adjoint techniques allowing us to obtain the Newton line search direction by integrating a tangent linear equations model backwards in time (starting from a final condition with negative time steps). The error present in approximating the Hessian (the matrix of second-order derivatives) of the cost function with respect to the control variables in the quasi-Newton type algorithm is thus completely eliminated, while the storage problem related to the Hessian no longer exists since the explicit Hessian is not required in this algorithm. The adjoint Newton algorithm is applied to three one-dimensional models and to a two-dimensional limited-area shallow water equations model with both model generated and First Global Geophysical Experiment data. We compare the performance of the adjoint Newton algorithm with that of truncated Newton, adjoint truncated Newton, and LBFGS methods. Our numerical tests indicate that the adjoint Newton algorithm is very efficient and could find the minima within three or four iterations for problems tested here. In the case of the two-dimensional shallow water equations model, the adjoint Newton algorithm improves upon the efficiencies of the truncated Newton and LBFGS methods by a factor of at least 14 in terms of the CPU time required to satisfy the same convergence criterion.The Newton, truncated Newton and LBFGS methods are general purpose unconstrained minimization methods. The adjoint Newton algorithm is only useful for optimal control problems where the model equations serve as strong constraints and their corresponding tangent linear model may be integrated backwards in time. When the backwards integration of the tangent linear model is ill-posed in the sense of Hadamard, the adjoint Newton algorithm may not work. Thus, the adjoint Newton algorithm must be used with some caution. A possible solution to avoid the current weakness of the adjoint Newton algorithm is proposed.     

Variational methods of constructing monotone approximations for atmospheric chemistry models

A new method of constructing efficient monotone numerical schemes for solving direct, adjoint, and inverse atmospheric chemistry problems is presented. It is a synthesis of variational principles combined with splitting and decomposition methods and a constructive implementation of Euler integrating multipliers (EIM) bymeans of a local adjoint problem technique. To increase the efficiency of calculations, a method of decomposing the multicomponent substance transformation operators in terms of the mechanisms of reactions is also proposed. With analytical EIMs, the systems of stiff ODEs are decomposed and reduced to equivalent systems of integral equations solved by noniterative multistage algorithms of a given order of accuracy. An unconventional variational method of constructing mutually consistent algorithms for direct and adjoint problems and sensitivity studies for complex models with constraints is described.     

Numerical methods for computing sensitivities for ODEs and DDEs

We investigate the performance of the adjoint approach and the variational approach for computing the sensitivities of the least squares objective function commonly used when fitting models to observations. We note that the discrete nature of the objective function makes the cost of the adjoint approach for computing the sensitivities dependent on the number of observations. In the case of ordinary differential equations (ODEs), this dependence is due to having to interrupt the computation at each observation point during numerical solution of the adjoint equations. Each observation introduces a jump discontinuity in the solution of the adjoint differential equations. These discontinuities are propagated in the case of delay differential equations (DDEs), making the performance of the adjoint approach even more sensitive to the number of observations for DDEs. We quantify this cost and suggest ways to make the adjoint approach scale better with the number of observations. In numerical experiments, we compare the adjoint approach with the variational approach for computing the sensitivities.