Abstract:
Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification.

Abstract:
Implicit particle filtering is a sequential Monte Carlo method for data assimilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by minimizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate, as happens in many geophysical applications, in particular in models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include models where uncertain dynamic equations are supplemented by conservation laws with zero uncertainty, or with higher order (in time) stochastic partial differential equations (PDE) or with PDEs driven by spatially smooth noise processes. We make the implicit particle filter applicable to such situations by combining gradient descent minimization with random maps and show that the filter is efficient, accurate and reliable because it operates in a subspace of the state space. As an example, we consider a system of nonlinear stochastic PDEs that is of importance in geomagnetic data assimilation.

Abstract:
Implicit particle filtering is a sequential Monte Carlo method for data assim- ilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by min- imizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate. This is the case in many geophysical applica- tions, in particular for models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include stochastic partial differential equations driven by spatially smooth noise processes and models for which uncertain dynamic equations are supplemented by con- servation laws with zero uncertainty. We make the implicit particle filter applicable to such situations by combining gradient descent minimization with random maps and show that the filter is efficient, accurate and reliable because it operates in a subspace whose dimension is smaller than the state dimension. As an example, we assimilate data for a system of nonlinear partial differential equations that appears in models of geomagnetism.

This paper presents new implicit algorithms for solving the variational inequality and shows that the proposed methods converge under certain conditions. Some special cases are also discussed.

Abstract:
Variational data assimilation in continuous time is revisited. The central techniques applied in this paper are in part adopted from the theory of optimal nonlinear control. Alternatively, the investigated approach can be considered as a continuous time generalisation of what is known as weakly constrained four dimensional variational assimilation (WC--4DVAR) in the geosciences. The technique allows to assimilate trajectories in the case of partial observations and in the presence of model error. Several mathematical aspects of the approach are studied. Computationally, it amounts to solving a two point boundary value problem. For imperfect models, the trade off between small dynamical error (i.e. the trajectory obeys the model dynamics) and small observational error (i.e. the trajectory closely follows the observations) is investigated. For (nearly) perfect models, this trade off turns out to be (nearly) trivial in some sense, yet allowing for some dynamical error is shown to have positive effects even in this situation. The presented formalism is dynamical in character; no assumptions need to be made about the presence (or absence) of dynamical or observational noise, let alone about their statistics.

Abstract:
A 4-dimensional variational data assimilation (4D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described in this article. The innovative approaches to the multi-incremental formulation, the weak digital filter constraint and the semi-Lagrangian time integration are highlighted with some details. The implicit dynamical structure functions are discussed using single observation experiments, and the sensitivity to various parameters of the 4D-Var formulation is illustrated. To assess the meteorological impact of HIRLAM 4D-Var, data assimilation experiments for five periods of 1 month each were performed, using HIRLAM 3D-Var as a reference. It is shown that the HIRLAM 4D-Var consistently out-performs the HIRLAM 3D-Var, in particular for cases with strong mesoscale storm developments. The computational performance of the HIRLAM 4D-Var is also discussed.The review process was handled by Subject Editor Abdel Hannachi

Abstract:
As a well-known numerical method, the extragradient method solves numerically the variational inequality of finding such that , for all . In this paper, we devote to solve the following hierarchical variational inequality Find such that , for all . We first suggest and analyze an implicit extragradient method for solving the hierarchical variational inequality . It is shown that the net defined by the suggested implicit extragradient method converges strongly to the unique solution of in Hilbert spaces. As a special case, we obtain the minimum norm solution of the variational inequality .

Abstract:
This paper studies the role of sparse regularization in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest exhibits sparsity in the real or transformed domain. We show that in the presence of sparsity, the $\ell_{1}$-norm regularization produces more accurate and stable solutions than the classic data assimilation methods. To motivate further developments of the proposed methodology, assimilation experiments are conducted in the wavelet and spectral domain using the linear advection-diffusion equation.

Abstract:
We consider the assimilation of Lagrangian data into a primitive equations circulation model of the ocean at basin scale. The Lagrangian data are positions of floats drifting at fixed depth. We aim at reconstructing the four-dimensional space-time circulation of the ocean. This problem is solved using the four-dimensional variational technique and the adjoint method. In this problem the control vector is chosen as being the initial state of the dynamical system. The observed variables, namely the positions of the floats, are expressed as a function of the control vector via a nonlinear observation operator. This method has been implemented and has the ability to reconstruct the main patterns of the oceanic circulation. Moreover it is very robust with respect to increase of time-sampling period of observations. We have run many twin experiments in order to analyze the sensitivity of our method to the number of floats, the time-sampling period and the vertical drift level. We compare also the performances of the Lagrangian method to that of the classical Eulerian one. Finally we study the impact of errors on observations.

Abstract:
为了研究单液膜气泡单层液膜包裹气体的特殊结构形式和内外壁面均受气液表面张力的特殊力学形式,在拉格朗日框架下采用无网格移动粒子半隐式法并基于表面自由能表面张力模型,建立了单液膜双气液界面表面张力模型,从而实现了单液膜气泡振荡变形过程中的复杂界面计算和捕捉。在此基础上对2个单液膜气泡的聚并和连接过程进行了模拟分析,获得了典型的流动现象和液膜变形特征与规律,发现减小表面张力系数或增大黏性系数均会减弱气泡变形过程中表面张力项的变形主导作用。为此,提出了凹点切线法用于计算连接型气泡的液膜夹角,明晰了连接型气泡的形状。计算结果可为工业消泡技术提供一定的理论依据。 The mesh？？less moving particle semi？？implicit method is employed within the Lagrangian framework to investigate the special geometry of the single film bubble with gas being shrouded by a layer of liquid film, and its special dynamic characteristic that surface tension acts both on the inside and outside interfaces. A surface tension model of the single？？film？？double？？interfaces is established based on the surface tension model of surface free energies, and the complex interface movement in the oscillation process of the single？？film bubble is calculated and captured. Moreover, the coalescence and connection process of two single？？film bubbles are simulated and analyzed, and the typical flow phenomena and deformation characteristics of the liquid film are obtained. Results show that the leading role of the surface tension in the bubble deforming process reduces when either the surface tension coefficient decreases or the fluid viscosity increases. Furthermore, a concave tangent method is proposed to calculate the angle of the liquid film of the connected bubbles, which helps to describe the shape of connected bubble, quantitatively. These results provide some theoretical supports to the industrial froth breaking