Variational data assimilation for 2D fluvial hydraulics simulations

International audienceA numerical method for model parameters identification is presented for a river model based on a finite volume discretization of the bidimensional shallow water equations. We use variational data assimilation to combine optimally physical information from the model and observation data of the physical system in order to identify the value of model inputs that correspond to a numerical simulation which is consistent with reality. Two numerical examples demonstrate the efficiency of the method for the identification of the inlet discharge and the bed elevation. An application to real data on the Pearl River for the identification of boundary conditions is presented

Variational data assimilation for 2D fluvial hydraulics simulations

International audienceA numerical method for model parameters identification is presented for a river model based on a finite volume discretization of the bidimensional shallow water equations. We use variational data assimilation to combine optimally physical information from the model and observation data of the physical system in order to identify the value of model inputs that correspond to a numerical simulation which is consistent with reality. Two numerical examples demonstrate the efficiency of the method for the identification of the inlet discharge and the bed elevation. An application to real data on the Pearl River for the identification of boundary conditions is presented

Sensitivity of Functionals in Problems of Variational Assimilation of Observational Data

International audienceThe problem of the variational assimilation of observational data is stated for a nonlinear evolutionmodel as a problem of optimal control in order to find the function of initial condition. The operator of themodel, and consequently the optimal solution, depend on parameters that may contain uncertainties. A functional of the solution of the problem of variational data assimilation is considered. Using the method of second-order adjoint equations, the sensitivity of the functional in respect to the model parameters is studied.The gradient of the functional is expressed through solving a “nonstandard” (nonclassical) problem thatinvolves the coupled system of direct and adjoint equations. The solvability of the nonstandard problem usingthe Hessian initial functional of observations is studied. Numerical algorithms for solving the problem andcomputing the gradient of the functional under consideration are developed with respect to the parameters.The results of the studies are applied in the problem of variational data assimilation for a 3D ocean thermodynamic model

Assimilation de données variationnelle pour des modèles d'hydrolique fluviale

National audienc

Assimilation of Remote Sensed Data for River Hydraulic Simulations

We present variational data assimilation methods applied to river hydraulics, especially when flooding. We consider two kinds of configurations. First, if observations are lagrangian trajectories (either extracted from videos or GPS drifting buoys); second, if observations include one satellite image of flood plain. Numerical results (with synthetic or real data) are presented. Copyright line will be provided by the publishe

Assimilation of Images via Dictionary Learning-Based Sparsity Regularization Strategy: An Application for Retrieving Fluid Flows

International audienceIn this work, we propose a structure sparsity regularization strategy in the framework of 4-D variational data assimilation (4-D Var). In meteorology and oceanography, the number of unknown model variables is far fewer than that of image observations, often leading to solve an underdetermined nonlinear inverse problem. In recent years, the ℓ¹-norm-based sparsity regularization approach has attracted great attention in the field of 4-D Var because of its data structure preservation and noise suppression. To avoid little underlying physical priors considered, we introduce a widely used dictionary learning (DL) method to adaptively derive an efficient sparse approximation via learning a basis from a given dataset. For our target application of estimating sea surface flows, we consider a DL sparsity constraint on the variable of flow vorticity due to its rich spatial variation related to flows evolution. A novel anisotropic regularization method combined with fluid dynamics characteristics could overcome magnitude underestimation and staircase artifacts appearing in the gradient regularization-based 4-D Var method. The split Bregman iteration with fast convergence property is employed to solve the ℓ¹+ℓ² nonsmooth minimization problem. The promising fluid flows estimation performance in real test cases (assimilation of image sequences collected from CORIOLIS experimental turntable) demonstrates the efficiency of our approach