Lagrangian data assimilation for river hydraulics simulations

Lagrangian data assimilation for river hydraulics simulations

We present a method to use lagrangian data from remote sensing observation in a variational data assimilation process for a river hydraulics model based on the bidimensional shallow water equations. The trajectories of particles advected by the flow can be extracted from video images and are used in addition to classical eulerian observations. This lagrangian data brings information on the surf...

Variational Data Assimilation for 2d Fluvial Hydraulics Simulations

A 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 simulatio...

On variational data assimilation for 1D and 2D fluvial hydraulics

We address two problems related to variational data assimilation (VDA) as applied to river hydraulics (1D and 2D shallow water models). In real cases, available observations are very sparse (especially during flood events). Generally, they are very few measures of elevation at gauging stations. The first goal of the present study is to estimate accurately some parameters such as the inflow disc...

Lagrangian data assimilation for point vortex systems †

A new method for directly assimilating Lagrangian tracer observations for flow state estimation is presented. It is developed in the context of point vortex systems. With tracer advection equations augmenting the point vortex model, the correlations between the vortex and tracer positions allow one to use the observed tracer positions to update the non-observed vortex positions. The method work...

Assimilation of Lagrangian Data in NEMOVAR