LEMDA: A Lagrangian-Eulerian Multiscale Data Assimilation Framework

Lagrangian trajectories are widely used as observations for recovering the underlying flow field via Lagrangian data assimilation (DA). However, the strong nonlinearity in the observational process and the high dimensionality of the problems often cause challenges in applying standard Lagrangian DA. In this paper, a Lagrangian-Eulerian multiscale DA (LEMDA) framework is developed. It starts with exploiting the Boltzmann kinetic description of the particle dynamics to derive a set of continuum equations, which characterize the statistical quantities of particle motions at fixed grids and serve as Eulerian observations. Despite the nonlinearity in the continuum equations and the processes of Lagrangian observations, the time evolution of the posterior distribution from LEMDA can be written down using closed analytic formulas after applying the stochastic surrogate model to describe the flow field. This offers an exact and efficient way of carrying out DA, which avoids using ensemble approximations and the associated tunings. The analytically solvable properties also facilitate the derivation of an effective reduced-order Lagrangian DA scheme that further enhances computational efficiency. The Lagrangian DA part within the framework has advantages when a moderate number of particles is used, while the Eulerian DA part can effectively save computational costs when the number of particle observations becomes large. The Eulerian DA part is also valuable when particles collide, such as using sea ice floe trajectories as observations. LEMDA naturally applies to multiscale turbulent flow fields, where the Eulerian DA part recovers the large-scale structures, and the Lagrangian DA part efficiently resolves the small-scale features in each grid cell via parallel computing. Numerical experiments demonstrate the skillful results of LEMDA and its two components.