*Result*: 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. Plain Language Summary: Lagrangian tracers are drifters, such as robotic instruments, balloons, sea ice floes, and litter. The observed Lagrangian tracer trajectories can be combined with a model to improve the state estimation of the flow field. This is called Lagrangian data assimilation (DA), where a large number of tracer trajectories are often needed to infer the multiscale features of the flow field accurately. However, this process is usually quite expensive. In this paper, a new multiscale DA scheme that exploits both the Eulerian and Lagrangian observations is developed. The Eulerian observations are induced by a group of Lagrangian tracers. They are described at fixed grids and represent the statistical quantities of these particle motions. The DA with these Eulerian observations provides a rapid and accurate way to recover the large‐scale flow features, while the Lagrangian DA part efficiently resolves the small‐scale features in each grid cell via parallel computing. Eulerian DA is also extremely valuable when collisions between tracers happen, such as when sea ice floes are used as tracers; in this case, significant errors appear in Lagrangian DA. Notably, with appropriate stochastic surrogate forecast models, closed analytic formulas are available for the proposed DA framework. Key Points: An efficient nonlinear Lagrangian‐Eulerian multiscale data assimilation framework with parallel computing is developedA set of continuum equations under the Eulerian coordinates are rigorously derived from Boltzmann kinetic theory of particle dynamicsClosed analytic formulas are available for solving nonlinear data assimilation solutions and deriving reduced‐order schemes [ABSTRACT FROM AUTHOR]

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