Surrogate-based ensemble data assimilation for reducing uncertainty in large-eddy simulation of microscale pollutant dispersion
This paper introduces and evaluates a novel data assimilation framework for atmospheric pollutant dispersion. The use of a surrogate model reduces the computational burden and makes it possible to inestigate in-depth the system observability.