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.
In this presentation, we implement and validate a reduced-cost EnKF that estimate meteorological forcing parameters, such as wind direction, for large-eddy simulations of microscale pollutant dispersion.
Poster presentation of a data assimilation workflow for microscale pollutant dispersion. The use of a surrogate model enables real-time data assimilation and large ensemble size while also providing an estimation of the uncertainties involved.
Poster presentation of a data assimilation workflow for microscale pollutant dispersion. The use of a surrogate model enables real-time data assimilation and large ensemble size while also providing an estimation of the uncertainties involved.