This paper tells the construction and validation of a surrogate model for atmospheric pollutant dispersion. We show that the selected approach is able to embed accurately the uncertainty at stakes.
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.
This paper provides a methodology to assess the effect of atmospheric microscale internal variability on time statistics. It is then applied to the validation of a LES model of the MUST field experiment.
In this presentation, we investigate the sensitivity of microscale dispersion large-eddy simulations to inflow boundary conditions, such as wind direction. This analysis constitutes a first step in the construction of a data assimilation workflow.