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.
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.