Evolving turbulence realizations of atmospheric flow

Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the "outer variability" (wind speed and wind direction) and the dispersion variables that comprise the "inner variability" (contaminant spread).

The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments.

The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.