Predictability of isotropic turbulence by massive ensemble forecasting

The predictability of isotropic turbulence is studied with unprecedented detail using massive Monte Carlo ensembles. Predictability loss is characterized by the smoothing of the ensemble-averaged flow field with the forecasting time, which is accurately captured by the ensembles. It is shown that this process is well described in scale and physical space by a Gaussian low-pass filter, with a characteristic length scale that increases in time following a self-similar scaling. These results simplify the quantification of uncertainty in turbulence forecasts and open the possibility to efficiently assess the predictability of local inertial- and large-scale flow patterns.

Predictability of isotropic turbulence by massive ensemble forecasting Articles