Superstatistical random fields from point-wise atmospheric turbulence measurements

We present an advanced model for the generation of synthetic wind fields that can be understood as an extension of the well-known Mann model of the wind energy sciences. In contrast to such Gaussian random field models which control second-order statistics (i.e., velocity correlation tensors or spectra), we demonstrate that our extended model incorporates the effects of higher-order statistics as well. In particular, the empirically observed phenomenon of small-scale intermittency, a key feature of atmospheric turbulent flows, can be reproduced with high accuracy and at considerably low computational cost. Our method is based on a recently developed multipoint statistical description of a turbulent velocity field [J. Friedrich et al., J. Phys. Complex. 2 045006 (2021)] and consists of a superposition of multivariate Gaussian statistics with fluctuating covariances. Furthermore, we explicitly show how such superstatistical Mann fields can be constrained on a certain number of point-wise measurement data. We give an outlook on the relevance of such surrogate wind fields in the context of the wind energy sciences.