Wind fields from atmospheric turbulence measurements

Abstract

We present recent advances in the modeling of wind fields and their reconstruction from real-world atmospheric turbulence measurements. The proposed wind field model provides a statistically coherent framework for modeling the empirically observed occurrence of extreme atmospheric events. Furthermore, we demonstrate that the model is general enough to enmesh real-world measurement points, e.g., from meteorological masts or LIDAR, and thus provides a rather convenient method for the reconstruction of highly resolved, three-dimensional wind fields. Our methodology is based on the so-called superstatistics of Gaussian-distributed (or Mann-type) velocity fields with fluctuating covariances and exactly reproduces higher-order statistics due to extreme small-scale wind fluctuations. Therefore, it could lead to more realistic inflow conditions and might potentially enhance the current design standard of wind turbines. We further discuss potential applications of the wind field model in the context of LIDAR measurements as well as for wake situations.