3D Wind Reconstruction and Turbulence Estimation in the Boundary Layer from Doppler Lidar Measurements using Particle Method

Abstract:

Turbulent phenomena in the atmospheric boundary layer (ABL) are characterized by small spatial and temporal scales which make them difficult to observe and to model.

New remote sensing instruments, like Doppler Lidar, give access to fine and high-frequency observations of wind in the ABL. This study suggests to use a method of nonlinear estimation based on these observations to reconstruct 3D wind in a hemispheric volume, and to estimate atmospheric turbulent parameters. The wind observations are associated to particle systems which are driven by a local turbulence model. The particles have both fluid and stochastic properties. Therefore, spatial averages and covariances may be deduced from the particles. Among the innovative aspects, we point out the absence of the common hypothesis of stationary-ergodic turbulence and the non-use of particle model closure hypothesis. Every time observations are available, 3D wind is reconstructed and turbulent parameters such as turbulent kinectic energy, dissipation rate, and Turbulent Intensity (TI) are provided.

This study presents some results obtained using real wind measurements provided by a five lines of sight Lidar. Compared with classical methods (e.g. eddy covariance) our technic renders equivalent long time results. Moreover it provides finer and real time turbulence estimations. To assess this new method, we suggest computing independently TI using different observation types. First anemometer data are used to have TI reference.Then raw and filtered Lidar observations have also been compared. The TI obtained from raw data is significantly higher than the reference one, whereas the TI estimated with the new algorithm has the same order.

In this study we have presented a new class of algorithm to reconstruct local random media. It offers a new way to understand turbulence in the ABL, in both stable or convective conditions. Later, it could be used to refine turbulence parametrization in meteorological meso-scale models.