Abstract
A preliminary work on a new way to estimate atmospheric turbulence using high-frequency Doppler lidar measurements is presented. The turbulence estimations are based on wind reconstruction using 3D Doppler lidar observations and a particle filter. The suggested reconstruction algorithm links the lidar observations to numerical particles to obtain turbulence estimations every time new observations are available. The high frequency of the estimations is a new point which is detailed and discussed. Moreover, the presented algorithm ables to reconstruct the wind in three dimensions in the observed volume. We have thus locally access to the spatial variability of the turbulent atmosphere. The suggested algorithm is applied to a set of real observations. The obtained results are very encouraging : they show significant improvements on turbulent parameter estimations.
Highlights
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
The aim is to reconstruct the 3D wind in the observed volume using the particle filter
It shows over a 2h period with a 4s time step the classical turbulent kinetic energy (TKE) calculated on 10 minutes and the TKE estimated at each time step with the particle method
Summary
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. Instead of working on these parameters we suggest a new way to reconstruct the wind from the observations. The reconstruction algorithm is a nonlinear Bayesian estimation method based on wind observations. The 3D wind is reconstructed and atmospheric turbulent parameters are estimated in a conic volume. The estimation method is based on a particle filter. Using this filter, the wind observations are associated to particle systems driven by a local Lagrangian turbulence model. The wind observations are associated to particle systems driven by a local Lagrangian turbulence model The particles have both fluid and stochastic properties. An application of the reconstruction algorithm to real WindCube observations is presented. For the turbulent parameter estimations, the main new point is the frequency of the estimations : turbulent parameters are estimated at the observation frequency
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