Cliquez pour modifier le style du titre Cliquez pour modifier le style des sous-titres du masque Retrieval of the turbulent and backscattering properties using a non-linear filtering technique applied to Doppler LIDAR observation Christophe Baehr*, **, C. Beigbeder*, F. Couvreux*, A. Dabas*, B. Piguet* ** Météo-France/CNRS – CNRM/GAME URA1357 ** Institut de Mathématiques de Toulouse - Université de Toulouse III ISARS Boulder, Colorado, USA, 5-8 June 2012

Outline  Introduction  How a Doppler lidar and a particle filter can retrieve the properties of a random medium?  Some theoretical elements.  Experimental results : a numerical exercise.  Comparisons with Numerical products  Next Steps

Introduction  Our final objective is to retrieve the characteristics of a random medium from sparse observations.  The retrieval is done locally, that is, in the vicinity of the observations.  The retrieval technique we have developed has already been applied successfully to in-situ measurements of wind and temperature performed at fixed locations.  We present here the extension of our method to Doppler lidar data for the retrieval of TKE and EDR at a fine temporal resolution.

 How a Doppler lidar and a particle filter can retrieve the properties of a random medium?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

How does it work ?

 Some theoretical background

Theoretical Background are a random path and a random field is the acquisition process of the random field along the path is the conditional expectation according to the trajectory. where

Theoretical Background The probability laws of the random medium considered along a random path: There is a time evolution that required a local model of the probed medium. Here this evolution is given by the Markovian kernel

Theoretical Background The algorithm in term of state vectors is given by : It is equivalent to the evolution in probability laws : and solve the stochastic dynamical system :

The Stochastic Lagrangian Model The local Markovian evolution needs a physical model. We have choose to use a Stochastic Lagrangian Model (SLM). The model adapted to wind vertical profiles is excerpt from the 3D SLM we use for the atmospheric measurements : The term is embedded in a truncated normal distribution learned by our algorithm.

 Experimental results : a numerical exercise. Data recorded the June 19th, 2011 every 6s between 13h26 and 14h49 UTC at Lannemezan, France. Leosphere vertical lidar involved during the BLLAST experiment.

Experimental Results Vertical wind times series ( 6s ) black : reference series

Experimental Results Vertical wind times series ( 6s ) black : reference, blue : observation

Experimental Results Vertical wind times series ( 6s ) black : reference, blue : observation, red : estimated

Experimental Results Vertical wind, black : reference, blue : observation, red : estimated times series ( 6s ) Power Spectral Density

Experimental Results Vertical wind PSD, black : reference, blue : observation, red : estimated

Experimental Results Vertical wind profiles averaged on 1’. Above : reference, bottom : estimated

Experimental Results TKE times series ( 6s )

Experimental Results EDR times series ( 6s )

Experimental Results Vertical wind + TKE + EDR profiles averaged on 1’

Experimental Results Mean TKE and vertical wind variance profiles

Comparisons with other Numerical products Comparison with a Meso-NH simulation (for an other day and an other location) to compare the shape of the structures (above : wind, bottom TKE), especially for the TKE. How it is possible to assess the quality of TKE and EDR estimates ?

Comparisons with other Numerical products On June 19th, 2011 from 13h26 to 14h49 UTC, the tethered balloon flew at 60m and we compare its data with the lidar range bin m: - Balloon wind variance ~ 0.39 m 2 s −2. - lidar filtered signal variance ~ 0.42 m 2 s −2. - lidar averaged TKE ~ 0.25 m 2 s −2. We are waiting for other lidar data to compare with other, more representative balloon flights.

Next steps  Continue the work on the 3D estimations using hemispherical scanning lidars.  Work on the lidar observation operator.  Full set of numerical comparisons.  MesoNH comparisons with specific BLLAST simulations  Merge the estimation of Doppler lidar and Aerosol lidar to estimate the parameters of a full 3D atmospheric domain.  Work on the assimilation of turbulence parameters in NH models (e. g. Meso-NH).

Thank you for your attention Acknowledgements :