1. 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* Christophe.Baehr@meteo.fr ** Météo-France/CNRS – CNRM/GAME URA1357 ** Institut de Mathématiques de Toulouse - Université de Toulouse III ISARS 2012 - Boulder, Colorado, USA, 5-8 June 2012

2. 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 Christophe.Baehr@meteo.fr

3. 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. Christophe.Baehr@meteo.fr

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

5. How does it work ? Christophe.Baehr@meteo.fr

6. How does it work ? Christophe.Baehr@meteo.fr

7. How does it work ? Christophe.Baehr@meteo.fr

8. How does it work ? Christophe.Baehr@meteo.fr

9. How does it work ? Christophe.Baehr@meteo.fr

10. How does it work ? Christophe.Baehr@meteo.fr

11. How does it work ? Christophe.Baehr@meteo.fr

12. How does it work ? Christophe.Baehr@meteo.fr

13. How does it work ? Christophe.Baehr@meteo.fr

14. How does it work ? Christophe.Baehr@meteo.fr

15. How does it work ? Christophe.Baehr@meteo.fr

16. How does it work ? Christophe.Baehr@meteo.fr

17.  Some theoretical background

18. Theoretical Background Christophe.Baehr@meteo.fr 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

19. Theoretical Background Christophe.Baehr@meteo.fr 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

20. Theoretical Background Christophe.Baehr@meteo.fr 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 :

21. The Stochastic Lagrangian Model Christophe.Baehr@meteo.fr 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.

22. Christophe.Baehr@meteo.fr  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.

23. Christophe.Baehr@meteo.fr Experimental Results Vertical wind times series ( 6s ) black : reference series

24. Christophe.Baehr@meteo.fr Experimental Results Vertical wind times series ( 6s ) black : reference, blue : observation

25. Experimental Results Christophe.Baehr@meteo.fr Vertical wind times series ( 6s ) black : reference, blue : observation, red : estimated

26. Experimental Results Christophe.Baehr@meteo.fr Vertical wind, black : reference, blue : observation, red : estimated times series ( 6s ) Power Spectral Density

27. Experimental Results Christophe.Baehr@meteo.fr Vertical wind PSD, black : reference, blue : observation, red : estimated

28. Experimental Results Christophe.Baehr@meteo.fr Vertical wind profiles averaged on 1’. Above : reference, bottom : estimated

29. Experimental Results Christophe.Baehr@meteo.fr TKE times series ( 6s )

30. Experimental Results Christophe.Baehr@meteo.fr EDR times series ( 6s )

31. Experimental Results Christophe.Baehr@meteo.fr Vertical wind + TKE + EDR profiles averaged on 1’

32. Experimental Results Mean TKE and vertical wind variance profiles Christophe.Baehr@meteo.fr

33. 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. Christophe.Baehr@meteo.fr How it is possible to assess the quality of TKE and EDR estimates ?

34. Comparisons with other Numerical products Christophe.Baehr@meteo.fr 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 75-125m: - 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.

35. Next steps Christophe.Baehr@meteo.fr  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).

36. Christophe.Baehr@meteo.fr Thank you for your attention Acknowledgements :