Tentative detection of clear-air turbulence using a ground-based Rayleigh lidar

Alain Hauchecorne 1 Charles Cot 1 Francis Dalaudier 1 Jacques Porteneuve 1 Thierry Gaudo 2 Richard Wilson 1 Claire Cénac 3 Christian Laqui 4 Philippe Keckhut 1 Jean-Marie Perrin 5 Agnès Dolfi 6 Nicolas Cézard 6 Laurent Lombard 6 Claudine Besson 6
1 STRATO - LATMOS
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
2 ONERA - The French Aerospace Lab [Châtillon]
3 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)
4 LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
5 OHP - Observatoire de Haute-Provence
6 ONERA - The French Aerospace Lab [Palaiseau]
Abstract : Atmospheric gravity waves and turbulence generate small-scale fluctuations of wind, pressure, density, and temperature in the atmosphere. These fluctuations represent a real hazard for commercial aircraft and are known by the generic name of clear-air turbulence (CAT). Numerical weather prediction models do not resolve CAT and therefore provide only a probability of occurrence. A ground-based Rayleigh lidar was designed and implemented to remotely detect and characterize the atmospheric variability induced by turbulence in vertical scales between 40 m and a few hundred meters. Field measurements were performed at Observatoire de Haute-Provence (OHP, France) on 8 December 2008 and 23 June 2009. The estimate of the mean squared amplitude of bidimensional fluctuations of lidar signal showed excess compared to the estimated contribution of the instrumental noise. This excess can be attributed to atmospheric turbulence with a 95% confidence level.
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CNRS | X | UPMC | ENS-PARIS | UVSQ | LMD | LATMOS | X-LMD | UNIV-AMU | INSU | PARISTECH | ONERA | PSL | UNIV-PARIS-SACLAY | ENPC-LMD | ENPC | IRD | IRSTEA | X-DEP-MECA | SORBONNE-UNIVERSITE | SU-SCIENCES

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Alain Hauchecorne, Charles Cot, Francis Dalaudier, Jacques Porteneuve, Thierry Gaudo, et al.. Tentative detection of clear-air turbulence using a ground-based Rayleigh lidar. Applied optics, Optical Society of America, 2016, 55 (13), pp.3420-3428. ⟨10.1364/AO.55.003420⟩. ⟨insu-01322228⟩

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