Statistical study of coherent turbulent structures properties observed by a Doppler lidar over Paris during two months
Authors: Cheliotis Ioannis , Dieudonné Elsa , Delbarre Hervé , Sokolov Anton , Dmitriev Egor , Augustin Patrick , Fourmentin Marc , Ravetta François , Pelon Jacques 
Published: March 2020

&lt;p&gt;Pulsed Doppler wind lidars (PDWL) have been extensively used in order to study the atmospheric turbulence. Their ability to scan large areas in a short period of time is a substantial advantage over in-situ measurements. Furthermore, PDWL are capable to scan horizontally as well as vertically thus providing observations throughout the atmospheric boundary layer (ABL). By analysing PDWL observations it is possible to identify large turbulent structures in the ABL such as thermals, rolls and streaks. Even though several studies have been carried out to analyse such turbulent structures, these studies examine peculiar cases spanning over short periods of time.&lt;/p&gt;&lt;p&gt;For this study we analysed the turbulent structures (thermals, rolls, streaks) over Paris during a two-months period (4 September &amp;#8211; 6 October 2014, VEGILOT campaign) observed with a PDWL installed on a 70&amp;#160;m tower in Paris city centre. The turbulent radial wind field was reconstructed from the radial wind field of the horizontal surface scans (1&amp;#176; elevation angle) by using the velocity azimuth display method. The VEGILOT campaign provided 4577 horizontal surface scans, hence for the classification of the turbulent structures we developed an automatic method based on texture analysis and machine learning of the turbulent radial wind fields. Thirty characteristic cases of each turbulent structure types were selected at the learning step after an extensive examination of the meteorological parameters. Rolls cases were selected at the same time that cloud streets were visible on satellite images, streaks cases were selected during high wind shear development near the surface and thermals case were selected when solar radiation measurements in the area were high. In addition, sixty cases of &amp;#8220;others&amp;#8221;, representing any other type of turbulence, were added to the training ensemble. The analysis of errors estimated by the cross-validation shows that the K-nearest neighbours&amp;#8217; algorithm was able to classify accurately 96.3% of these 150 cases. Subsequently the algorithm was applied to the whole dataset of 4577 scans. The results show 52% of the scans classified as containing turbulent structures with 33% being coherent turbulent structures (22% streaks, 11% rolls).&lt;/p&gt;&lt;p&gt;Based on this classification, the physical parameters associated with the different types of turbulent structures were determined, e.g. structure size, ABL height, synoptic wind speed, vertical wind speed. Range height indicator and line of sight scans provided vertical observations that illustrate the presence of vertical motions during the observation of turbulent structures. The structure sizes were retrieved from the spectral analysis in the transverse direction relative to the synoptic wind, and are in agreement with the commonly observed sizes (a few 100&amp;#160;m for streaks, a few km for rolls).&lt;/p&gt;