Deriving 3D wind fields in the Paris urban atmosphere from scanning Doppler lidar observations

Air quality and meteorology in urban environments are strongly affected by dynamical processes occurring in the atmospheric boundary layer. Vertical ventilation, horizontal advection, and atmospheric stratification, largely driven by surface-atmosphere exchanges influence the transport of momentum, heat, moisture, gases, and aerosols.

To improve the understanding of these exchange processes in the urban atmosphere and the implications of spatial variations in topography, surface roughness, and surface cover; the 3-dimensional wind field is studied. In this work, we are reporting results of the novel “volume wind processing (VW)” software to retrieve horizontal wind information on a 3D spatial grid from observations of a single scanning Doppler wind lidar (Vaisala Windcube 400s). In the framework of the PANAME initiative (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch), the Doppler wind lidar is deployed on the rooftop of a tall building in central Paris, France, for the duration of two years. It is set to perform a series of scan strategies to monitor the vertical and horizontal variations of the mean wind field across the city center.

In addition to classical vertical wind profiling at the location of the lidar in Doppler Beam Swinging mode (DBS), 2D maps of horizontal wind speed are obtained from zero-elevation Plan Position Indicator (PPI) scans to assess spatial heterogeneity of the wind field. Further, the VW provides vertical profiles of horizontal wind to be derived at large distances (up to 7km) from the sensor using sector PPI scans at multiple elevation angles. It is the objective of this work to quantify the uncertainties in the VW products, to optimize the scan strategies considering spatial and temporal variations of the wind field, and to finally demonstrate their potential for a variety of applications.

Observations describing the horizontal and vertical variations in wind speed and direction, at high spatial resolution and continuous temporal coverage, are expected to greatly advance the process of understanding the urban atmosphere dynamics. These new generation data are also valuable for the evaluation of numerical simulations (weather and air quality), the quantification of wind energy resources, air traffic (e.g., drones) and sustainable urban design.