The atmospheric boundary layer is characterized by processes over a wide range of scales including turbulent and submesoscale motions. Simulations of the boundary layer in atmospheric numerical weather prediction (NWP) and climate models are known to show deficiencies. One reason is a lack of knowledge about submesoscale processes. The connection between the character and intensity of turbulence and submesoscale motions within the diurnal cycle is of particular interest for the evaluation and development of boundary layer parameterizations. The latter requires
- information about all three velocity components
- turbulent kinetic energy
- eddy dissipation rate (EDR)
- turbulence length scales
For studying the diurnal cycle of the submeso motions, ground-based remote sensing is especially useful, because it enables the continuous monitoring of the boundary layer in a specific region. Scanning Doppler lidars provide a wide range of applications by using different scan strategies.
- Document the turbulent characteristics of typical summertime boundary layers (clear, cloud-topped, with cold pools)
- diurnal evolution convective and stable boundary layer
- day-to-day variability (dry, cloudy, with cold pools)
- Identify the dominant scales of turbulent & submeso motions, the processes promoting these motions, and how well they can be identified
- diurnal evolution, day-to-day variability (dry, cloudy, cold pools)
- Evaluate boundary layer parameterizations (typical summertime ABL regimes and transition between the regimes)
A small network of Doppler wind lidars of type HALO Streamline will be set up in the FESSTVaL domain. We plan to combine different scan strategies, including VAD scans, RHI and PPI scans as well as vertical stares to get an optimal information about the turbulence and sub-mesoscale motions, as well as their spatial distribution.
The field campaign will be accompagnied by Large Eddy Simulations (LES). This will first of all help us to determine an optimal positioning and scan strategy for the LIDARs. Second, it will yield a 3D gridded dataset in addition to the measurements.