We present a detailed evaluation of the turbulence forecast product eddy dissipation parameter (EDP) used at the Deutscher Wetterdienst (DWD). It is based on the turbulence parameterization scheme TURBDIFF, which is operational within the Icosahedral Nonhydrostatic (ICON) numerical weather prediction model used operationally by DWD. For aviation purposes, the procedure provides the cubic root of the eddy dissipation rate ε1/3 as an overall turbulence index. This quantity is a widely used measure for turbulence intensity as experienced by aircraft. The scheme includes additional sources of turbulent kinetic energy with particular relevance to aviation, which are briefly introduced. These sources describe turbulence generation by the subgrid-scale action of wake eddies, mountain waves, and convection, as well as horizontal shear as found close to fronts or the jet stream. Furthermore, we introduce a postprocessing calibration to an empirical EDR distribution, and we demonstrate the potential as well as limitations of the final EDP-based turbulence forecast by considering several case studies of typical turbulence events. Finally, we reveal the forecasting capability of this product by verifying the model results against one year of aircraft in situ EDR measurements from commercial aircraft. We find that the forecasted EDP performs favorably when compared to the Ellrod index. In particular, the turbulence signal from deep convection, which is accounted for in the EDP product, is advantageous when spatial nonlocality is allowed in the verification procedure.
We further compare against data from the SOUTHTRAC campaign that took place in 2019 over the Andes.
In particular we compare high quality turbulence data from the HALO aircragt against ICON.
The model runs in global and also local mode. Since ICON is not used so far for aviation turbulence
forecasting at convectionn permitting scales we expect insights for the development
of future turbulence products. The focus is on turbulence generated by
mountain waves and jet stream dynamics.
How to cite: Goecke, T., Machulskaya, E., Geldenhuys, M., Ungermann, J., Dörnbrack, A., and Schumann, U.: Aviation Turbulence Forecasting at DWD with ICON: Methodology, Case Studies, and Verification, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-696, https://doi.org/10.5194/ems2022-696, 2022.
