Abstract
The predictability of aviation turbulence is influenced by energy-intensive flow patterns that are significantly smaller than the horizontal grid scale of current numerical weather prediction (NWP) models. The parameterization of these subgrid scale (SGS) processes is possible by means of an additional prognostic equation for the temporal change of turbulence kinetic energy (TKE), whereby scale transfer terms are used. This turbulence scheme has been applied operationally for 5 years in the NWP model ICON (Icosahedral Nonhydrostatic). The most important of the source terms parameterizes the Kelvin–Helmholtz instability, better known as clear air turbulence. This shear term was subjected to a nowcasting technique, is calculated with satellite data, and shifted forward in time using motion based on optical flow estimates and atmospheric motion vector (AMV). The nowcasts include turbulence altitude as determined by an adapted height assignment scheme presented here. The case studies illustrate that the novel approach for satellite-based turbulence nowcasting is a supplement to the NWP models.
Highlights
Almost 75% of weather-related flight incidents occur in areas of turbulence [1] or atmospheric areas of intense wind fluctuation
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Together with the adapted H2O-intercept method for determining the turbulence top height, it is shown within case studies that the novel method for turbulence nowcasting provides information
Summary
Almost 75% of weather-related flight incidents occur in areas of turbulence [1] or atmospheric areas of intense wind fluctuation. Turbulence is difficult to forecast because the phenomenon is produced by highly energy-intensive flow patterns, the lower spatial scale of which is limited only by the dimensions of the aircraft. Such small-scale structures cannot be adequately resolved on the computational meshes of current numerical weather prediction (NWP) models. Many of these turbulence phenomena, such as clear-air turbulence (CAT), mountain wave turbulence (MWT), convectively induced turbulence (CIT), or in-cloud turbulence (ICT) are characterized in diverse studies using high-resolution measurements or direct numerical simulations (see [1] for a detailed introduction). For short-term forecast, times up to 3–5 h, optical flow methods are able to provide accurate atmospheric motion fields [5] or horizontal velocities
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