Clear air turbulence is an aviation hazard that is still inadequately understood due to the lack of observational data. This is especially so for those occur in the upper troposphere and lower stratosphere (UT/LS) where most research aircraft do not reach and hence in-situ scientific data are rare. It would be highly beneficial if satellite data can be used for understanding and forecasting clear air turbulence because such data provide wide and, in the case of geostationary satellites, continuous coverage of atmospheric conditions.

Statistics have shown that a large majority of turbulence encounters associated with aviation is closely related to deep convective storms. Thus a good understanding of how convective storms generate turbulence in the UT/LS would be highly beneficial for the turbulence forecast. We have recently identified two storm top features that we believe are clear indicators of turbulence. The first is the occurrence of jumping cirrus and the second is anvil top plumes. The second feature has been observe by satellites and the first was initially observed by aircraft but may potentially been seen by satellites.

Jumping cirrus was observed by Fujita in 1980s but its properties remained unexplained. We performed a cloud model study and showed that jumping cirrus is produced by the breaking of gravity waves at the cloud top level. Wave breaking is the main generation mechanism of turbulence. Model results fit Fujita's description very closely. We will show videos of model results and actual observations of jumping cirrus to illustrate this turbulence generation mechanism.

The second feature, the storm top plumes, has been observed by meteorological satellites since early 1990s but like jumping cirrus the nature of this feature remained unexplained. We again use cloud model simulation results to show that this, too, is a wave breaking phenomenon closely associated with the overshooting top of severe thunderstorms and hence is also a turbulence generation mechanism. Videos of both model simulations and geostationary satellite image loops will be shown to illustrate this mechanism.

The turbulence generated associated with the above two features would be away from the clouds and hence would be classified as clear air turbulence. Our study shows that a model-assisted satellite data analysis of deep convective storms can be highly beneficial to the understanding and forecasting of severe clear air turbulence and to the aviation safety.