Data Mining for Atmospheric GravityWaves (LeeWaves)

Abstract

Gravity waves can emerge as a result of the perturbation of atmospheric circulatory systems. They encompass periodic, yet geographically stationary, changes in temperature, pressure and vertical wind component. Occurrence
of such waves is frequent if strong winds hit high mountains. Secondary effect of such waves may also be encountered as clear air turbulence (CAT) in commercial flights. Atmospheric gravity waves strongly influence weather phenomena and on a larger time scale climatic processes. They are responsible for the vertical transport and mixing of air from the stratosphere up to the mesosphere. First results from research flights in the Pyrenees during the spring 2015 measuring campaign are reported. Several flights with a sensor equipped unpowered
glider in altitudes between 2000 and 7000m were undertaken. Data Mining and Knowledge Discovery methods were applied. The results point to interesting patterns (states) in the structure and formation of lee waves and
lead to the understanding of such flights as Wave Track Flight scenarios.