What are the driving parameters for Large Scale Travelling Ionospheric Disturbances? A statistical analysis for the European region

Abstract

The conditions in the thermosphere and ionosphere need to be monitored and predicted, because they are impacting many technological systems like global navigation systems and satellite orbit prediction and the performance of satellite communication links. The thermosphere-ionosphere system gets severely disturbed during the passage of disturbances in the solar wind and inter planetary magnetic field, when large amounts of energy are transferred into the Earth system. The generation of strong electric fields and sudden intensive thermosphere heating in the auroral region are major consequent forces, which drive significant changes in the thermosphere-ionosphere system. A typical phenomenon during these storm conditions are large scale atmospheric gravity waves, which are generated by the heating processes in the auroral region and spread propagate equatorward. Commonly, these large-scale atmospheric gravity waves are measured and analysed via their ionosphere signature, the Large Scale Travelling Ionospheric Disturbances (LSTIDs). There have been a few statistical studies, describing the typical speed, wavelength and direction of LSTIDs. Also, a few attempts have been made to correlate the LSTID characteristics with geomagnetic activity indices. LSTID amplitudes and occurrence rate have been shown to be correlated with the Auroral Electrojet index (AE), the geomagnetic perturbation index Kp and the magnitude of field aligned currents. Most of these statistical studies have been performed more than a decade ago. Today, we are using the LSTID observations of two decades, to statistically analyse the correlation of LSTID amplitudes with the most relevant geomagnetic indices and geoeffective parameters based on solar wind observations. The question motivating our study is: Can we predict the occurrence and amplitude of LSTIDs? We are presenting the correlation results, which show best correlation for solar wind- magnetosphere coupling functions, and demonstrate initial prediction capabilities, which can be generated based on this knowledge.