Research Projects

Thermospheric winds are essential components of the mesosphere and lower thermosphere system, but there is a lack of systematic understanding of their recovery after strong geomagnetic activity and their influence on the mesosphere-lower thermosphere system. To advance our understanding, physics-based assimilations and machine learning models should be used to better predict space weather effects on the upper atmosphere's composition, energetics, and electrodynamics. The understanding of the polar ionosphere during intense space weather events is mainly based on studying individual components of solar wind and their effect on the upper atmosphere. However, a systematic understanding of solar wind interaction with magnetosphere-ionosphere-thermosphere coupling is crucial. This includes understanding the interconnection between sub-auroral polarization steams (SAPS), storm enhanced density (SED), and field aligned currents and their effects on radiative cooling by NO during different local and solar conditions. The current understanding of the effects of intense geomagnetic activity on radiative cooling by NO and CO? has mainly resulted from event-specific studies, which have not addressed some important scientific gaps. For example, the dynamical balance of atomic oxygen density with respect to altitude, temperature, and its interrelation with overall infrared flux by NO or CO? in thermosphere and mesosphere is still unknown. The machine learning approach, an application of artificial intelligence, can handle large datasets and provide a realistic baseline for many geophysical parameters during moderate solar activity conditions.