Bayesian techniques for radar observations of the near-Earth space
Emerging research project on the focus area Earth and near-space system and environmental change
Focus institute: Kvantum
At high latitudes the almost vertical geomagnetic field couples the top of the Earth’s atmosphere with the magnetosphere and the solar wind. The magnetic field maps electric fields and guides energetic particles from the magnetosphere down to the ionosphere, a partially ionized region of the atmosphere between 60 and 1000 km in altitude. In the ionosphere the electric fields and particle precipitation drive electric currents and plasma drifts, ionize and heat the atmosphere, and cause changes in both ion and neutral chemical composition. The visible auroras are formed when precipitating electrons excite atoms and molecules, which emit light when returning to their ground states.
The ionosphere is observed with various instruments including radars, optical instruments, rockets and satellites. The EISCAT scientific association operates incoherent scatter radars in the northern Fenno-Scandinavia. These radars have the benefit that they can observe the whole altitude range of the ionosphere from ground and they are not affected by weather. EISCAT is building the next-generation EISCAT_3D radar system, which will provide an order-of-magnitude improvement in resolution and volumetric observations. However, resolutions of the radar observations will still need to be improved to catch the rapid, small-scale processes of the high-latitude ionosphere, and information of the ion chemical composition is currently not extracted from EISCAT radar data.
We use state-of-the-art Bayesian computational tools to combine the radar observations with physics-based models of the ionosphere. This combination allows us to reach very high resolutions and to estimate the previously unattainable ion composition. With the sophisticated analysis tools, we can study the small-scale processes in the high-latitude ionosphere.
Researcher, PhD student Habtamu Tesfaw
Email: Habtamu.tesfaw at oulu.fi
Keywords: EISCAT, EISCAT_3D, Bayesian techniques