Multi-instrumental View of the Auroral Oval

Статья конференции
Yaysukevich Y., Astafyeva E., Oinats A., Vesnin A., Yasyukevich A., Vasiliev A., Garashchenko A., Sidorov D.
Progress in Electromagnetics Research Symposium
2022 Photonics and Electromagnetics Research Symposium, PIERS 2022
9781665460231
2022
The auroral oval is a region of footprints where configuration of the Earth's magnetic field lines is such that energetic particles could penetrate into the denser part of the ionosphere. The auroral oval features the most complex ionospheric processes. In this region intensive small-scale ionospheric irregularities exist during both calm and disturbed geomagnetic conditions. Such irregularities could result in radio wave scattering, GNSS (global navigation satellite system) positioning quality deterioration, failures in radio communication, etc. During the last decades scientists have been using GNSS ROTI (rate of total electron content index) data along with other tools to study small-scale irregularities. This data can help to study the oval dynamics. The current report provides data on the auroral oval dynamics, based on GNSS receiver global network data, coherent radars data, and satellite data. To calculate ROTI data we used SIMuRG system (https://simurg.iszf.irk.ru/). The auroral oval regions are assumed to correspond to high values of ROTI. Therefore, we can keep track of the oval's boundary using these data Coherent scatter radars record signals scattered from plasma irregularities which intensively appear at the oval boundary. We used SuperDARN-like radars located in Russia. Satellite data show sharp variations in field-aligned currents. During magnetic storms the oval expands equatorward, and small-scale irregularities generation shifts to mid-latitudes. All instruments are in good agreement when positioning the oval's boundary. That allows us to use different data to estimate the oval boundary. Some advance was achieved with computer vision techniques to find the auroral oval boundary in the Northern hemisphere. The techniques implemented mathematical morphology to expand data and decrease data gaps, and K-means and Otsu techniques to cluster image data. © 2022 IEEE.

Библиографическая ссылка

Yaysukevich Y., Astafyeva E., Oinats A., Vesnin A., Yasyukevich A., Vasiliev A., Garashchenko A., Sidorov D. Multi-instrumental View of the Auroral Oval // Progress in Electromagnetics Research Symposium. Vol.2022-April. 2022. P.1009-1013. ISBN (print): 9781665460231. DOI: 10.1109/PIERS55526.2022.9792594
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