Machine Learning Forecast and Statistical Exploration of Equatorial Ionization Anomaly Based on Total Electronic Content
Abstract
The ionosphere total electronic content (TEC), derived from multi-frequency Global Navigation Satellite System (GNSS) receiver, has been one of the most popular datasets in ionosphere research academia. The new advances in the completion of TEC maps and the forecast of TEC data by the modern ML(Machine Learning) algorithms have significantly leveraged its usability. While observing the TEC data, significant equatorial ionization anomaly (EIA) phenomenon displays that observably high TEC values occur around the magnetic equator lasting for a noticeable time, showing two strips each on one side of the equator. As we marched into multi-GNSS era, a new frontier of combining the traditional space science and the cutting-edge statistical learning to make a breakthrough in the specification and forecasting of EIA phenomenon has emerged. In this project, we aim at specifying EIA phenomena by automatically identifying its location and statistically describing its properties. We adopt Gaussian Mixture Model (GMM) with relatively free number of peaks to specify the EIA phenomenon and a series of state-of-the-art ML algorithms will be used to forecast local, regional and global EIA behavior. We automatically identify the EIA peaks, evaluating the peak TECs and prominences, and other key features, such as peak to equator distances and hemispheric asymmetry. Based on these EIA properties obtained, we could further explore the evolution of EIA peaks and the frequency, duration, intensity and periodicity of EIA bifurcation by constructing a state-of-the-art ML model based on the constructed EIA database as well as data indicating space weather conditions, for instance, solar wind and FISM solar radiation measurements, etc.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMSA42A..06S