This talk will cover undergraduate student contributions to SWx research—as done within the SWx TREC's DLL—as well as delving into the need for technical workforce development (including methods leveraged by the DLL for undergraduate professional development resources).
In recent years, machine learning (ML) techniques have become a promising and effective tool to predict ionospheric total electron content (TEC) and irregularities. This seminar presents an image-based convolutional long short-term memory (convLSTM) ML algorithm to predict global TEC and storm-time high-latitude rate of TEC index (ROTI) maps.
This seminar takes listeners through an overview of student solar flare prediction research done within the SWx TREC Deep Learning Laboratory (DLL). Attendees will also be informed on the DLL's hardware, and availability to those interested in leveraging that.
Space weather is the main source of uncertainty in the location of satellites and debris in Low Earth Orbit (LEO). All objects in LEO orbit through the region of the Earth’s upper atmosphere called the “Thermosphere”. As solar conditions change, the thermosphere swells and shrinks, causing variable drag forces on orbiting objects. During an extreme geomagnetic storm, these forces could lead to dramatic changes in the orbits of all objects in LEO leading to a much higher risk of collision.