Dutta Wins Outstanding Presentation Award at AMS Annual Meeting
Shweta Dutta won the Outstanding Presentation Award at the American Meteorological Society (AMS) 101st Annual Meeting, held January 10-15, 2021 in a virtual format. The AMS is the leading scientific organization for meteorologists and atmospheric scientists; the organization also studies space weather.
Dutta is a second year Ph.D. student in the Georgia Tech School of Electrical and Computer Engineering (ECE). She works in the Low Frequency Radio Group, where she is advised by ECE Associate Professor Morris Cohen.
The title of Dutta’s award-winning poster is “Electron Density Prediction in the Topside Ionosphere.” Space weather is caused by fluctuations in the sun's output of plasma, called the solar wind, which is punctuated by coronal mass ejections where the sun suddenly ejects tons of matter at once. All of these fluctuations change properties of the Earth's ionosphere, a layer of electrically charged particles in the upper atmosphere.
More relevantly for engineers, a major solar flare or coronal mass ejection from the Sun has the potential to disrupt Global Positioning System (GPS) service for hours to days, which would have all kinds of cascading impacts on society. These fluctuations from the sun translate to fluctuations in the ionosphere and have the potential to disrupt GPS satellites and even knock out large portions of power grids in a way that the state of Texas just experienced, except over a wider area and lasting longer. As such, learning to forecast the ionospheric response to space weather continues to be an important scientific goal for a more resilient infrastructure. Dutta’s work involves machine learning to model the ionosphere, which is known to be a major source of GPS/Global Navigation Satellite System (GNSS) error, and triggers satellite communication outages.
The uppermost portion of the ionosphere, known as the topside, 500-1,000 km, is rather difficult to measure because it cannot be remotely sensed from the ground. The more intense ionosphere in the middle blocks radio waves that might probe the topside. As such, a gap exists in the understanding of that area of the ionosphere. Dutta’s work is to compile decades worth of satellite observations and use machine learning tools to piece together the entire topside ionospheric response to space weather. Her ionospheric model is critical to forecasting where these disruptions within the ionosphere will occur, so that GPS satellites and other systems such as power grids can be kept up and running.