UAH researcher wins NASA FINESST award to study solar weather to better protect against threats to humans, satellites and near-Earth technologies

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BYLINE: Russ Nelson

Newswise — A doctoral student at The University of Alabama in Huntsville has been awarded a NASA Future Investigators in NASA Earth and Space Science and Technology (FINESST) fellowship to study space weather to improve predictive methods for coronal mass ejections (CME) from the Sun. Improving the methods for forecasting the arrival time of these powerful events is vital to protecting against solar threats to satellites and near-Earth technology.

Syed Raza is a first-year Ph.D. student majoring in space science at UAH, a part of the University of Alabama System. FINESST awards proposals for graduate student-designed and performed research projects that contribute to the Science Mission Directorate’s science, technology and exploration goals. FINESST awards are research grants for up to three years and up to $50,000 per year. Winning proposals must present a well-defined research problem or activity and a justification of its scientific significance to NASA.

The researcher has been working in the field of heliophysics since summer 2021 when he took part in a joint Research Experiences for Undergraduates (REU) program hosted by UAH and NASA’s Marshall Space Flight Center. His current research involves studying CME, large expulsions of plasma from the Sun’s corona that can eject billions of tons of coronal material and carry an embedded magnetic field.

“Our research will be modeling space weather phenomena to provide better predictions,” Raza says. “This includes both the prediction of potential space weather events and their various effects in near-Earth space. This is an important task for the protection of our assets in space, as most modern technologies depend on space-borne satellites.”

His advisors are co-principal investigators on the project. “I developed a great relationship with my now-graduate mentors: Distinguished Professor of Space Science Dr. Nikolai Pogorelov and Research Scientist Dr. Talwinder Singh,” Raza says. “After the REU, I started working with them as an undergraduate research assistant. Their mentorship and support have already been instrumental in my scientific career.”

Accurate space weather forecasts are necessary to protect both humans and electronic equipment in space. Their importance is recognized in the objectives of the National Space Weather Strategy and Action Plan and U.S. Congress PROSWIFT Act 116-181, Dr. Pogorelov reports.

“My multi-institutional team, supported jointly by the National Science Foundation and NASA, leads the development of publicly accessible software for improving space weather forecasts with quantified uncertainty,” Dr. Pogorelov explains. “We have recognized, however, that such improvements are impossible without a synergy of data handling, computer simulations and machine learning (ML) techniques. The reason for that is the lack of observational data that would ensure the desired accuracy. The application of ML requires extensive observational data for proper training of our prediction algorithms. Our new FINESST fellow, Syed Raza, together with the UAH graduate and now Research Scientist, Dr. Talwinder Singh, are deeply involved in the optimization of space weather forecasts and nowcasts via ML, ensuring the best results in the framework of available observational data. They are also formulating requirements for new space missions that would allow the space weather community to improve our predictive capabilities.”

“This is a very impactful research,” adds Dr. Singh. “Accurate prediction of CMEs are needed by our technologically advanced society. Hopefully, Syed will play an important role in solving this problem during his Ph.D. program. We got very good results with his REU project that later were reported in the Astrophysical Journal. Syed worked on using Heliospheric Imager data to improve the arrival time prediction of coronal mass ejections. His proposed work for this fellowship involves taking this work forward with more advanced techniques. He has investigated several machine learning techniques to accomplish this.”



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