Graduate students receive competitive NASA FINESST awards
Two atmospheric science graduate students have received NASA grants to improve research into cloud effects and precipitation. FINESST awards support graduate student research projects relevant to NASA’s Science Mission Directorate, totaling up to $150K over three years.
Fewer than 10% of applicants are selected for the competitive NASA award, called FINESST or Future Investigators in NASA Earth and Space Science and Technology. Three outstanding Walter Scott, Jr. College of Engineering graduate students received awards this year, including Xiaocheng Wei from the Department of Electrical and Computer Engineering.
Olivia Pierpaoli
I study tiny particles in the air called aerosols, which interact with sunlight and clouds and play a key role in Earth’s climate. Satellites help us observe aerosols by measuring sunlight reflected to space. By examining how bright that reflected sunlight is, and how it changes across different colors of light, we can identify aerosol types (e.g., smoke, dust, or pollution) and estimate their abundance and particle sizes.
However, this has only worked in areas far from clouds. Near clouds, the strong reflection from clouds obscures the weaker signal from aerosols, leaving a blind spot in our ability to study aerosols. My research addresses this challenge by developing a new approach that accounts for cloud effects, making it possible to study aerosols even in near-cloud regions. This advancement will improve estimates of aerosol impacts on climate and open new opportunities to study aerosol–cloud interactions in areas previously considered inaccessible.
Spencer Jones
The satellites we use to observe precipitation use highly specialized sensors that measure Earth’s naturally emitted microwave radiation, and these sensors have limited resolution due to antenna size. We use algorithms that use principles of physics to estimate what atmospheric conditions produced these measurements. My research involves blending physics and AI to improve the resolution of microwave sensors by allowing the AI to “learn” the natural spatial patterns of precipitation to help find the physical solution.
Check out their full interviews on SOURCE.