Papers
3
Total Citations
19
H-Index
3
About
Jiyunting Sun is an emerging leader in atmospheric remote sensing, whose work zeroes in on one of climate science’s most stubborn uncertainties: how much sunlight airborne particles absorb. Sun’s research centers on the absorbing aerosol index (AAI) and its conversion from a qualitative flag into a quantitative tool for measuring aerosol absorption. In a landmark 2021 study, Sun pioneered a deep-learning approach to derive absorbing aerosol optical depth (AAOD) and single scattering albedo (SSA) over land directly from the ultraviolet aerosol index—a method that bypasses traditional, complex radiative transfer models and opens the door to global, high-resolution absorption retrievals. Earlier work (2018) demonstrated this principle by successfully quantifying SSA for the intense January 2017 Chile wildfires using OMI satellite data, showing how the near-UV index can be inverted to yield physically meaningful absorption values. With over 19 citations across these core papers, Sun’s contributions are foundational for reducing the large uncertainties in aerosol radiative forcing assessments. By bridging satellite observations and machine learning, Sun is helping transform a decades-long qualitative record into a quantitative climate data product—a critical step for improving both climate models and our understanding of wildfire and pollution impacts on Earth’s energy balance.
Research Focus
Key Achievements
Top Papers
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