Hanlim Lee
Papers
4
Total Citations
65
H-Index
4
About
Hanlim Lee is a leading researcher in satellite-based aerosol remote sensing and atmospheric classification, with a particular focus on leveraging machine learning to understand aerosol types and their environmental impacts. Lee’s most cited work introduces a pioneering random forest (RF) model that classifies aerosols using space-borne measurement data, trained on the Aerosol Robotic Network (AERONET) dataset. This approach has been applied to capital cities across Asia, revealing how urban and industrial emissions mix with natural dust—a critical insight for climate and air quality modeling. With over 65 combined citations across key publications, Lee’s contributions stand out for improving the spatial coverage and accuracy of satellite aerosol classification, addressing a long-standing challenge in remote sensing. Notable achievements include the retrieval of single scattering albedo for Asian dust mixed with pollutants using lidar observations, a method that enhances our understanding of aerosol radiative effects. Lee’s work is essential for students and researchers interested in the intersection of machine learning, atmospheric science, and satellite data analysis, offering practical tools for tracking pollution and climate-forcing particles on a global scale.
Research Focus
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Top Papers
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