Machine Learning Uncovers Aerosol Size Information from Chemistry and Meteorology to Quantify Potential Cloud-Forming Particles
Arshad Arjunan Nair,Fangqun Yu,Pedro Campuzano-Jost,Paul J. DeMott,Ezra J. T. Levin,Jose L. Jimenez,Jeff Peischl,Ilana B. Pollack,Carley D. Fredrickson,Andreas J. Beyersdorf,Benjamin A. Nault,Minsu Park,Seong Soo Yum,Brett B. Palm,Lu Xu,Ilann Bourgeois,Bruce E. Anderson,Athanasios Nenes,Luke D. Ziemba,Richard H. Moore,Taehyoung Lee,Taehyun Park,Chelsea R. Thompson,Frank Flocke,Lewis Gregory Huey,Michelle J. Kim,Qiaoyun Peng Geophysical Research Letters（2021）
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Cloud condensation nuclei (CCN),particle size distribution (PNSD),aerosols,aircraft campaign observations,machine learning,explainable artificial intelligence (xAI)
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