Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

• Main Authors: Li, Lianfa, Franklin, Meredith, Girguis, Mariam, Lurmann, Frederick, Wu, Jun, Pavlovic, Nathan, Breton, Carrie, Gilliland, Frank, Habre, Rima
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2020
• Subjects: Bioengineering; Clinical Research; Aerosol Optical Depth; MAIAC; MERRA-2 GMI Replay Simulation; Deep learning; Downscaling; Missingness imputation; Air quality; Physical Geography and Environmental Geoscience; Geomatic Engineering; Geological & Geomatics Engineering; Merra; Aerosol Robotic Network
• Online Access: https://escholarship.org/uc/item/1rm291mz

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characteriz...