Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects
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- Department of Atmospheric Science Colorado State University Fort Collins CO USA
- NASA Goddard Space Flight Center Greenbelt MD USA
- NOAA Chemical Sciences Laboratory Boulder CO USA
- NASA Goddard Space Flight Center Greenbelt MD USA, Joint Center for Earth System Technology University of Maryland Baltimore County Baltimore MD USA
- NASA Goddard Space Flight Center Greenbelt MD USA, GESTAR/Morgan State University Baltimore MD USA
- NOAA Chemical Sciences Laboratory Boulder CO USA, Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder CO USA
- Department of Atmospheric Science Colorado State University Fort Collins CO USA, Department of Meteorology University of Reading Reading UK
Abstract There is a lack of satellite‐based aerosol retrievals in the vicinity of low‐topped clouds, mainly because reflectance from aerosols is overwhelmed by three‐dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100–500 m horizontal resolution for all cloud‐free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately −2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear‐sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near‐cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.
- Research Organization:
- National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). NOAA Chemical Sciences Laboratory (CSL); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
- Sponsoring Organization:
- Cooperative Institute for Research in the Atmosphere; European Research Council (ERC); National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Contributing Organization:
- Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)
- Grant/Contract Number:
- 89243020SSC000055; AC05-76RL01830
- OSTI ID:
- 1893549
- Journal Information:
- Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 20 Vol. 49; ISSN 0094-8276
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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