Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects

Details

• Journal Title:
  Geophysical Research Letters
• Personal Author:
  Yang, C. Kevin ; Chiu, J. Christine ; Marshak, Alexander ; Feingold, Graham ; Várnai, Tamás ; Wen, Guoyong ; Yamaguchi, Takanobu ; Jan van Leeuwen, Peter
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; CSL (Chemical Sciences Laboratory)
• Description:
  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
• Keywords:
  General Earth And Planetary Sciences Geophysics General Earth And Planetary Sciences General Earth And Planetary Sciences
• Source:
  Geophysical Research Letters, 49(20)
• DOI:
  https://doi.org/10.1029/2022gl098274
• ISSN:
  0094-8276 ; 1944-8007
• Format:
  PDF
• Publisher:
  American Geophysical Union (AGU)
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by-nc/4.0/
• Rights Information:
  CC BY-NC
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:f41db958216d6b60bad6b0292c873f3a9e6b58e6d323d708c08f1a3f87f528b8
• Download URL:
  https://repository.library.noaa.gov/view/noaa/55129/noaa_55129_DS1.pdf
• File Type:
  [PDF - 5.42 MB ]