On the relationship between shallow cumulus cloud field properties and surface solar irradiance

Details

• Journal Title:
  Geophysical Research Letters
• Personal Author:
  Gristey, Jake J. ; Feingold, Graham ; Glenn, Ian B. ; Schmidt, K. Sebastian ; Chen, Hong
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; CSL (Chemical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
• Description:
  Shallow cumulus clouds exhibit highly three‐dimensional (3‐D) spatial structure leading to complex variability in the surface solar irradiance (SSI) beneath. This variability is captured by the typically bimodal shape of the SSI probability density function (PDF). Using large eddy simulation to generate well‐resolved cloud fields and Monte Carlo 3‐D radiative transfer to reproduce realistic associated SSI PDFs, we seek direct relationships between the cloud field properties and the SSI PDF shape. Applying both random forest and artificial neural network algorithms, we find variations in the two modes of the SSI PDF are well predicted by just a handful of cloud field properties. The two algorithms utilize cloud properties similarly, with indistinguishable performance despite their different architectures. These results offer a marked improvement in realism relative to one‐dimensional radiative transfer while bypassing computationally expensive 3‐D radiative transfer, with immediate application to renewable energy assessments, and potential for several other geophysical applications.
• Keywords:
  Clouds Cumulus
• Source:
  Geophysical Research Letters, 47, e2020GL090152.
• DOI:
  https://doi.org/10.1029/2020GL090152
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:a57836d65bdcaa3910466a71a30bec0af0102876e008eb6cb1a7e06f4ec0f3bc
• Download URL:
  https://repository.library.noaa.gov/view/noaa/29401/noaa_29401_DS1.pdf
• File Type:
  [PDF - 1.69 MB ]