Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 10
Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 10
Research article
 | 
14 Oct 2020
Research article |  | 14 Oct 2020

A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.

Viewed

Total article views: 1,874 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,180 628 66 1,874 63 66
  • HTML: 1,180
  • PDF: 628
  • XML: 66
  • Total: 1,874
  • BibTeX: 63
  • EndNote: 66
Views and downloads (calculated since 11 Jun 2020)
Cumulative views and downloads (calculated since 11 Jun 2020)

Viewed (geographical distribution)

Total article views: 1,874 (including HTML, PDF, and XML) Thereof 1,891 with geography defined and -17 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Nov 2024
Download
Short summary
This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
Read more