Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
Articles | Volume 13, issue 10
Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5491–5511, 2020
https://doi.org/10.5194/amt-13-5491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5491–5511, 2020
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 et al.

Viewed

Total article views: 523 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
383 128 12 523 17 16
  • HTML: 383
  • PDF: 128
  • XML: 12
  • Total: 523
  • BibTeX: 17
  • EndNote: 16
Views and downloads (calculated since 11 Jun 2020)
Cumulative views and downloads (calculated since 11 Jun 2020)

Viewed (geographical distribution)

Total article views: 585 (including HTML, PDF, and XML) Thereof 579 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Feb 2021
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