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.

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Cited articles

Ai, Y., Li, J., Shi, W., Schmit, T. J., Cao, C., and Li, W.: Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements, J. Geophys. Res., 122, 1700–1712,, 2017. 
Angal, A., Xiong, X., Choi, T., Chander, G., and Wu, A.: Using the Sonoran and Libyan desert test sites to monitor the temporal stability of reflective solar bands for Landsat 7 ETM+ and Terra MODIS sensors, J. Appl. Remote Sens., 4, 043525,, 2010. 
Aumann, H. H. and Ruzmaikin, A.: Frequency of deep convective clouds in the tropical zone from 10 years of AIRS data, Atmos. Chem. Phys., 13, 10795–10806,, 2013. 
Bedka, K., Brunner, J., Dworak, R., Feltz, W., Otkin, J., and Greenwald, T.: Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients, J. Appl. Meteorol. Clim., 49, 181–22,, 2010. 
Bedka, K., Brunner, J., and Feltz, W.: Overshooting top and enhanced-V anvil thermal couplet detection: Algorithm theoretical basis document, available at: (last access: 8 October 2020), 2011. 
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.