Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5491–5511, 2020
https://doi.org/10.5194/amt-13-5491-2020
Atmos. Meas. Tech., 13, 5491–5511, 2020
https://doi.org/10.5194/amt-13-5491-2020

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.

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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, https://doi.org/10.1002/2016JD025408, 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, https://doi.org/https://doi.org/10.1117/1.3424910, 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, https://doi.org/10.5194/acp-13-10795-2013, 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, https://doi.org/10.1175/2009JAMC2286.1, 2010. 
Bedka, K., Brunner, J., and Feltz, W.: Overshooting top and enhanced-V anvil thermal couplet detection: Algorithm theoretical basis document, available at: http://clouds.larc.nasa.gov/site/people/data/kbedka/GOES-R_ABI_ATBD_OvershootingTop_Enhanced-V_100perc.doc (last access: 8 October 2020), 2011. 
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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.