Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5491–5511, 2020
Atmos. Meas. Tech., 13, 5491–5511, 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.

Related authors

Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections
Benjamin R. Scarino, Patrick Minnis, Thad Chee, Kristopher M. Bedka, Christopher R. Yost, and Rabindra Palikonda
Atmos. Meas. Tech., 10, 351–371,,, 2017
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Improving cloud type classification of ground-based images using region covariance descriptors
Yuzhu Tang, Pinglv Yang, Zeming Zhou, Delu Pan, Jianyu Chen, and Xiaofeng Zhao
Atmos. Meas. Tech., 14, 737–747,,, 2021
Short summary
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech., 13, 7047–7057,,, 2020
Short summary
Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997,,, 2020
Short summary
Microwave single-scattering properties of non-spheroidal raindrops
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech., 13, 6933–6944,,, 2020
Short summary
Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar
Jasper R. Lewis, James R. Campbell, Sebastian A. Stewart, Ivy Tan, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 13, 6901–6913,,, 2020
Short summary

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.