Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5491-2020
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, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.

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, https://doi.org/10.5194/amt-10-351-2017,https://doi.org/10.5194/amt-10-351-2017, 2017
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024,https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024,https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024,https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024,https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024,https://doi.org/10.5194/amt-17-6707-2024, 2024
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, 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. 
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.