Articles | Volume 14, issue 4
Atmos. Meas. Tech., 14, 2699–2716, 2021
Atmos. Meas. Tech., 14, 2699–2716, 2021

Research article 08 Apr 2021

Research article | 08 Apr 2021

Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

Yoonjin Lee et al.

Related authors

Latent heating profiles from GOES-16 and its comparison to heating from NEXRAD and GPM
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech. Discuss.,,, 2021
Preprint withdrawn
Short summary
A simplified method for the detection of convection using high-resolution imagery from GOES-16
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 14, 3755–3771,,, 2021
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Physical characteristics of frozen hydrometeors inferred with parameter estimation
Alan J. Geer
Atmos. Meas. Tech., 14, 5369–5395,,, 2021
Short summary
Cloud height measurement by a network of all-sky imagers
Niklas Benedikt Blum, Bijan Nouri, Stefan Wilbert, Thomas Schmidt, Ontje Lünsdorf, Jonas Stührenberg, Detlev Heinemann, Andreas Kazantzidis, and Robert Pitz-Paal
Atmos. Meas. Tech., 14, 5199–5224,,, 2021
Short summary
Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126,,, 2021
Short summary
Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics
Florian Ewald, Silke Groß, Martin Wirth, Julien Delanoë, Stuart Fox, and Bernhard Mayer
Atmos. Meas. Tech., 14, 5029–5047,,, 2021
Short summary
Estimating the optical extinction of liquid water clouds in the cloud base region
Karolina Sarna, David P. Donovan, and Herman W. J. Russchenberg
Atmos. Meas. Tech., 14, 4959–4970,,, 2021
Short summary

Cited articles

Afzali Gorooh, V., Kalia, S., Nguyen, P., Hsu, K. L., Sorooshian, S., Ganguly, S., and Nemani, R. R.: Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS, Remote Sens., 12, 316,, 2020. 
Bankert, R. L., Mitrescu, C., Miller, S. D., and Wade, R. H.: Comparison of GOES cloud classification algorithms employing explicit and implicit physics, J. Appl. Meteorol. Clim., 48, 1411–1421,, 2009. 
Bedka, K. M. and Khlopenkov, K.: A probabilistic multispectral pattern recognition method for detection of overshooting cloud tops using passive satellite imager observations, J. Appl. Meteorol. Climatol., 55, 1983–2005,, 2016. 
Bedka, K. M., 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, 181–202,, 2010. 
Bedka, K. M., Dworak, R., Brunner, J., and Feltz, W.: Validation of satellite-based objective overshooting cloud-top detection methods using CloudSat cloud profiling radar observations, J. Appl. Meteorol. Clim., 51, 1811–1822,, 2012. 
Short summary
Convective clouds are usually associated with intense rain that can cause severe damage, and thus it is important to accurately detect convective clouds. This study develops a machine learning model that can identify convective clouds from five temporal visible and infrared images as humans can point at convective regions by finding bright and bubbling areas. The results look promising when compared to radar-derived products, which are commonly used for detecting convection.