Articles | Volume 14, issue 4
Atmos. Meas. Tech., 14, 2699–2716, 2021
https://doi.org/10.5194/amt-14-2699-2021
Atmos. Meas. Tech., 14, 2699–2716, 2021
https://doi.org/10.5194/amt-14-2699-2021

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.

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Preprint withdrawn
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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, https://doi.org/10.3390/rs12020316, 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, https://doi.org/10.1175/2009JAMC2103.1, 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, https://doi.org/10.1175/JAMC-D-15-0249.1, 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, https://doi.org/10.1175/2009JAMC2286.1, 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, https://doi.org/10.1175/JAMC-D-11-0131.1, 2012. 
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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.