Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2699-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2021
Research article |
| 08 Apr 2021
| 08 Apr 2021
Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA
Christian D. Kummerow
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA
Cooperative Institute for Research in the Atmosphere, Fort Collins,
Colorado, USA
Imme Ebert-Uphoff
Cooperative Institute for Research in the Atmosphere, Fort Collins,
Colorado, USA
Department of Electrical and Computer Engineering, Colorado State
University, Fort Collins, Colorado, USA
Viewed
Total article views: 3,941 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Nov 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,548 | 1,316 | 77 | 3,941 | 116 | 107 |
- HTML: 2,548
- PDF: 1,316
- XML: 77
- Total: 3,941
- BibTeX: 116
- EndNote: 107
Total article views: 3,205 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Apr 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,243 | 891 | 71 | 3,205 | 106 | 96 |
- HTML: 2,243
- PDF: 891
- XML: 71
- Total: 3,205
- BibTeX: 106
- EndNote: 96
Total article views: 736 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Nov 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 305 | 425 | 6 | 736 | 10 | 11 |
- HTML: 305
- PDF: 425
- XML: 6
- Total: 736
- BibTeX: 10
- EndNote: 11
Viewed (geographical distribution)
Total article views: 3,941 (including HTML, PDF, and XML)
Thereof 3,781 with geography defined
and 160 with unknown origin.
Total article views: 3,205 (including HTML, PDF, and XML)
Thereof 3,138 with geography defined
and 67 with unknown origin.
Total article views: 736 (including HTML, PDF, and XML)
Thereof 643 with geography defined
and 93 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 citations as recorded by crossref.
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al. 10.1016/j.rse.2023.113762
- Advanced prediction of radar reflectivity using U-Net with visual and infrared data from Himawari-8 A. Patombongi et al. 10.1080/22797254.2025.2490021
- Lightning Detection Using GEO-KOMPSAT-2A/Advanced Meteorological Imager and Ground-Based Lightning Observation Sensor LINET Data S. Lee & M. Suh 10.3390/rs16224243
- Using machine learning to improve the estimate of U.S. background ozone F. Hosseinpour et al. 10.1016/j.atmosenv.2023.120145
- Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data 10.1175/MWR-D-21-0096.1
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al. 10.1175/JTECH-D-21-0084.1
- Comparison of GOES16 Data with the TRACER-ESCAPE Field Campaign Dataset for Convection Characterization: A Selection of Case Studies and Lessons Learnt A. Galfione et al. 10.3390/rs17152621
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al. 10.3390/rs16091561
- Cloud Top Temperature and Cloud Optical Thickness Can Effectively Identify Convective Clouds Over the Tibetan Plateau X. Ri et al. 10.1109/TGRS.2024.3486463
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al. 10.1017/eds.2023.26
- Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al. 10.1109/TGRS.2023.3237008
- Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks J. Rader et al. 10.1029/2021MS002941
- Convective–Stratiform Identification Neural Network (CONSTRAINN) for the WIVERN Mission F. Mustich et al. 10.3390/rs17152590
- The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects X. Tang et al. 10.3390/rs17050778
- End-to-End Prediction of Lightning Events from Geostationary Satellite Images S. Brodehl et al. 10.3390/rs14153760
- Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models R. Dávila-Ortiz et al. 10.3390/rs16040675
- A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning A. Shishov 10.3103/S1068373924040071
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al. 10.1109/TGRS.2023.3298976
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. 10.3390/rs16162871
- Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks S. Upadhyaya et al. 10.1109/LGRS.2022.3189535
22 citations as recorded by crossref.
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al. 10.1016/j.rse.2023.113762
- Advanced prediction of radar reflectivity using U-Net with visual and infrared data from Himawari-8 A. Patombongi et al. 10.1080/22797254.2025.2490021
- Lightning Detection Using GEO-KOMPSAT-2A/Advanced Meteorological Imager and Ground-Based Lightning Observation Sensor LINET Data S. Lee & M. Suh 10.3390/rs16224243
- Using machine learning to improve the estimate of U.S. background ozone F. Hosseinpour et al. 10.1016/j.atmosenv.2023.120145
- Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data 10.1175/MWR-D-21-0096.1
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al. 10.1175/JTECH-D-21-0084.1
- Comparison of GOES16 Data with the TRACER-ESCAPE Field Campaign Dataset for Convection Characterization: A Selection of Case Studies and Lessons Learnt A. Galfione et al. 10.3390/rs17152621
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al. 10.3390/rs16091561
- Cloud Top Temperature and Cloud Optical Thickness Can Effectively Identify Convective Clouds Over the Tibetan Plateau X. Ri et al. 10.1109/TGRS.2024.3486463
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al. 10.1017/eds.2023.26
- Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al. 10.1109/TGRS.2023.3237008
- Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks J. Rader et al. 10.1029/2021MS002941
- Convective–Stratiform Identification Neural Network (CONSTRAINN) for the WIVERN Mission F. Mustich et al. 10.3390/rs17152590
- The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects X. Tang et al. 10.3390/rs17050778
- End-to-End Prediction of Lightning Events from Geostationary Satellite Images S. Brodehl et al. 10.3390/rs14153760
- Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models R. Dávila-Ortiz et al. 10.3390/rs16040675
- A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning A. Shishov 10.3103/S1068373924040071
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al. 10.1109/TGRS.2023.3298976
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. 10.3390/rs16162871
- Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks S. Upadhyaya et al. 10.1109/LGRS.2022.3189535
Latest update: 28 Aug 2025
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.
Convective clouds are usually associated with intense rain that can cause severe damage, and...
