Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3755-2021
© 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-14-3755-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2021
Research article |
| 25 May 2021
| 25 May 2021
A simplified method for the detection of convection using high-resolution imagery from GOES-16
Department of Atmospheric Science, Colorado state university, Fort Collins, CO 80521, USA
Christian D. Kummerow
Department of Atmospheric Science, Colorado state university, Fort Collins, CO 80521, USA
Cooperative Institute for Research in the Atmosphere, Colorado state university, Fort Collins, CO 80521, USA
Milija Zupanski
Cooperative Institute for Research in the Atmosphere, Colorado state university, Fort Collins, CO 80521, USA
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Cited
17 citations as recorded by crossref.
- Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations M. Gelsinger et al. https://doi.org/10.1214/22-AOAS1708
- Latent heating profiles from GOES-16 and its impacts on precipitation forecasts Y. Lee et al. https://doi.org/10.5194/amt-15-7119-2022
- RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs C. Lu et al. https://doi.org/10.1109/TGRS.2025.3587883
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al. https://doi.org/10.1016/j.rse.2023.113762
- Fusing Multisource A-Train Satellites and Reanalysis Data for a Comprehensive Deep Convective System Dataset X. Hu et al. https://doi.org/10.1109/JSTARS.2024.3491160
- Evaluation of multi-source WRF data assimilation for a surface-reaching tropical hail event in Yogyakarta, Indonesia: a case study K. Oktabrian & A. Mulsandi https://doi.org/10.1007/s40808-026-02937-y
- A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum M. Frincu et al. https://doi.org/10.3390/electronics11172756
- Recognition of Organizational Morphology of Mesoscale Convective Systems Using Himawari-8 Observations Y. SHOU et al. https://doi.org/10.3724/j.1006-8775.2024.025
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. https://doi.org/10.3390/rs16162871
- Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8 B. Liu et al. https://doi.org/10.1109/TGRS.2026.3656118
- 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. https://doi.org/10.3390/rs17152621
- Satellite Cloud-Top Temperature-Based Method for Early Detection of Heavy Rainfall Triggering Flash Floods S. Hwang et al. https://doi.org/10.3390/w17243552
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. https://doi.org/10.1016/j.eswa.2024.126301
- First insights into deep convection by the Doppler velocity measurements of the EarthCARE Cloud Profiling Radar A. Galfione et al. https://doi.org/10.5194/amt-18-6747-2025
- A topological framework for real-time 3D weather radar data processing M. Lu et al. https://doi.org/10.1080/17538947.2022.2061616
- Multi-parameter Doppler Weather Radar analysis of an extreme rain event in central India: Precursors of convective intensification, sectoral variability and storm structure T. Sreekanth et al. https://doi.org/10.1016/j.atmosres.2026.108952
- Multisensor Analysis of Mediterranean Convective Case Studies Using EarthCARE and Meteosat Rapid Scan Observations A. Galfione et al. https://doi.org/10.3390/rs18203481
17 citations as recorded by crossref.
- Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations M. Gelsinger et al. https://doi.org/10.1214/22-AOAS1708
- Latent heating profiles from GOES-16 and its impacts on precipitation forecasts Y. Lee et al. https://doi.org/10.5194/amt-15-7119-2022
- RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs C. Lu et al. https://doi.org/10.1109/TGRS.2025.3587883
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al. https://doi.org/10.1016/j.rse.2023.113762
- Fusing Multisource A-Train Satellites and Reanalysis Data for a Comprehensive Deep Convective System Dataset X. Hu et al. https://doi.org/10.1109/JSTARS.2024.3491160
- Evaluation of multi-source WRF data assimilation for a surface-reaching tropical hail event in Yogyakarta, Indonesia: a case study K. Oktabrian & A. Mulsandi https://doi.org/10.1007/s40808-026-02937-y
- A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum M. Frincu et al. https://doi.org/10.3390/electronics11172756
- Recognition of Organizational Morphology of Mesoscale Convective Systems Using Himawari-8 Observations Y. SHOU et al. https://doi.org/10.3724/j.1006-8775.2024.025
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al. https://doi.org/10.3390/rs16162871
- Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8 B. Liu et al. https://doi.org/10.1109/TGRS.2026.3656118
- 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. https://doi.org/10.3390/rs17152621
- Satellite Cloud-Top Temperature-Based Method for Early Detection of Heavy Rainfall Triggering Flash Floods S. Hwang et al. https://doi.org/10.3390/w17243552
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. https://doi.org/10.1016/j.eswa.2024.126301
- First insights into deep convection by the Doppler velocity measurements of the EarthCARE Cloud Profiling Radar A. Galfione et al. https://doi.org/10.5194/amt-18-6747-2025
- A topological framework for real-time 3D weather radar data processing M. Lu et al. https://doi.org/10.1080/17538947.2022.2061616
- Multi-parameter Doppler Weather Radar analysis of an extreme rain event in central India: Precursors of convective intensification, sectoral variability and storm structure T. Sreekanth et al. https://doi.org/10.1016/j.atmosres.2026.108952
- Multisensor Analysis of Mediterranean Convective Case Studies Using EarthCARE and Meteosat Rapid Scan Observations A. Galfione et al. https://doi.org/10.3390/rs18203481
Saved (final revised paper)
Latest update: 19 Oct 2026
Short summary
This study suggests two methods to detect convection using 1 min data from GOES-16: one method detects early convective clouds using their vertical growth rate and the other method detects mature convective clouds using their lumpy cloud top surfaces. Applying the two methods to 1-month data showed that the accuracy of the combined methods was 85.8 % and showed their potential to be used in regions where radar data are not available.
This study suggests two methods to detect convection using 1 min data from GOES-16: one method...
