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
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Cited
27 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.
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al.
- Advanced prediction of radar reflectivity using U-Net with visual and infrared data from Himawari-8 A. Patombongi et al.
- Reducing Latency in Satellite-Based Precipitation Estimates Using GOES-16 and Machine Learning J. Muñoz et al.
- Lightning Detection Using GEO-KOMPSAT-2A/Advanced Meteorological Imager and Ground-Based Lightning Observation Sensor LINET Data S. Lee & M. Suh
- Using machine learning to improve the estimate of U.S. background ozone F. Hosseinpour et al.
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al.
- Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al.
- 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.
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al.
- Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8 B. Liu et al.
- 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.
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al.
- Cloud Top Temperature and Cloud Optical Thickness Can Effectively Identify Convective Clouds Over the Tibetan Plateau X. Ri et al.
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al.
- Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al.
- Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks J. Rader et al.
- Convective–Stratiform Identification Neural Network (CONSTRAINN) for the WIVERN Mission F. Mustich et al.
- 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.
- Self-Supervised Temporal Super-Resolution Framework With METEOR-Net for Enhancing Convective Process Reconstruction in Geostationary Satellite Cloud Data X. Lin et al.
- End-to-End Prediction of Lightning Events from Geostationary Satellite Images S. Brodehl et al.
- Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models R. Dávila-Ortiz et al.
- A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning A. Shishov
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al.
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al.
- Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks S. Upadhyaya et al.
27 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.
- Improved tropical deep convective cloud detection using MODIS observations with an active sensor trained machine learning algorithm K. Yang et al.
- Advanced prediction of radar reflectivity using U-Net with visual and infrared data from Himawari-8 A. Patombongi et al.
- Reducing Latency in Satellite-Based Precipitation Estimates Using GOES-16 and Machine Learning J. Muñoz et al.
- Lightning Detection Using GEO-KOMPSAT-2A/Advanced Meteorological Imager and Ground-Based Lightning Observation Sensor LINET Data S. Lee & M. Suh
- Using machine learning to improve the estimate of U.S. background ozone F. Hosseinpour et al.
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al.
- Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data
- Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods J. Haynes et al.
- 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.
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al.
- Hybrid Physical Segmentation and Machine Learning Approach for Deep Convective Cloud Detection With Himawari-8 B. Liu et al.
- 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.
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al.
- Cloud Top Temperature and Cloud Optical Thickness Can Effectively Identify Convective Clouds Over the Tibetan Plateau X. Ri et al.
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al.
- Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation A. Kaps et al.
- Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks J. Rader et al.
- Convective–Stratiform Identification Neural Network (CONSTRAINN) for the WIVERN Mission F. Mustich et al.
- 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.
- Self-Supervised Temporal Super-Resolution Framework With METEOR-Net for Enhancing Convective Process Reconstruction in Geostationary Satellite Cloud Data X. Lin et al.
- End-to-End Prediction of Lightning Events from Geostationary Satellite Images S. Brodehl et al.
- Approximation of a Convective-Event-Monitoring System Using GOES-R Data and Ensemble ML Models R. Dávila-Ortiz et al.
- A Method for Object-oriented Detection of Deep Convection from Geostationary Satellite Imagery Using Machine Learning A. Shishov
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al.
- Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images X. Ji et al.
- Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks S. Upadhyaya et al.
Saved (final revised paper)
Latest update: 02 May 2026
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...
