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
17 citations as recorded by crossref.
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al. 10.1017/eds.2023.26
- 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
- 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
- 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
- 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
- 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
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al. 10.1109/TGRS.2023.3298976
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- 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
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al. 10.3390/rs16091561
17 citations as recorded by crossref.
- Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks A. Bansal et al. 10.1017/eds.2023.26
- 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
- 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
- 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
- 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
- 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
- Convective Cloud Detection and Tracking Using the New-Generation Geostationary Satellite Over South China Y. Yang et al. 10.1109/TGRS.2023.3298976
- Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data S. Brüning et al. 10.5194/amt-17-961-2024
- 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
- Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument Y. Qin et al. 10.3390/rs16091561
Latest update: 20 Nov 2024
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...
