Articles | Volume 18, issue 22
https://doi.org/10.5194/acp-18-16537-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-16537-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Nov 2018
Research article |
| 22 Nov 2018
| 22 Nov 2018
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Jan Cermak
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Hendrik Andersen
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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- Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors B. Jian et al. 10.5194/acp-21-9809-2021
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- Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation E. Pauli et al. 10.1016/j.jhydrol.2020.125451
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
- Tropical Cyclone Size Identification over the Western North Pacific Using Support Vector Machine and General Regression Neural Network X. LU et al. 10.2151/jmsj.2022-048
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Latest update: 29 Jul 2025
Short summary
This study separates the influence of aerosol on cloud properties in the southeast Atlantic region from meteorological conditions in the biomass-burning season. Machine learning is used to link 8-day-averaged satellite and reanalysis data sets. Distinct regimes of aerosol–cloud interactions are identified in the subregions of the southeast Atlantic based on the obtained sensitivities.
This study separates the influence of aerosol on cloud properties in the southeast Atlantic...
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