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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- Untangling the influence of Antarctic and Southern Ocean life on clouds M. Mallet et al. https://doi.org/10.1525/elementa.2022.00130
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- Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic S. Doherty et al. https://doi.org/10.5194/acp-22-1-2022
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. https://doi.org/10.1029/2019JD031380
- Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors H. Andersen et al. https://doi.org/10.1029/2021GL096498
- Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations H. Andersen et al. https://doi.org/10.5194/acp-19-4383-2019
- Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective P. Nowack & D. Watson-Parris https://doi.org/10.5194/acp-25-2365-2025
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- The vertically time-dependent aerosol effect on marine water clouds H. Lu et al. https://doi.org/10.1016/j.jastp.2025.106453
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- Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation E. Pauli et al. https://doi.org/10.1016/j.jhydrol.2020.125451
- Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales Y. Li et al. https://doi.org/10.3390/rs17244045
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- Tropical Cyclone Size Identification over the Western North Pacific Using Support Vector Machine and General Regression Neural Network X. LU et al. https://doi.org/10.2151/jmsj.2022-048
- A satellite-based analysis of semi-direct effects of biomass burning aerosols on fog and low-cloud dissipation in the Namib Desert A. Mass et al. https://doi.org/10.5194/acp-25-491-2025
- The dry‐season low‐level cloud cover over western equatorial Africa: A case study with a mesoscale atmospheric model A. Berger et al. https://doi.org/10.1002/qj.4962
- Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover Y. Chen et al. https://doi.org/10.1038/s41561-022-00991-6
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- Biomass burning aerosol radiative effects in the Southeast Atlantic depend strongly on meteorological forcing method E. Giuffrida et al. https://doi.org/10.5194/acp-25-14879-2025
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. https://doi.org/10.5194/acp-20-3415-2020
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. https://doi.org/10.16993/tellusb.1868
34 citations as recorded by crossref.
- Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations L. Zipfel et al. https://doi.org/10.3390/atmos13040586
- A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model S. Fiddes et al. https://doi.org/10.5194/gmd-17-2641-2024
- The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic J. Zhang & P. Zuidema https://doi.org/10.5194/acp-19-14493-2019
- Untangling the influence of Antarctic and Southern Ocean life on clouds M. Mallet et al. https://doi.org/10.1525/elementa.2022.00130
- Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements H. Dadashazar et al. https://doi.org/10.5194/acp-20-4637-2020
- Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning R. Stirnberg et al. https://doi.org/10.5194/acp-21-3919-2021
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu https://doi.org/10.5194/acp-20-12853-2020
- Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic S. Doherty et al. https://doi.org/10.5194/acp-22-1-2022
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. https://doi.org/10.1029/2019JD031380
- Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors H. Andersen et al. https://doi.org/10.1029/2021GL096498
- Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations H. Andersen et al. https://doi.org/10.5194/acp-19-4383-2019
- Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective P. Nowack & D. Watson-Parris https://doi.org/10.5194/acp-25-2365-2025
- Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations H. Andersen et al. https://doi.org/10.5194/acp-23-10775-2023
- How Cloud Droplet Number Concentration Impacts Liquid Water Path and Precipitation in Marine Stratocumulus Clouds—A Satellite-Based Analysis Using Explainable Machine Learning L. Zipfel et al. https://doi.org/10.3390/atmos15050596
- Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning Y. Jia et al. https://doi.org/10.5194/acp-24-13025-2024
- Diurnal to interannual variability of low‐level cloud cover over western equatorial Africa in May–October V. Moron et al. https://doi.org/10.1002/joc.8188
- Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors B. Jian et al. https://doi.org/10.5194/acp-21-9809-2021
- Regional to large‐scale mechanisms controlling intraseasonal variability of low‐level clouds in Western Equatorial Africa V. Moron et al. https://doi.org/10.1002/qj.4974
- Influence of the subtropical high, IOD, and ENSO on cloud cover variability over the Indian Ocean J. Moher et al. https://doi.org/10.1007/s00382-025-07905-3
- The vertically time-dependent aerosol effect on marine water clouds H. Lu et al. https://doi.org/10.1016/j.jastp.2025.106453
- Impact of the variability in vertical separation between biomass burning aerosols and marine stratocumulus on cloud microphysical properties over the Southeast Atlantic S. Gupta et al. https://doi.org/10.5194/acp-21-4615-2021
- Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H. Andersen et al. https://doi.org/10.1080/16000889.2021.1971925
- A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data M. Kim et al. https://doi.org/10.3390/rs12213475
- Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation E. Pauli et al. https://doi.org/10.1016/j.jhydrol.2020.125451
- Global Low Clouds Evolution and Their Meteorological Drivers Across Multiple Timescales Y. Li et al. https://doi.org/10.3390/rs17244045
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. https://doi.org/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. https://doi.org/10.2151/jmsj.2022-048
- A satellite-based analysis of semi-direct effects of biomass burning aerosols on fog and low-cloud dissipation in the Namib Desert A. Mass et al. https://doi.org/10.5194/acp-25-491-2025
- The dry‐season low‐level cloud cover over western equatorial Africa: A case study with a mesoscale atmospheric model A. Berger et al. https://doi.org/10.1002/qj.4962
- Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover Y. Chen et al. https://doi.org/10.1038/s41561-022-00991-6
- Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors H. Dadashazar et al. https://doi.org/10.5194/acp-21-10499-2021
- Biomass burning aerosol radiative effects in the Southeast Atlantic depend strongly on meteorological forcing method E. Giuffrida et al. https://doi.org/10.5194/acp-25-14879-2025
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. https://doi.org/10.5194/acp-20-3415-2020
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. https://doi.org/10.16993/tellusb.1868
Saved (final revised paper)
Latest update: 11 Jun 2026
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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