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.
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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- The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic J. Zhang & P. Zuidema 10.5194/acp-19-14493-2019
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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
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- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
- Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors H. Andersen et al. 10.1029/2021GL096498
- Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations H. Andersen et al. 10.5194/acp-19-4383-2019
- Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors H. Dadashazar et al. 10.5194/acp-21-10499-2021
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. 10.5194/acp-20-3415-2020
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Latest update: 07 Jun 2023
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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