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
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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16 citations as recorded by crossref.
- 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
- Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors B. Jian et al. 10.5194/acp-21-9809-2021
- Probing the Fog Life Cycles in the Namib Desert R. Spirig et al. 10.1175/BAMS-D-18-0142.1
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- Meteorology-driven variability of air pollution (PM<sub>1</sub>) revealed with explainable machine learning R. Stirnberg et al. 10.5194/acp-21-3919-2021
- 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. 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. 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. 10.3390/rs12213475
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- 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
- Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic S. Doherty et al. 10.5194/acp-22-1-2022
- 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
1 citations as recorded by crossref.
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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