Articles | Volume 18, issue 22
Atmos. Chem. Phys., 18, 16537–16552, 2018
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.
Special issue: New observations and related modelling studies of the aerosol–cloud–climate...
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
Julia Fuchs et al.
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Cited
10 citations as recorded by crossref.
- Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements H. Dadashazar et al. 10.5194/acp-20-4637-2020
- 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
- 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
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
- 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
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. 10.5194/acp-20-3415-2020
- Probing the Fog Life Cycles in the Namib Desert R. Spirig et al. 10.1175/BAMS-D-18-0142.1
- First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib H. Andersen & J. Cermak 10.5194/amt-11-5461-2018
9 citations as recorded by crossref.
- Stratocumulus cloud clearings: statistics from satellites, reanalysis models, and airborne measurements H. Dadashazar et al. 10.5194/acp-20-4637-2020
- 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
- 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
- Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
- 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
- Synoptic-scale controls of fog and low-cloud variability in the Namib Desert H. Andersen et al. 10.5194/acp-20-3415-2020
- Probing the Fog Life Cycles in the Namib Desert R. Spirig et al. 10.1175/BAMS-D-18-0142.1
1 citations as recorded by crossref.
Latest update: 03 Mar 2021
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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