Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning
<p>Understanding the processes that determine low-cloud properties and aerosol–cloud interactions (ACIs) is crucial for the estimation of their radiative effects. However, the covariation of meteorology and aerosols complicates the determination of cloud-relevant influences and the quantif...
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doaj-636d4e7801524d1dbcbce24068628cd82020-11-24T23:28:37ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-11-0118165371655210.5194/acp-18-16537-2018Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learningJ. Fuchs0J. Fuchs1J. Cermak2J. Cermak3H. Andersen4H. Andersen5Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyInstitute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany<p>Understanding the processes that determine low-cloud properties and aerosol–cloud interactions (ACIs) is crucial for the estimation of their radiative effects. However, the covariation of meteorology and aerosols complicates the determination of cloud-relevant influences and the quantification of the aerosol–cloud relation.</p><p>This study identifies and analyzes sensitivities of cloud fraction and cloud droplet effective radius to their meteorological and aerosol environment in the atmospherically stable southeast Atlantic during the biomass-burning season based on an 8-day-averaged data set. The effect of geophysical parameters on clouds is investigated based on a machine learning technique, gradient boosting regression trees (GBRTs), using a combination of satellite and reanalysis data as well as trajectory modeling of air-mass origins. A comprehensive, multivariate analysis of important drivers of cloud occurrence and properties is performed and evaluated.</p><p>The statistical model reveals marked subregional differences of relevant drivers and processes determining low clouds in the southeast Atlantic. Cloud fraction is sensitive to changes of lower tropospheric stability in the oceanic, southwestern subregion, while in the northeastern subregion it is governed mostly by surface winds. In the pristine, oceanic subregion large-scale dynamics and aerosols seem to be more important for changes of cloud droplet effective radius than in the polluted, near-shore subregion, where free tropospheric temperature is more relevant. This study suggests the necessity to consider distinct ACI regimes in cloud studies in the southeast Atlantic.</p>https://www.atmos-chem-phys.net/18/16537/2018/acp-18-16537-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. Fuchs J. Fuchs J. Cermak J. Cermak H. Andersen H. Andersen |
spellingShingle |
J. Fuchs J. Fuchs J. Cermak J. Cermak H. Andersen H. Andersen Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning Atmospheric Chemistry and Physics |
author_facet |
J. Fuchs J. Fuchs J. Cermak J. Cermak H. Andersen H. Andersen |
author_sort |
J. Fuchs |
title |
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning |
title_short |
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning |
title_full |
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning |
title_fullStr |
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning |
title_full_unstemmed |
Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning |
title_sort |
building a cloud in the southeast atlantic: understanding low-cloud controls based on satellite observations with machine learning |
publisher |
Copernicus Publications |
series |
Atmospheric Chemistry and Physics |
issn |
1680-7316 1680-7324 |
publishDate |
2018-11-01 |
description |
<p>Understanding the processes that determine low-cloud properties
and aerosol–cloud interactions (ACIs) is crucial for the estimation of their
radiative effects. However, the covariation of meteorology and aerosols
complicates the determination of cloud-relevant influences and the
quantification of the aerosol–cloud relation.</p><p>This study identifies and analyzes sensitivities of cloud fraction and cloud
droplet effective radius to their meteorological and aerosol environment in
the atmospherically stable southeast Atlantic during the biomass-burning
season based on an 8-day-averaged data set. The effect of geophysical
parameters on clouds is investigated based on a machine learning technique,
gradient boosting regression trees (GBRTs), using a combination of satellite
and reanalysis data as well as trajectory modeling of air-mass origins. A
comprehensive, multivariate analysis of important drivers of cloud occurrence
and properties is performed and evaluated.</p><p>The statistical model reveals marked subregional differences of relevant
drivers and processes determining low clouds in the southeast Atlantic. Cloud
fraction is sensitive to changes of lower tropospheric stability in the
oceanic, southwestern subregion, while in the northeastern subregion it is
governed mostly by surface winds. In the pristine, oceanic subregion
large-scale dynamics and aerosols seem to be more important for changes of
cloud droplet effective radius than in the polluted, near-shore subregion,
where free tropospheric temperature is more relevant. This study suggests the
necessity to consider distinct ACI regimes in cloud studies in the southeast
Atlantic.</p> |
url |
https://www.atmos-chem-phys.net/18/16537/2018/acp-18-16537-2018.pdf |
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