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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Main Authors: J. Fuchs, J. Cermak, H. Andersen
Format: Article
Language:English
Published: Copernicus Publications 2018-11-01
Series:Atmospheric Chemistry and Physics
Online Access:https://www.atmos-chem-phys.net/18/16537/2018/acp-18-16537-2018.pdf
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spelling 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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