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DOI: 10.5445/IR/1000087789
Veröffentlicht am 23.11.2018

Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

Abstract (englisch):
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.

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.

The statistical model reveals marked subregional differences of relevant drivers and processes determining low clouds in the southeast Atlantic. Cloud fraction is sensitive t ... mehr

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1680-7324
URN: urn:nbn:de:swb:90-877893
KITopen-ID: 1000087789
HGF-Programm 12.01.01 (POF III, LK 01)
Erschienen in Atmospheric chemistry and physics
Band 18
Heft 22
Seiten 16537–16552
Projektinformation NaFoLiCA (DFG, DFG EIN, CE 163/7-1)
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 22.11.2018
Nachgewiesen in Web of Science
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