Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products

dc.bibliographicCitation.firstPage2821
dc.bibliographicCitation.issue11
dc.bibliographicCitation.journalTitleAtmospheric Measurement Techniqueseng
dc.bibliographicCitation.lastPage2836
dc.bibliographicCitation.volume16
dc.contributor.authorHünerbein, Anja
dc.contributor.authorBley, Sebastian
dc.contributor.authorHorn, Stefan
dc.contributor.authorDeneke, Hartwig
dc.contributor.authorWalther, Andi
dc.date.accessioned2024-06-13T06:50:20Z
dc.date.available2024-06-13T06:50:20Z
dc.date.issued2023
dc.description.abstractThe EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) satellite mission will provide new insights into aerosol-cloud-radiation interactions by means of synergistic observations of the Earth's atmosphere from a collection of active and passive remote sensing instruments, flying on a single satellite platform. The Multi-Spectral Imager (MSI) will provide visible and infrared images in the cross-track direction with a 150km swath and a pixel sampling at 500m. The suite of MSI cloud algorithms will deliver cloud macro- and microphysical properties complementary to the vertical profiles measured from the Atmospheric Lidar (ATLID) and the Cloud Profiling Radar (CPR) instruments. This paper provides an overview of the MSI cloud mask algorithm (M-CM) being developed to derive the cloud flag, cloud phase and cloud type products, which are essential inputs to downstream EarthCARE algorithms providing cloud optical and physical properties (M-COP) and aerosol optical properties (M-AOT). The MSI cloud mask algorithm has been applied to simulated test data from the EarthCARE end-to-end simulator and satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as from the Spinning Enhanced Visible InfraRed Imager (SEVIRI). Verification of the MSI cloud mask algorithm to the simulated test data and the official cloud products from SEVIRI and MODIS demonstrates a good performance of the algorithm. Some discrepancies are found, however, for the detection of thin cirrus clouds over bright surfaces like desert or snow. This will be improved by tuning of the thresholds once real observations are available.eng
dc.description.fondsLeibniz_Fonds
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/14701
dc.identifier.urihttps://doi.org/10.34657/13723
dc.language.isoeng
dc.publisherKatlenburg-Lindau : Copernicus
dc.relation.doihttps://doi.org/10.5194/amt-16-2821-2023
dc.relation.essn1867-8548
dc.rights.licenseCC BY 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subject.ddc550
dc.subject.otheraerosoleng
dc.subject.otheralgorithmeng
dc.subject.othercirruseng
dc.subject.otherMODISeng
dc.subject.othermultispectral imageeng
dc.subject.otherremote sensingeng
dc.subject.othersatellite dataeng
dc.subject.othersatellite missioneng
dc.subject.otherSEVIRIeng
dc.subject.othersimulatoreng
dc.subject.othervertical profileeng
dc.titleCloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM productseng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccess
wgl.contributorTROPOS
wgl.subjectGeowissenschaftenger
wgl.typeZeitschriftenartikelger
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