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Operational Cloud Products In The UV-VIS-NIR: From Sentinel-5 Precursor Towards Sentinel-4 And Sentinel-5

Lutz, Ronny und Argyrouli, Athina und Romahn, Fabian und Loyola, Diego (2018) Operational Cloud Products In The UV-VIS-NIR: From Sentinel-5 Precursor Towards Sentinel-4 And Sentinel-5. ESA ATMOS Conference 2018, 2018-11-26 - 2018-11-29, Salzburg, Austria.

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Kurzfassung

The Copernicus missions focused on atmospheric composition and trace gas retrieval operate in the UV/VIS/NIR/SWIR spectral region. For an accurate trace gas retrieval, also a precise knowledge of the cloud properties in this wavelength region at each given scene is required. In this work we present the algorithms for retrieving the operational cloud products from the low earth orbit (LEO) Sentinel-5 Precursor and Sentinel-5 missions and the geostationary (GEO) Sentinel-4 mission. The cloud retrieval algorithms OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) have their heritage with GOME/ERS-2 and GOME-2 MetOp-A/B, where they have already been successfully implemented in an operational environment. The ROCINN algorithm retrieves cloud height, cloud optical thickness and cloud albedo from NIR measurements in and around the oxygen A-band, taking as input the cloud fraction computed with the OCRA algorithm that is based on a broad-band UV/VIS/NIR color space approach. This OCRA color space approach makes use of the assumption that clouds usually have a higher reflectivity than the surrounding surface and that the cloud reflectivity is almost wavelength independent across the UV/VIS/NIR region. Assigning R, G and B colors to respective broad-band reflectances in the NIR, VIS and UV leads to clouds appearing white in the normalized RGB color space. The scene furthest away from the white point is the one where we expect the least possible amount of cloud contamination. Joining all these scenes on a global grid allows to generate a cloud-free reflectance background composite map which, together with the measured reflectance, can be used to calculate a radiometric cloud fraction for any given measurement. The cloud height, optical thickness and albedo retrieved by ROCINN are provided for two different cloud models. One which treats clouds more realistically as layers of scattering water droplets (clouds-as-layers, CAL) and one which treats clouds as simple Lambertian reflectors (clouds-as-reflecting boundaries, CRB). Initial comparisons and validation results are shown for Sentinel-5P and perspectives are outlined for Sentinel-4.

elib-URL des Eintrags:https://elib.dlr.de/122615/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Operational Cloud Products In The UV-VIS-NIR: From Sentinel-5 Precursor Towards Sentinel-4 And Sentinel-5
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Lutz, RonnyRonny.Lutz (at) dlr.dehttps://orcid.org/0000-0002-7215-3642NICHT SPEZIFIZIERT
Argyrouli, Athinaathina.argyrouli (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Romahn, Fabianfabian.romahn (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350NICHT SPEZIFIZIERT
Datum:2018
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Atmospheric Composition, Clouds
Veranstaltungstitel:ESA ATMOS Conference 2018
Veranstaltungsort:Salzburg, Austria
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:26 November 2018
Veranstaltungsende:29 November 2018
Veranstalter :ESA
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Atmosphären- und Klimaforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Atmosphärenprozessoren
Hinterlegt von: Lutz, Dr. Ronny
Hinterlegt am:07 Nov 2019 11:59
Letzte Änderung:24 Apr 2024 20:26

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