Cloud-probability-based estimation of black-sky surface albedo from AVHRR data

Abstract This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Cloud-probability-based estimation of black-sky surface albedo from AVHRR data ; volume:15 ; number:4 ; year:2022 ; pages:879-893 ; extent:15 ; Atmospheric measurement techniques ; 15, Heft 4 (2022), 879-893 (gesamt 15)

Beteiligte, Orts- und Zeitangaben

• Urheber: Manninen, Terhikki ; Jääskeläinen, Emmihenna ; Siljamo, Niilo ; Riihelä, Aku ; Karlsson, Karl-Göran

Weitere Informationen

• DOI: 10.5194/amt-15-879-2022
• URN: urn:nbn:de:101:1-2022022404215488616768
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:30 MESZ