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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2019-413
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-413
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Jan 2020

Submitted as: research article | 16 Jan 2020

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This preprint is currently under review for the journal AMT.

Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

Larysa Istomina1,a, Henrik Marksa, Marcus Huntemann1,2, Georg Heygster2, and Gunnar Spreen2 Larysa Istomina et al.
  • 1Alfred-Wegener-Insitute, Helmholz Zentrum für Polar und Meeresforschung, Bremerhaven, 27570, Germany
  • 2Institute of Environmental Physics, University of Bremen, Bremen, 28357, Germany
  • aformerly at: Institute of Environmental Physics, University of Bremen, Bremen, 28357, Germany

Abstract. The historic MERIS sensor onboard Envisat (2002–2012) provides valuable remote sensing data for the retrievals of the summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI onboard Sentinel 3 (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the end product. The presence of 15 VIS and NIR spectral channels of MERIS allow high quality retrievals of sea ice albedo and melt pond fraction, but make cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm.

In this paper, we present a new cloud screening method MECOSI (MERIS Cloud screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high quality screening throughout the whole swath. Three years of reference cloud mask from AATSR (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well.

The comparison of the developed cloud mask to the operational AATSR and MODIS cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behaviour is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF/albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artefacts, which were clearly visible in the previous version of the dataset.

The resulting cloud mask for the MERIS operating time, as well as the improved MPF/albedo datasets are available as swath data and daily means on the ftp server of the University of Bremen https://seaice.uni-bremen.de/data/meris/gridded_cldscr/.

Larysa Istomina et al.

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Larysa Istomina et al.

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