Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1059-2019
https://doi.org/10.5194/amt-12-1059-2019
Research article
 | 
18 Feb 2019
Research article |  | 18 Feb 2019

A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements

Soheila Jafariserajehlou, Linlu Mei, Marco Vountas, Vladimir Rozanov, John P. Burrows, and Rainer Hollmann

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Cited articles

Allen, R. C., Durkee, P. A., and Wash, C. H.: Snow/cloud discrimination with multispectral satellite measurements, J. Appl. Meteor., 29, 994–1004, https://doi.org/10.1175/1520-0450(1990)029<0994:SDWMSM>2.0.CO;2, 1990. 
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Arola, A., Eck, T. F., Kokkola, H., Pitkänen, M. R. A., and Romakkaniemi, S.: Assessment of cloud-related fine-mode AOD enhancements based on AERONET SDA product, Atmos. Chem. Phys., 17, 5991–6001, https://doi.org/10.5194/acp-17-5991-2017, 2017. 
Benesty, J., Chen, J., Huang, Y., and Cohen, I.: Noise Reduction in Speech Processing, Springer Topics in Signal Processing 2, Springer-Verlag Berlin Heidelberg, https://doi.org/10.1007/978-3-642-00296-0_5, 2009. 
Birks, A. R.: Improvements to the AATSR IPF relating to Land Surface Temperature Retrieval and Cloud Clearing over Land, AATSR Technical Note, Rutherford Appleton Laboratory, Chilton, UK, 2007. 
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Short summary
We developed a new algorithm for cloud identification over the Arctic. This algorithm called ASCIA, utilizes time-series measurements of Advanced Along-Track Scanning Radiometer (AATSR) on Envisat and Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and -3B. The data product of ASCIA is compared with three satellite products: ASCIA shows an improved performance compared to them. We validated ASCIA by ground-based measurements and a promising agreement is achieved.
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