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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 10
Atmos. Meas. Tech., 4, 2213–2224, 2011
https://doi.org/10.5194/amt-4-2213-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 4, 2213–2224, 2011
https://doi.org/10.5194/amt-4-2213-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Oct 2011

Research article | 19 Oct 2011

Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations

J. M. Krijger1, P. Tol1, L. G. Istomina2, C. Schlundt2, H. Schrijver1, and I. Aben1 J. M. Krijger et al.
  • 1SRON, Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
  • 2Institute of Enviromental Physics, University of Bremen, Bremen, Germany

Abstract. In the ultra-violet, visible and near infra-red wavelength range the presence of clouds can strongly affect the satellite-based passive remote sensing observation of constituents in the troposphere, because clouds effectively shield the lower part of the atmosphere. Therefore, cloud detection algorithms are of crucial importance in satellite remote sensing. However, the detection of clouds over snow/ice surfaces is particularly difficult in the visible wavelengths as both clouds an snow/ice are both white and highly reflective. The SCIAMACHY Polarisation Measurement Devices (PMD) Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 μm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the on-board SCIAMACHY PMDs are used because they provide higher spatial resolution compared to the main spectrometer measurements. In this paper we expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately detect clouds over snow-covered forests which is inherently difficult because of the similar spectral characteristics. Furthermore the SCIAMACHY measurements suffer from degradation with time. This must be corrected for adequate performance of SPICI over the full SCIAMACHY time range. Such a correction is described here. Finally the performance of the new SPICI algorithm is compared with various other datasets, such as from FRESCO, MICROS and AATSR, focusing on the algorithm improvements.

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