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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 3
Atmos. Meas. Tech., 7, 799–822, 2014
https://doi.org/10.5194/amt-7-799-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 7, 799–822, 2014
https://doi.org/10.5194/amt-7-799-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Mar 2014

Research article | 28 Mar 2014

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

J. P. Musial1,2,*, F. Hüsler1,2, M. Sütterlin1,2, C. Neuhaus1,2, and S. Wunderle1,2 J. P. Musial et al.
  • 1Geographisches Institut der Universität Bern (GIUB), 3012 Bern, Switzerland
  • 2Oescher-Zentrum für Klimaforschung, Universität Bern, Zähringerstrasse 25, 3012, Bern, Switzerland
  • *currently at: the Remote Sensing Department, Institute of Geodesy and Cartography, Modzelewskiego 27, 02-679 Warsaw, Poland

Abstract. Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

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