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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-2017-64
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
05 Apr 2017
Review status
This discussion paper is under review for the journal Atmospheric Measurement Techniques (AMT).
Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
Johan Strandgren1, Luca Bugliaro1, Frank Sehnke2, and Leon Schröder2 1Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
2Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden Württemberg, Systemanalyse, Stuttgart, Germany
Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-Atmosphere system. Nevertheless they remain one of the largest uncertainties in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.

CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0 respectively, that are retrieved by CALIOP. Among the cirrus free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8, and of 100 % or less for cirrus clouds with an optical thickness down to 0.07, with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS does also retrieve an opacity flag, which tells whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.

By retrieving CALIOP like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.


Citation: Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-64, in review, 2017.
Johan Strandgren et al.
Johan Strandgren et al.
Johan Strandgren et al.

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