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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-130
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-130
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 Jun 2020

Submitted as: research article | 08 Jun 2020

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This preprint is currently under review for the journal AMT.

Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements

Ilona Ylivinkka1,2, Santeri Kaupinmäki1,a, Meri Virman1, Maija Peltola1, Ditte Taipale1,2, Tuukka Petäjä1, Veli-Matti Kerminen1, Markku Kulmala1, and Ekaterina Ezhova1 Ilona Ylivinkka et al.
  • 1Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
  • 2SMEAR II station, University of Helsinki, 35500 Korkeakoski, Finland
  • acurrent address: Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, United Kingdom

Abstract. We developed a simple algorithm to classify clouds based on global radiation and cloud base height measured by pyranometer and ceilometer, respectively. We separated clouds into seven different classes (stratus, stratocumulus, cumulus, nimbostratus, altocumulus+altostratus, cirrus+cirrocumulus+cirrostratus and clear sky+cirrus). We also included classes for cumulus and cirrus clouds causing global radiation enhancement, and classified multilayered clouds, when captured by the ceilometer, based on their height and characteristics (transparency, patchiness and uniformity). The overall performance of the algorithm was nearly 70 % when compared with classification by an observer using total sky images. The performance was best for clouds having well-distinguishable effects on solar radiation: nimbostratus clouds were classified correctly in 100 % of the cases. The worst performance corresponds to cirriform clouds (50 %). Although the overall performance of the algorithm was good, it is likely to miss the occurrence of high and multilayered clouds. This is due to the technical limits of the instrumentation: the vertical detection range of the ceilometer and occultation of the laser pulse by the lowest cloud layer.

We examined the use of brightness parameter, which is defined as a ratio between measured global radiation and modeled radiation at the top of the atmosphere, as an indicator of clear sky conditions. Our results show that cumulus, altocumulus, altostratus and cirriform clouds can be present when the parameter indicates clear sky conditions. Those conditions have previously been associated with enhanced aerosol formation under clear sky. This is an important finding especially in case of low clouds coupled to the surface which can influence aerosol population via aerosol-cloud interactions. Overall, caution is required when the parameter is used in the analysis of processes affected by partitioning of radiation by clouds.

Ilona Ylivinkka et al.

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Latest update: 10 Jul 2020
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Short summary
In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland and we discuss on the advantages and shortcomings of the algorithm.
In this study, we developed a new algorithm for cloud classification using solar radiation and...
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