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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 3
Ann. Geophys., 21, 649–654, 2003
https://doi.org/10.5194/angeo-21-649-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 21, 649–654, 2003
https://doi.org/10.5194/angeo-21-649-2003
© Author(s) 2003. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Mar 2003

31 Mar 2003

Using routine meteorological data to derive sky conditions

D. Pagès1, J. Calbó2,1, and J. A. González1 D. Pagès et al.
  • 1Department of Physics, University of Girona, Girona, Spain
  • 2Institute of the Environment, University of Girona, Girona, Spain

Abstract. Sky condition is a matter of interest for public and weather predictors as part of weather analyses. In this study, we apply a method that uses total solar radiation and other meteorological data recorded by an automatic station for deriving an estimation of the sky condition. The impetus of this work is the intention of the Catalan Meteorological Service (SMC) to provide the public with real-time information about the sky condition. The methodology for deriving sky conditions from meteorological records is based on a supervised classification technique called maximum likelihood method. In this technique we first need to define features which are derived from measured variables. Second, we must decide which sky conditions are intended to be distinguished. Some analyses have led us to use four sky conditions: (a) cloudless or almost cloudless sky, (b) scattered clouds, (c) mostly cloudy – high clouds, (d) overcast – low clouds. An additional case, which may be treated separately, corresponds to precipitation (rain or snow). The main features for estimating sky conditions are, as expected, solar radiation and its temporal variability. The accuracy of this method of guessing sky conditions compared with human observations is around 70% when applied to four sites in Catalonia (NE Iberian Peninsula). The agreement increases if we take into account the uncertainty both in the automatic classifier and in visual observations.

Key words. Meteorological and atmospheric dynamics (instruments and techniques; radiative processes) – Atmospheric composition and structure (cloud physics and chemistry)

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