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\begin{document}

\title{Hierarchical color similarity metrics for step-wise application on sky
monitoring surface cameras}



\author[1]{Sylvio Luiz Mantelli Neto}%
\author[2]{Aldo von Wangenheim}%
\author[3]{Enio Bueno Pereira}%
\author[4]{Antonio Carlos Sobieranki}%
\affil[1]{INPE Brazilian National Institute for Space Research}%
\affil[2]{UFSC Federal University of Santa Catarina}%
\affil[3]{Instituto Nacional de Pesquisas Espaciais}%
\affil[4]{Federal University of Santa Catarina}%


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  \date{May 25, 2024}


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\begin{abstract}
Digital cameras on the surface are frequently used for monitoring
atmospheric conditions. Several methods were developed to use the images
for synoptic observations, cloud assessments, short term forecasting and
so on. However, there are some restrictions not considered by these
methods, especially when a linear camera is used to observe logarithmic
ranges of atmospheric luminance. Cameras accommodate the scene to a
linear scale causing distortions on pattern distributions by pixel value
saturation (PVS) and drifts from its original hues. This brings on some
simplifying practices commonly found in the literature to overcome these
problems. But those practices result in loss of data, misinterpretation
of valid pixels and restriction on the use of computer vision
algorithms. The present work begins by illustrating these problems
performing supervised learning for two reasons: all observation systems
seek out automation of human synoptic observation in order to provide a
sound mathematical modeling of the observed patterns. A new modeling
paradigm is proposed to map the sky patterns to represent the existent
physical atmospheric phenomena not considered by the literature. We
validate the proposed method, and compared the results using 1630 images
against two well-established methods. A hypothesis test showed that
results are compatible with currently used binary approach with
advantages. Differences were due to PVS and other restrictions not
considered by the methods existent on literature. Finally, the present
work concludes that the new paradigm presents more meaningful results of
sky patterns interpretation, allows extended daylight observation
periods and uses a higher dimensional space.%
\end{abstract}%



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