Standard Skies Classification Using Common Climatic Parameters
Danny H.W. Li, Chris C.S. Lau, Joseph C. Lam
Abstract
Danny H.W. Li, Chris C.S. Lau, Joseph C. Lam
Abstract
A good understanding of the sky luminance distribution is essential for efficient daylighting designs and developments. Sky luminance patterns are always modeled under various sky conditions using climatic parameters as weighting factors to indicate the degree of sky clearness. This paper presents the work on the identification of sky luminance patterns using three common climatic elements, namely cloud cover (CLD), sunshine hour (SH) and clearness index Kt. Three sky types representing overcast, partly cloudy and clear were selected from the Kittler et al. 15 standard skies. It has been shown that sky conditions classified by individual sky indicators produced acceptable agreements with the measured data. In general, CLD shows the best results, followed by Kt, then SH. Better results in terms of the frequency of occurrence and root mean square errors can be found when CLD and SH are jointly utilised to describe the sky conditions.
OpenAlex reports 38 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
A good understanding of the sky luminance distribution is essential for efficient daylighting designs and developments. Sky luminance patterns are always modeled under various sky conditions using climatic parameters as weighting factors to indicate the degree of sky clearness. This paper presents the work on the identification of sky luminance patterns using three common climatic elements, namely cloud cover (CLD), sunshine hour (SH) and clearness index Kt. Three sky types representing overcast, partly cloudy and clear were selected from the Kittler et al. 15 standard skies. It has been shown that sky conditions classified by individual sky indicators produced acceptable agreements with the measured data. In general, CLD shows the best results, followed by Kt, then SH. Better results in terms of the frequency of occurrence and root mean square errors can be found when CLD and SH are jointly utilised to describe the sky conditions.
Key concepts: Sky, Overcast, Weighting, Cloud cover, Daylighting, Luminance, Environmental science, Remote sensing