Simulation of Seasonal Variations in the Local Daylight Climate

Abstract

The natural human biorhythm was mainly influenced by periodic changes of daytime activities and nighttime rest. In the original evolution of the human environment in the equatorial region, the regular daily and yearly changes of sunrise and sunset influenced considerably also vital behavior of the body for permanent 12 h of daytime and the same nighttime period. Migration to northern and southern territories during prehistoric time brought experience of seasonal daylight changes. These were linked with the daytime interval varying in the yearly cycles.

Appendix 6

6.1.1 Possibilities to Simulate Year-Round Changes of the Local Daylight Climate

Soon after the set of sky standards had been published by Kittler et al. (1997), Tregenza (1999) tried to analyze available sky scan data in several maritime locations and noticed some similarities in the frequency of sky types. In Singapore, Garston, and Sheffield, sky types 1, 3, 4, 8, 11, and 13 seemed to be most relevant. Later, Tregenza (2004) recommended his method of sky scan analysis to obtain the sky type frequency distribution. Thereafter, several IDMP stations used their long-term data to find the most frequent locally occurring sky types, as reported by Ng et al. (2007) or Li and Tang (2008) for Hong Kong, Chirarattananon and Chaiwiwatworakul (2007) for Bangkok, Wittkopf and Soon (2007) for Singapore, and Torres et al. (2010) for northern Spain. However, luminance scan measurements are available only in IDMP research stations, so other approaches to identify prevailing sky types or the daylight climate were sought, such as the classification parameter ${L_{\rm{vZ}}}/{D_{\rm{v}}}$(Kittler et al. 2001; Bartzokas et al. 2005), the vertical sky component, i.e., the ratio of vertical to horizontal diffuse illuminance (${\hbox{VSC}} = {D_{\rm{vv}}}/{D_{\rm{v}}}$) (Alshaibani 2008, 2011; Li et al. 2011) or analyzing the daylight climate by applying several meteorological parameters (Markou et al. 2009).

Daylight climate in the temperate region is characterized by almost all sky types as documented by the Bratislava seasonal distribution (winter overcast and summer clear, shown in Fig. A6.1). This seasonal effect increases with the distance of the locality from the ocean (Kittler et al. 2001). In the subtropical or Mediterranean climate, e.g., in Athens, clear sky types (Fig. A6.2) prevail. A more illustrative analysis based on the ${L_{\rm{vZ}}}/{D_{\rm{v}}}$ parameterization following the ±2.5% strip along each sky type ${L_{\rm{vZ}}}/{D_{\rm{v}}}$ curve from long-term Bratislava IDMP data which show sunless cases is shown in Fig. A6.3 and sunny cases in Fig. A6.4, same presentation for Athens documents considerable different climate conditions in Figs. A6.5 and A6.6. Owing to the ±2.5% strip selection in this analysis, the overall number of cases taken into consideration in Bratislava was reduced to 113,473 within the range of solar altitudes 5–70°. In the original study (Darula et al. 2001), a comparison was made by taking ±1 and ±2.5% strips to detect differences. However, good agreement with the previously documented data resulted.

Fig. A6.1

Seasonal sky type distribution after long-term 1994–1998 data gathered in Bratislava

Fig. A6.2

Seasonal sky type distribution after long-term 1992–1996 data gathered in Athens

Fig. A6.3

Situations without sun represent prevailing overcast skies in Bratislava

Bratislava and Athens sky type frequencies are thus compared in Figs. A6.3 and A6.4 and Figs. A6.5 and A6.6, where high-frequency peaks are shown in the overcast range in Bratislava, with almost the opposite prevalence and extremely high peaks in the clear sky range in Athens.

Fig. A6.4

Situations with sun documenting clear and partly cloudy skies in Bratislava

Fig. A6.5

Situations without sun represent cloudy and overcast skies in Athens

Fig. A6.6

Situations with sun documenting prevailing clear skies in Athens