Using Satellite Images to Predict Sky Types for Better Daylight Design of Buildings in Sub-tropic South China (416109)(Jan 10 - Apr 12)
• Researchers: NG Yan Yung, TREGENZA Peter
• Funding Amount: HK$670,000
• Fundng source: Research Grants Council - General Research Fund

Daylight data are of great importance for daylight design in buildings. Basic daylight data, such as illuminance data, can be used in simple analysis of design options, while more detailed data, such as sky types are essential to assess the building performance using some simulation tools. Because of the dynamic nature of daylight, it is important for the designers to know the typical sky types under local climate conditions for better daylight design. Traditionally, sky data can be observed by sky scanner that measures the spatial distribution of the luminance and/or radiance of the entire sky automatically. Due to high costs of installation and maintenance, only a few ground stations have been set up to scan the sky all over the world. In addition, researchers proposed methods to predict sky types using some ground meteorological data. Nevertheless, most methods were based on CIE old sky classifications rather than the CIE 15 standard skies, and these methods still relied heavily on the ground measurements and cannot be applied to the region without enough meteorological data. Satellite-based methods to predict sky types can overcome these difficulties as satellite images can cover a large scale area no matter the ground conditions. More importantly, satellite images can directly represent the cloud information on the sky that is essential to estimate the sky types. This study will investigate the relationship between satellite visible channel counts and sky types based on CIE 15 standard skies under subtropical climate conditions. This investigation is based on the geostationary satellite, GOES9, from June 2003 to May 2005. Simultaneous ground measurements are used to derive and validate the satellitebased algorithms from CIE IDMP research class station in The Chinese University of Hong Kong and Hong Kong Observatory. The algorithms developed under local subtropical climate conditions in this study could be adopted elsewhere with the similar climatic situation. This study will also conduct the sensitivity study to simulate the daylighting performance in buildings under sky conditions derived from satellite images compared with standard CIE Overcast Sky model and measured sky types. (CU09161)