Development of a PCA-based climatic similarity index to enhance weather file selection criteria for climate-based daylight modelling simulations in tropical climates
Siew Bee Aw, Pau Chung Leng, Yaik Wah Lim · 2026
Read Development of a PCA-based climatic similarity index to enhance weather file selection criteria for climate-based daylight modelling simulations in tropical climates by Siew Bee Aw, Pau Chung Leng, Yaik Wah Lim (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13529801.txt.
Description
Siew Bee Aw, Pau Chung Leng, and Yaik Wah Lim build a smarter way to pick weather files for daylight design in the tropics in a 2026 study. Designers often borrow the nearest weather file, which can mislead sunlight simulations. The team compressed many weather traits into four main components. Those four explained about eighty-nine percent of file differences. Tropical files were then clustered by climate character.
A similarity index based on the components was tested against a Malaysian benchmark city. The new pick gave a seventy-eight percent chance of matching daylight scores within ten percent. Pure distance picks did worse. Across world class A files the protocol reached about eighty-seven percent selection accuracy. The authors say matching sky character beats matching map distance. The method helps sites that lack their own weather station.
Catalog record
| CREATOR | Siew Bee Aw, Pau Chung Leng, Yaik Wah Lim |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41868 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |