| contributor author | Chapman, L. | |
| contributor author | Thornes, J. E. | |
| date accessioned | 2017-06-09T14:37:28Z | |
| date available | 2017-06-09T14:37:28Z | |
| date copyright | 2004/05/01 | |
| date issued | 2004 | |
| identifier issn | 0739-0572 | |
| identifier other | ams-2304.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4159557 | |
| description abstract | Previously, the acquisition of sky-view factor data for climate studies has been time consuming and dependent on postprocessing. However, advances in technology now mean that techniques using fish-eye imagery can be algorithmically processed in real time to provide an instant calculation of the sky-view factor. Although data collection is often limited due to the need to survey under homogenous overcast skies, vast datasets can now be rapidly assembled for the training of proxy ?all weather? techniques. An artificial neural network is used to estimate the sky-view factor using raw global positioning system (GPS) data and is shown to explain over 69% of the variation of the sky-view factor in urban areas. | |
| publisher | American Meteorological Society | |
| title | Real-Time Sky-View Factor Calculation and Approximation | |
| type | Journal Paper | |
| journal volume | 21 | |
| journal issue | 5 | |
| journal title | Journal of Atmospheric and Oceanic Technology | |
| identifier doi | 10.1175/1520-0426(2004)021<0730:RSFCAA>2.0.CO;2 | |
| journal fristpage | 730 | |
| journal lastpage | 741 | |
| tree | Journal of Atmospheric and Oceanic Technology:;2004:;volume( 021 ):;issue: 005 | |
| contenttype | Fulltext | |