Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10292390 | Journal of Wind Engineering and Industrial Aerodynamics | 2011 | 10 Pages |
Abstract
Many studies have shown that the synthetic aperture radar (SAR) approach is an adequate tool for wind assessment and mapping in coastal and offshore areas. However, a large SAR scene sample is required to precisely calculate wind statistics. The proposed sampling algorithm, called strategic sampling, is based on a long term data series (e.g. QuikSCAT) from a nearby location to accurately evaluate the wind statistics with a reasonable number of wind observations (<45). Strategic sampling methodology relies on the complementarity of two databases: the SAR satellite RADARSAT-1 and the QuikSCAT scatterometer were chosen. Random sampling, on the other hand, requires 75 and 225 wind observations to estimate, respectively, the average wind speed and mean power output of a modern wind turbine, with an error less than 10% for a 90% confidence level. This represents a difference of at least two to seven times more SAR scenes for random sampling than for strategic sampling. The SAR sample selected via strategic sampling at a specific offshore location can then be used for wind predictions in neighboring regions. Finally, the strategic sampling algorithm is used to estimate the minimum number of SAR images for a coastal region located in eastern Canada.
Keywords
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
Philippe Beaucage, Gaƫtan Lafrance, Julie Lafrance, Julien Choisnard, Monique Bernier,