Class Generation for Numerical Wind Atlases

• Published in: Wind engineering 2006-10, Vol.30 (5), p.401-415
• Main Authors: Cutler, N. J., Jørgensen, B. H., Ersbøll, B. K., Badger, J.
• Format: Article
• Language: English
• Subjects: Centroids; DISSERTATION PAPER; Froude number; Geostrophic wind; Hymenoptera; Standard deviation; Term weighting; Thermal stability; Wind direction; Wind power; Wind velocity
• ISSN: 0309-524X; 2048-402X
• DOI: 10.1260/030952406779502704

A new optimised clustering method is presented for generating wind classes for mesoscale modelling to produce numerical wind atlases. It is compared with the existing method of dividing the data in 12 to 16 sectors, 3 to 7 wind-speed bins and dividing again according to the stability of the atmosphere. Wind atlases are typically produced using many years of on-site wind observations at many locations. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in a number of hours of computation. 40 years of twice daily NCEP/NCAR Reanalysis geostrophic wind data (approximately 200 km resolution) are represented in typically around 150 classes, each with a frequency of occurrence. The mean wind-speed and direction in each class is used as input data to force the mesoscale model, which downscales the wind to a 5 km resolution while adapting to the local topography. The purpose of forming classes is to minimise the computational time for the mesoscale model while still representing the synoptic climate features. Only tried briefly in the past, clustering has traits that can be used to improve the existing class generation method by optimising the representation of the data and by automating the procedure more. The Karlsruhe Atmospheric Mesoscale Model (KAMM) is combined with the WAsP analysis to produce numerical wind atlases for two sites, Ireland and Egypt. The model results are compared with wind atlases made from measurements at specific sites. The sources are The New Irish Wind Resource Atlas and the Wind Atlas for the Gulf of Suez. The new clustering method has the ability to include wind-speed, direction and thermal stability from different heights for the classification. It is shown that the clustering method is able to produce results at least as accurate as the existing method for both sites. A refined, general clustering procedure is devised which could improve the results for both sites, where the existing method requires two different configurations.