@MASTERSTHESIS\{IMM2005-03959,
    author       = "N. Cutler",
    title        = "Class generation for numerical wind atlases",
    year         = "2005",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Assoc. Prof. Bjarne Ersb{\o}ll",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3959-full.html",
    abstract     = "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 12-16 sectors, {3-}7 wind speed bins and dividing again on the stability of the atmosphere.

Wind atlases are typically produced from many years of on-site measurements. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in as quickly as a day. 40 years of twice daily {NCEP}/{NCAR} Reanalysis geostrophic wind data (200 km resolution) is represented in typically around 100 classes, each with a frequency of occurrence. The mean wind speeds and directions in each class is used as input data to force the mesoscale model, which downscales to 5 km resolution while adapting to the local topography. The number of classes is to minimise the computational time for the mesoscale model while still representing the synoptic climate features.

Only tried brie y 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 WAsP to produce numerical wind atlases for two sites, Ireland and Egypt. The model results are compared with The New Irish Wind Resource Atlas and wind atlases made from meteorological station measurements in Egypt.

The new clustering method has the ability to include wind data from different heights and thermal stability for the classification. The results show that the clustering method is able to produce results at least equivalent to the existing method results 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 parameter settings."
}