Uppsala universitet, Institutionen för geovetenskaper Examensarbete E, 30 hp i Meteorologi
Examensarbete vid Institutionen för geovetenskaper ISSN 1650-6553 Nr 202 Tryckt hos Institutionen för geovetenskaper,
Geotryckeriet, Uppsala universitet, Uppsala, 2010.
Examensarbete vid Institutionen för geovetenskaper ISSN 1650-6553 Nr 202
Making remote sensing bankable – cold climate SODAR and meteorological mast intercomparison
Ian Engblom Wallberg Ian Engblom Wallberg
Making remote sensing bankable – cold climate SODAR and
meteorological mast intercomparison
Abstract
The value of an easy to install, accurate, far-reaching measurement instrument is easily understood when dealing with setting up and monitoring wind power parks.
However the use of remote sensing equipment when doing this is a subject of much discussion amongst professional experts and scholars.
This is especially the case when the environmental conditions are difficult, such as areas with complex terrain or cold climate (or both), for example forested, hilly or mountainous regions in north Europe and North America.
The remote sensing technology SODAR provides for detailed 3-dimensional datasets, but need a skilled analyst to make sure the measurements are realistic.
This thesis is aimed at verifying the accuracy of the SODAR wind measuring equipment at two potential wind power parks in Sweden.
It has been done by comparing the wind speed, wind direction and turbulence measured by an AQ Systems AQ500 Wind Finder SODAR to the wind speed, wind direction and turbulence measured by in situ anemometers mounted in nearby meteorological masts. The comparison was made by calculation of statistical parameters such as correlation and root mean square error.
The results of this investigation shows that the differences in the measured quantities are site specific and that it’s very difficult to distinguish between differences arising from ambient conditions, the measurement method and the method of calculating comparable values. It is clear, however,
that conditions such as temperature, inhomogeneous fetch and wind speed are contributors to the observed discrepancies. When compensating for
these various sources of error; such as eliminating data from malfunctioning equipment, icing conditions, mast wake and forest flow disturbance, the SODAR wind speed data shows a very high correlation with the mast anemometer data, giving a correlation coefficient of around 0.90 – 0.95.
Keywords: SODAR, remote sensing, wind power, wind energy
