Abstract
Abstract
Supercooled cloud droplets combined with strong wind, can produce heavy
ice accretions on unheated structures. It is called ”in-cloud atmospheric icing”,
and is a well known problem at high elevations in wintertime. Atmospheric
icing on wind turbines is a challenge that has to be considered when erecting
wind turbines at hills and ridges at high latitudes, for example in Norway.
Ice accretion on the turbine blades can reduce the production significantly
and large amounts can stop the turbine entirely. The need of a method to
predict atmospheric icing events is increasing since there is a growing interest
for building wind turbines along the windy coastline of Norway.
In this study we have tested the ability of a mesoscale numerical weather pre-
diction model (Weather Research and Forecasting (WRF) modeling system)
to predict in-cloud atmospheric icing events. The simulations were executed
with a fine spatial resolution for a selected area, and with use of a detai-
led second-moment parameterization-scheme for the microphysical processes.
Two mountains have been used as test sites, Ylläs in Finland and Gamlemsve-
ten in Norway, where measurements of icing are available for selected cases.
The overall results showed a fairly good agreement between the measure-
ments and the simulations for most of the icing events. The experiment at
Ylläs, where accurate measurements of supercooled cloud water were direct-
ly compared to the modeled cloud water content, gave the best results. The
ratio between modeled and measured values was about 1.3 for all the cases
in the finest grid, and about 0.8 in all the cases when the model’s spatial
resolution was decreased by a factor 4.
The results from the experiment at Gamlemsveten are a bit more intricate
to analyze because the modeled ice loads, which is compared to the measu-
rements, is calculated from temperature and wind speed in addition to cloud
water. There are also several uncertainties regarding the comparison between
the calculated and the observed ice loads. The agreement between the mo-
deled and the observed ice loads seems to be best in weather situations with
low stratus clouds containing mostly liquid cloud water. The model seems to
underestimate the icing rate in a period with convective clouds in cold air
masses, when cloud ice is mixed into the cloud.