Schlussfolgerungen
3.3 Simulation Results
0.005 0 0.015 0.01
0.02
0 0.01 0.02
250 260 270 280 290 300 310
Second level PTG First level PTG
Real temperature [K]
Figure 3.3: Clustering of temperature data of1960∼1970
shows the clustered wind data of one temperature subset by Ward’s method without re-finement byk-mean clustering, and Fig.3.4b shows the case with refinement. The refined result is more reasonable, for example, some of the data objects in cluster No.8 is more close to cluster No.6 before refinement. One problem can be noticed in both Fig.3.4a and 3.4b is that wind with very diversified directions, even complete opposite directions are classified into one group, which is not proper. Therefore, some constraints on the wind direction for distance calculation are imposed on the clustering algorithm, which shows Fig.3.4c.
The final 200 temperature-wind clusters are shown in Fig.3.5a, and the windrose of the 200 wind clusters are shown in Fig.3.5b. As we can see that by taking the centroid of all data objects, the maximum wind speed of the clusters is smaller than the original data in Fig.3.2.
One may also notice that, the windrose is a flip of the clusters in Fig.3.5a, this is because that windrose denotes the originating direction of the wind, whereas in the cluster plot express the wind in the opposite direction as specified before.
-15 -10 -5 0 5 10 15
-20 -15 -10 -5 0 5 10 15 20 25
Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
(a) Clustering with only Ward’s
-15 -10 -5 0 5 10 15
-20 -15 -10 -5 0 5 10 15 20 25
Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
(b) Clustering by Ward’s and refined byk-mean
-15 -10 -5 0 5 10 15
-20 -15 -10 -5 0 5 10 15 20 25
Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
(c) Clustering with wind direction restriction
Figure 3.4: Example cluster results of wind data (The unit of the axes are in m/s. The small circles are the individual data objects, and the big circles stand for the clusters.
The centers of the big circles reflect the cluster center, and the area of circles rep-resent the number of the objects belongs to the corresponding clusters.)
-15 -10 -5 0 5 10 15
-15 -10 -5 0 5 10 15 20 25
270-0.0158-0.0115
274-0.0103-0.0083
280-0.0062-0.0083
277-0.0032-0.0031
293-0.0042-0.0032
271-0.0056-0.0061
280-0.0047-0.0050
272-0.0088-0.0124
289-0.0045-0.0054
287-0.0029-0.0031
(a) Final wind clusters based on wind and temperature
20%
15%
10%
5%
WEST EAST
SOUTH NORTH
0 − 2 2 − 4 4 − 6 6 − 8 8 − 10 10 − 12 12 − 14 14 − 16 16 − 18 18 − 20 20 − 22 22 − 24 (m/s)
(b) Windrose of the wind speed clusters
Figure 3.5: Temperature-wind clusters and windrose of wind speed clusters
shows the distribution of wind energy for each land use type. It shows that for forest and building area which have a bigger surface roughness, the wind is generally smaller, whereas
for grass and agriculture the wind is stronger.
!
!p3 !p2 p1 0 2.5 5 10km
(a) DEM (b) Land use
g
4 4.5 5 5.5 6 6.5 7
(c) Weighted mean wind pattern
20%
15%
10%
5%
WEST EAST
SOUTH NORTH
0 − 5 5 − 10 10 − 15 15 − 20 20 − 25 (m/s)
(d) Windrose P1
20%
15%
10%
5%
WEST EAST
SOUTH NORTH
0 − 5 5 − 10 10 − 15 15 − 20 20 − 25 (m/s)
(e) Windrose P2
20%
15%
10%
5%
WEST EAST
SOUTH NORTH
0 − 2 2 − 4 4 − 6 6 − 8 8 − 10 10 − 12 12 − 14 14 − 16 16 − 18 18 − 20 20 − 22 (m/s)
(f) Windroes P3 Figure 3.6: Wind simulation results for Talhausen
2.5 3 3.5 4 4.5 5 5.5 6 6.5 7
0 0.2 0.4 0.6 0.8 1 1.2 1.4
Probability Density of wind speed at different land use
Composite wind speed (m/s)
Probability density
mixed use grassland moorland bushes mixed forest pine forest building
Figure 3.7: Distribution of wind strength of different land use
Table 3.1 shows the statistical results of correlation between the wind speed resulted from each wind cluster and the three topographic parameters.ρwindis the average correlation co-efficient of wind with the individual topographic parameters, andσwindis the corresponding standard deviation. ρwindis the correlation coefficient of the weighted mean wind with to-pography. In this case, no correlation between wind and any topographic parameters can be detected. Because wind may not show a linear relationship to topographic aspect, wind strength is classified into 8 categories according to the aspect, and the mean wind of different
Table 3.1: Correlation between the terrain parameters and wind aspect slope elevation
ρwind 0.054 0.000 0.140 σwind 0.014 0.030 0.150 ρwind 0.127 0.056 -0.035
aspects are compared. For all days the Null-hypothesis that the mean wind at points with different aspects are the same, is rejected. Fig.3.8 shows the boxplot of the mean daily wind of different aspects. The lower, middle and upper line within the box are the lower quartile (Q1), the median(Q2), and the upper quartile (Q3) respectively. Whiskers extend from the box out to the limits of 1.5 times the interquartile range (IQR). Extreme values out of this range is denotes as crosses. The notches at the median bar indicate the5%significance level of the median. Notches do not overlap each other means that the medians are significantly different.
N NE E SE S SW W NW
4 4.5 5 5.5 6 6.5
Mean wind speed(m/s)
Figure 3.8: Boxplot of wind vs. aspect for mean daily wind
3.3.2 Results for Daily Wind
A continuous simulation fed with daily synoptic wind data of year 2002 is also performed for another bigger area with a resolution of 1000m, the topographic and land use infor-mation of the study area can be found in Chapter 4. Fig.3.9a shows the mean daily wind, which shows a very similar pattern with DEM. The correlation coefficient of mean daily wind speed with DEM is as high as 0.594. The high correlation of wind speed with DEM
is also demonstrated by statistically for multiple days. The DEM dependence of wind force agrees with the results of other statistical study (Winstral et al., 2009) and model simulations (Ayotte, 2008). The missing correlation in the east part in the Talhausen domain, is presum-ably caused by the small size of the domain, which may not resolve the influence of the neighboring terrain. Table 3.2 shows the statistical results of correlation between daily wind
20 40 60 80
10 20 30 40 50 60
70 1.5
2 2.5 3 3.5 4 4.5
(km)
(km) (m/s)
(a) Mean daily wind
N NE E SE S SW W NW
1.5 2 2.5 3 3.5 4 4.5
Mean wind speed(m/s)
(b) Boxplot of wind at locations with different aspects
Figure 3.9: Wind results for continous daily simulation
speed and topographic parameters. Fig.3.9b shows the boxplot of the mean daily wind of different aspects. For this area, wind also demonstrate some influence from aspect.
Table 3.2: Correlation between the terrain parameters and wind aspect slope elevation
ρwind 0.066 0.012 0.302 σwind 0.007 0.011 0.066 ρwind 0.127 0.057 0.594
In this chapter, the the local wind patterns have been mapped with a dynamic downscal-ing approach with mesoscale meteorological model METRAS PC. The statistical wind field show strong spatial difference resulted from topography and land cover modification, which is related by the topography, aspect, and land use. The spatial wind pattern will conse-quently affect hydrological processes, such as ET, precipitation drift, snow accumulation and snowmelt, etc. In the following chapter, we will demonstrate the wind effects on differ-ent hydrological processes.