Boundary-layer height detection with a ceilometer at a coastal ... - Orbit

increases from 19:00–20:00. A residual layer is visible from 19:00 at ∼500 m. The four different
BLH detection methods show very similar BLH estimates at ∼1100 m from midnight
to 4:00. The BLH estimates show a generally decreasing height from 4:00–20:00, whereafter
the BLHs are ∼200 m.
All data 24 April 2010
1200 N = 144
1200 N = 144
BLH [m] minimum TKE
1000
800
600
400
200
R = 0.51
Slope = 0.41
RMSD = 301
BLH [m] minimum TKE
1000
800
600
400
200
R = 0.5
Slope = 0.42
RMSD = 297
0
0 500 1000
BLH [m] ideal. profile
0
0 500 1000
BLH [m] exp. ideal. profile
1200 N = 144
1200 N = 144
BLH [m] minimum TKE
1000
800
600
400
200
R = 0.5
Slope = 0.4
RMSD = 307
BLH [m] minimum TKE
1000
800
600
400
200
R = 0.49
Slope = 0.42
RMSD = 299
0
0 500 1000
BLH [m] vertical gradient
0
0 500 1000
BLH [m] critical threshold
Figure 28: Inter-comparison between the BLH methods using the ceilometer and that using the
minimum TKE using the wind lidar on April 24 2010. The dashed line is the linear regression
and the solid line is the 1:1 line.
The TKE per unit mass is shown in Figure 27(b) and indicated by the colour bar, measured
on the same day with the wind lidar. The minimum of every vertical TKE profile is
indicated by a pink circle. From midnight to ∼9:00 the TKE values are relatively low, but
clearly distinguishable minima are found. These minima indicate the BLH and most of them
follow the cut-off value of the wind measurements, though with a few outliers closer to the
surface. The turbulence is strongest from ∼10:00–17:00 showing high TKE values from the
first measurement height (100 m) to the estimated BLH. After 18:00 turbulence generation
decreases and no clear minima are found. The minimum values are scattered between a near
surface layer and a residual layer visible from around 20:00. The estimates in the residual layer
are somewhat artificial, as the TKE profiles are not continuous.
In Figure 28 it is seen that the inter-comparisons all show correlations ∼0.5 and slopes
are all ∼0.4. Although the correlation coefficients are not high they are significant at 99.9%
confidence level. In Table 1 is seen that a correlation of 0.26, where N = 145 is significant
on a 99.9% confidence level. The low-valued correlation coefficients and slopes are due to the
outliers, as most of the data points are seen to lie close to the 1:1 line.
A filtering method is applied to remove the outliers of the data and improve the correlation
40 DTU Wind Energy Master Thesis M-0039