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

An initial guess is needed for the parameters before solving the least squares. Here B u = 0
and s = 30 are held constant but B m and z m are found from the critical backscatter method.
In Figure 11 is seen an example of the idealized profile fitted to the measured aerosol
backscatter values from the ceilometer. There is a close resemblance between the two profiles,
as the conditions for BLH detection are ideal. Notice though, that equation 25 is not based
on physical considerations, but merely applied on observational grounds. This method is fairly
easy to implement in automatic detection algorithms, but is computationally heavier than the
previously mentioned methods. Smoothing of the aerosol backscatter profile before fitting
with the idealized profile does not improve the results of the method.
4.4 Addition to the idealized profile
The observed backscatter profile in Høvsøre often differs from the idealized profile, especially
when the winds are westerly. Then the observed profile generally shows higher backscatter
values near the surface. This may be because the aerosols (in this case marine aerosols),
close to the surface are heavier than those immediately above, and larger aerosols give higher
backscatter values. Another reason could be that the near-surface air is more humid.
To make a better fit to the observed profile, an additional exponential term to the idealized
profile is suggested in this study.
B(z) = B m + B u
2
− B m − B u
erf
2
( z − zm
s
)
+ a exp(−bz) (26)
where a is the initial value, and b the growth factor of the exponential function.
Under certain circumstances this improved profile fits the aerosol profile better than the
idealized profile, resulting in a more robust BLH detection. This is seen in Figure 12, where
a fitted idealized profile is shown, both with and without the added exponential term. The
BLH estimate with the exponential term is ∼150 m higher than the estimate without. The
thickness of the EZT is reduced considerably by the new method, where s is found to be less
than half of the value fitted by the idealized profile.
The addition of the exponential term makes the non-linear curve fitting algorithm computationally
more intense, with two additional parameters to fit. A great improvement is done in
the fitting process by defining lower and upper bounds on all fitted parameters. This minimizes
misfits of the profile and speeds up the procedure. The method will be called the exponent
idealized profile.
4.5 Filtering clouds
In the available ceilometer data, clouds are very frequently observed. Cloud droplets give very
high backscatter values, often more than 100 to 1000 times stronger than backscatter values
in clear sky conditions. These high values dominate the backscatter profile as high peaks and
the boundary-layer structure is not visible due to these peaks. The BLH detection methods
are not always able to identify the BLH in cloudy conditions and they automatically detect
the BLH at the top or the bottom of the cloud. This may cause problems in automated BLH
detection programs, when the cloud is well above the ABL as seen in Figure 13.
Here is presented a new method to filter the clouds to gain better control of BLH detection
in cloudy conditions. Backscatter signals greater than 1000/(10 8 m srad) are interpreted as
indicating clouds, fog or precipitation, and therefore filtered (or removed). The neighbouring
backscatter values to the cloud, β(z cloud + ∆z), β(z cloud + 2∆z),β(z cloud − ∆z), β(z cloud −
2∆z), are also removed regardless of their value. Three different methods are used to fill in
the ’gap’ of removed β values.
1. Every data point in the gap is set to zero.
DTU Wind Energy Master Thesis M-0039 25