Geoscience Reference
In-Depth Information
METHOD AND SPACEBORNE OBSERVATIONS
Different approaches have been developed to retrieve the CTH from spaceborne lidar
systems. Such approaches have been used for the operational algorithms for GLAS
(Palm and Spinhirne, 1998) and CALIPSO (Cloud-Aerosol Lidar Pathfinder Satellite
Observation), (Vaughan et al., 2004) missions. Based on spaceborne lidar modeling
performed by Chazette et al. (2001), we suggest an alternative methodology to retrieve
the CTH for both semitransparent and dense clouds and apply this methodology to the
LITE data.
Algorithm to Retrieve the Cloud Top Heights
We adapt the method developed by Chazette et al. (2001) to infer the CTHs of scatter-
ing layers in the atmosphere from simulated spaceborne lidar signals with low SNRs
(~3) to actual spaceborne lidar measurements. The method will be called the “Local
Method” hereafter.
To determine the existence of a peak (i.e., a cloud) in a lidar calibrated and at-
tenuated backscatter signal
S
at any altitude level
i
requires an ability to discriminate
between an actual signal and signal noise. Here, the discrimination is performed by
determining a threshold value
F
. The value of
F
is proportional on the signal noise,
which is used to defi ne the variance
Va r
as follows:
2
⎡
⎤
[]
kn
+
Si
−
S
1
21
∑
()
⎢
⎥
(1)
k
=
>
F
Var
⎢
⎥
n
+
σ
⎣
⎦
ikn
=−
N
where (2n+1) is the number of points of the filtering window. A constant size is as-
sumed for the w
in
dows, with n equal to 3, corresponding to a window size equivalent
to 7 pixels. The
S
and σ
N
are respectively the mean value of the detected signal and the
noise standard deviation in an altitude range where only noise is expected to be present
(i.e., between 19 and 20 km height).
Figure 1 provides an example of the determination of
F
for GLAS. This fi gure
gives the mean cloud depth as a function of
F
. The method has been optimized based
on the depth of the scattering layers so that the only cloud structures considered have a
geometrical depth larger than 100 m. This approach attempts to minimize the number
of false alarms.
For a value of
F
in the interval of 1-10, most of the values of
Va r
are greater
than
F
. This means that for an individual altitude level, the noise dominates the
measurement and it is not possible to discern the presence of a cloud. If this situ-
ation occurs at every altitude, the lidar shot is not used in our analysis. As values
of
F
increase between 10 and 1,000, fewer lidar signals are misclassifi ed as cloudy
structures. The misclassifi cations between noise and an actual cloud structure are
further reduced in the case of the LITE data by using a median fi lter. For values of
F
> 1,000, the lidar signal from a cloudy structure is more certain to not be caused
by signal noise.
