Geoscience Reference
In-Depth Information
In the case of LITE data, there is no existing Level 2 cloud product so we will use
the results derived from the local algorithm.
Figure 3.
Correlation between the local method and the operational algorithm of GLAS and CALIOP
lidar against the value of
F
. Considering the GLAS data, the segment that are been considered are the
same that ones used to construct the Figure 1. For the CALIPSO data, the used data come from the
day of the June 26, 2006, at 20:52 GMT.
To compare the cloud classifi cations performed from the previous algorithm and
the operational algorithms of the GLAS and CALIPSO missions, it is necessary to
evaluate the coherence between their respective results. For the CTH of only the upper-
most structure on each lidar shot, we calculated the coeffi cients of correlation between
each operational algorithm and the local method for cloudy scenes observed by GLAS
and CALIOP. Figure 3 shows the results for GLAS and CALIOP according to the val-
ue of
F
. The maximum level of correlation is reached for a value of
F
close to 400 for
both GLAS and CALIOP. These values correspond to the thresholds that we will use
in subsequent analyses. The correlation is high with values of 0.95 and 0.93 for GLAS
and CALIPSO, respectively. The variations observed are related primarily to the non-
detection of cloudy structures associated with low optical thickness (lower than 0.1
at the wavelength of 532 nm) by both GLAS and CALIOP. Indeed, the detection of
the semitransparent scattering structures is less sensitive with GLAS and CALIOP
because the SNR is weaker than that of LITE. The limits of cloud detection that result
from different SNR values have implications for the statistics of global cloud cover
presented later in this chapter.
LIDAR-DERIVED CLOUD TOP HEIGHT
Now that some understanding has been gained of the coherence between the various
algorithms for the identification of cloudy structures, the associated statistical distribu-
tions of the CTHs can be compared.
