Cloud Statistics from Calipso Lidar Data for the ... - espace-tum.de

Chapter 6. Cloud flatness occurrence 58
100 km flat cloud top
exceeding search window
20 km flat cloud top
isolated cloud
40 km flat cloud top
partly flat cloud
HEIGHT
1.42 km
1.39 km
1.36 km
1.33 km
1.30 km
1.27 km
1.24 km
100 km overlap of search window search window with 200 km horizontal extent 100 km overlap of search window
Figure 25: The cloud flatness search algorithm uses a histogram approach with halfoverlapping
200 km search windows to detect flat cloud tops.
Thus, the detection threshold for a 50 km cloud with a flat cloud top in a 200 km search
window is 0.25. As a next step the algorithm computes a cloud top height histogram
with 30 m vertical binning of the clouds within the search window. If a cloud height bin
exceeds the detection threshold, a cloud flatness event will be registered within the search
window. In case there are multiple bins that exceed the threshold, an additional event
is assigned. For the example seen in Figure 25, two events would have been detected
because the height bin of 1.33 km and 1.36 km would have exceeded the threshold of 0.25.
By using the histogram approach, not only consecutive clouds are found, but also broken
cloud scenes are taken into account (see Chapter 4 for examples). This is possible because
cloud free columns are treated apart. The major drawback of the histogram approach
is the inability of the algorithm to distinguish whether the histogram bins belong to
one or more clouds. As a consequence, cloud flatness could also be associated to one
large cloud with a slightly rough cloud top instead of multiple individual clouds. To
exclude the detection of neighboring height bins, an additional vertical layer threshold
can be used. However, such a threshold would also exclude multiple individual flat
cloud tops with similar heights such as in Figure 25. Therefore two analyses have been
performed, one with no additional vertical threshold and one with a minimum vertical
spacing of 120 m, which accounts for slightly rough cloud tops of large clouds. To assess
the global distribution of such cloud flatness events, the rounded latitude and longitude
coordinates of the search window midpoint as well as all event top heights are stored.
All individual measurements that contribute to the cloud flatness event are not retained
and a determination of different cloud layers and their exact position is not forseen.
However, for a basic estimation of the global flatness distribution in location and height,
this approach is sufficient.