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

Chapter 2. Background information and methodology of CALIPSO 17
further averaging to ensure that even the faintest layers can be detected. Therefore the
averaging process described by the ATBD (Algorithm Theoretical Basis Document) and
even the improved version 3 [Liu et al., 2010], removes some highly reflective boundary
layer clouds at the original FOV resolution in order to not completely dominate over
weaker signals. Nevertheless, it is required to filter strong signals (e.g., stratus and fair
weather cumulus) from the weak signals (e.g., thin cirrus clouds or aerosol layers) in the
process of cloud and aerosol discrimination. SIBYL scans the profiles for strong features,
extracts them with their properties such as transmittance and the layer-integrated signal
backscatter and then proceeds with a coarser horizontal averaging scheme of up to
80 km horizontal extent. Those properties are used in the next processing step for the
Scene Classification Algorithms (SCA).
Within this processing operation, layer descriptors are computed. These layer descriptors
provide spatial, temporal and optical characteristics of the feature, as well as a
classification of the feature as either being a cloud or an aerosol using the layer-mean
(layer top to base) attenuated total color ratio X ′ :
X ′ = 〈β′ 1064 〉
〈β 532 ′ (12)
∗〉.
For this purpose the information of both 532 nm channels, the 1064 nm channel, their
backscatter intensity and the depolarization information is used to discriminate the
features even further (e.g., ice cloud or aerosol type).
The process, however, is not
entirely based only on the L1 data, but also on ancillary information from models and
other observations. After the first classification, the cloud-aerosol discrimination (CAD)
algorithm [Liu et al., 2010] is used to distinguish between a variety of layer classes.
The cloud, aerosol and clear air lidar ratios are then handed over to the third and last
processing step: the Hybrid Extinction Retrieval Algorithms (HERA).
Using SIBYL’s and SCA’s outputs, HERA performs extinction retrievals which produce
profiles of particulate backscatter and extinction at both 532nm and 1064nm.
Each
detected layer is thereby checked for its properties, characteristics and surroundings.
Additional parameters like optical depth, assumed particle size and depolarization ratio
are computed and assigned to each detected feature (only for 5 km product). Because
of the need for extensive averaging of some layers, the spatial resolution for each layer
is different, just like the profiles of extinction and backscatter intensities. This effect
is compensated by providing spatially uniform Level 2 data sets for the single shot
resolution of 333 m, an averaged 1 km product and the 5 km product for which all
processing algorithms are performed [Vaughan et al., 2006, Winker et al., 2006].