Geoscience Reference
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particle size. Multilayered, overlapping clouds are presently poorly modeled because
their life cycle implies dynamical processes at scales much smaller than those used
in general circulation model (GCM) calculations, and also because of their complex
microphysics (Flatau et al., 1989). A better knowledge of the horizontal and vertical
distribution of all cloud layers is required to improve cloud parameterization in exist-
ing climatic models and better assess their feedbacks. Numerous previous studies have
been performed using spaceborne passive instruments to infer the vertical distribu-
tion of clouds, for example combination of ISCCP and SSM/I (Yeh and Liou, 1983),
NIMBUS 7 (Stowe, 1984), TOVS (Susskind et al., 1987), 3DNEPH and RDNEPH
(Hugues and Henderson-Sellers, 1985), ISCCP (Rossow et al., 1985), combination of
AVHRR and HIRS/2 (Baum et al., 1995), AVHRR (Heidinger and Pavolonis, 2005;
Pavolonis and Heidinger, 2004), MODIS published by Copernicus Publications on
behalf of the European Geosciences Union.
Berthier et al. made comparisons of cloud statistic from spaceborne lidar system
(Baum et al., 2003; Chang and Li, 2005; Nasiri and Baum, 2004), SAGE-II (Kent et al.,
1993), and HIRS/2 (Jin and Rossow, 1997; Wylie et al., 2005). Even with techniques
now available that show some facility in detecting the occurrence of multiple, but
overlapping, cloud layers in passive radiometric data, it is still problematic to infer
the properties of each cloud layer. Lidar offers the opportunity to better determine
the presence of optically thin ice clouds and to detect lower-level stratiform systems
(e.g., Winker et al., 1998). Active measurements may also be used to mitigate biases
in CTHs that arise in complex situations, such as in the polar regions. New spaceborne
backscatter lidar missions (ESA: Ingnann et al., 2008 and http://www.esa.int/export/
esaLP/index.html, NASA: http://www.veg3dbiomass.org/VolzVeg3Dworkshop.pdf)
are currently underway or in preparation to give further insight on the spatial and
vertical distribution of both clouds and aerosols in the troposphere, on a continuous
observational basis as required by the models. However, compared to ground-based
systems, spaceborne lidar systems provide an atmospheric backscattered signal with a
relatively weak signal to noise ratio (SNR), thus requiring signifi cant signal process-
ing (e.g., Chazette et al., 2001).
In this study, we develop and apply a methodology to derive the PDF of cloud
layer structures from lidar profi les obtained during the LITE, (Winker, 1996) mis-
sion in September, 1994. This pioneering mission provides an opportunity to
estimate the cloud spatial distribution with a high spatial resolution under a given
satellite footprint. The PDF retrieved from LITE data are compared with those cal-
culated from the new spaceborne lidar missions, such as GLAS, (Palm et al., 2002)
and CALIOP (Winker et al., 2002). The GLAS and CALIOP data are processed two
ways: (1) with the methodology developed for the LITE profi les and (2) the meth-
odology used for the operational products. For the LITE time period in 1994, we
perform comparisons to the ISCCP cloud products (Rossow and Schiffer, 1991). A
full year of products generated from CALIOP is used to analyze the impact of the
ITCZ latitudinal position and the occurrence of PSCs on the intra-and inter-annual
lidar signal variability.
 
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