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

Chapter 2. Background information and methodology of CALIPSO 22
necessary for an analysis that represents not only the best possible representation for a
spaceborne lidar system, but also a realistic composition of the atmosphere. Using the
333 m product with its prevailing low level dense clouds and combining it with selective
clouds from its 5 km counterpart is in best accordance with the needs. (see Chapter 3
for further information)
2.5 CALIPSO lidar surface return
To understand the great discrepancy of the varying surface returns of the three products
and to derive characteristics from the cloud free occurrence, the global distribution
of both parameters has to be analyzed. While the detection of a feature spike within
the atmospheric column and lidar profile is quite straight forward, the identification of
surface returns is accomplished by using a high-resolution digital elevation map (DEM)
and a model of the measured impulse response function of the lidar receiver. Due to
the attenuation by overlying features, the intensity of the surface spike can be diminished.
Therefore the absolute magnitude of the signal is of less importance whereas the
signal shape becomes more evident. To be correctly detected as a surface return, the
spike must have a shape characteristic for a surface return (defined by the receiver’s
instrument response function) and also has to occur in an altitude regime consistent
with the DEM. The valid altitude range is thereby location-depended, relies on the
uncertainties in satellite position and altitude as well as on the relative error of the
DEM [Vaughan et al., 2006]. Taking these considerations into account, Figure 6 confirms
the inability of CALIOP to retrieve surface signals from a bulk of the oceans,
as well as from highly vegetated regions such as the South American northern- and
eastern-, the Mid-African- and all Indonesian rain forests. Otherwise a surface return
is almost always existent for icy regions and deserts like the Sahara. The underlying
Normalized Difference Vegetation Index (NDVI) world map for January 2007 is a
high resolution (0.1 ◦ x 0.1 ◦ degree) Terra Moderate Resolution Imaging Spectroradiometer
(MODIS) representation of consistent, spatial and temporal global vegetation conditions
(see http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD13A2_M_NDVI
for more details). Due to low backscatter signals from the ocean and highly vegetated
regions, the surface return detection algorithm might fail for a horizontal resolution of
333 m, but due to extensive averaging in the 5 km product a return can be acquired.