This page serves as a guide to educate users on how to interpret the browse images as well
as explain all the different types of images that are available.
This section serves to provide enough background information so that some of the terminology used is
familiar to users reading this document. This information is not meant to provide a comprehensive description
of the lidar level 1 calibration or lidar theory. Much of the following information may be found in
section 3.1 of the lidar level 1B ATBD.
CALIOP is a three channel lidar, with detectors that collect 532 nm parallel, 532 nm perpendicular, and 1064 nm
light that is backscattered from molecules and particulates (i.e. aerosols, clouds) in the atmosphere. The image at
right shows a functional diagram of the lidar receiver system. The blocks labeled “Detectors and Electronics” are what
we’ll refer to generically as “detectors”.
Once the signals have been range-scaled, energy-normalized, gain-normalized, and calibrated,
we can refer to the signals from each detector with the following set of
equations, (the equation numbers are the same that are found in the
lidar level 1B ATBD:
(L1B ATBD, eq. 3.13)
(L1B ATBD, eq. 3.14)
where β' is the attenuated backscatter, β is the backscatter coefficient, and T2 is the two-transmittance for the
Image of the total 532 nm attenuated backscatter signal, the sum of the 532 nm parallel
and perpendicular return signals, as shown by the following equation:
(L1B ATBD, eq. 3.12)
The signal strength has been color coded such that blues correspond to molecular scattering
and weak aerosol scattering, aerosols generally show up as yellow/red/orange.
Stronger cloud signals are plotted in gray scales, while weaker cloud returns
are similar in strength to strong aerosol returns and coded in yellows and reds.
Some of you might wonder why we use such a unique color scale, and not some generic color scale that
is provided by Matlab, or IDL. The answer is that CALIPSO has a significantly lower SNR compared to most
ground-based or aircraft instruments, and this color scale has been specifically designed to allow
data users to easily pick out and discern features of interest from the molecular backscatter signal and noise flucuations.
Perpendicular Attenuated Backscatter 532 nm
Image of the 532 nm attenuated perpendicular backscatter signal, as shown by the following equation:
(L1B ATBD, eq. 3.13)
The image is generated using the same color scale as the total attenuated backscatter image.
This image is useful for discerning the difference
between spherical and non-spherical particles. Non-spherical particles (i.e. dust, ice crystals)
will change the polarization state of the backscattered light, while spherical particles such as water droplets or spherical
aerosols will not.
Because the receiver footprint of the lidar is large (~90 m) the contribution from multiple scattered photons can contribute
in a significant way to the lidar return. In water clouds this multiple scattering changes the polarization state of the
backscattered light, and this signature increases as a function of penetration depth. So, it is common to see an significant
perpendicular backscatter return from water clouds.
Depolarization Ratio 532 nm
Image of the volume depolarization ratio, the ratio of the 532 nm perpendicular and parallel channels,
as shown by the following equation:
(Feature Finder ATBD, eq. 6.9)
This image is useful for discerning the difference between spherical and non-spherical particles.
Non-spherical particles (i.e. dust, ice crystals) will change the polarization state of the backscattered light,
while spherical particles such as water droplets or spherical aerosols will not.
Ice clouds (i.e. cirrus) generally will exhibit a depolarization ratio in the 0.25-0.40 range, dust aerosols are usually
in the ~0.15 range. Water clouds will exhibit an increase in depolarization ratio as a function of the penetration depth,
this is due to the multiple scattering contribution that was discussed in the previous image.
Total Attenuated Backscatter 1064 nm
Image of the total 1064 nm attenuated backscatter signal, CALIOP does not have separate detectors for
the 1064 perpendicular and parallel return signals. The equation for the 1064 attenuated backscatter is:
(L1B ATBD, eq. 3.14)
The same color scale is used here as in the 532 nm total and perpendicular attenuated backscatter images.
The 1064 nm attenuated backscatter image can be useful for picking out faint (weak) scattering layers because the
molecular backscatter is virtually non-existent. Also, looking for differences in the 532 nm and 1064 nm attenuated backscatter is
useful for inferring information about the particle size. The volume color ratio plot is useful in that regard, see next image (below).
Attenuated Color Ratio
Image of the attenuated color ratio as given by the following equation,
(Feature Finder ATBD, eq. 6.12)
where B is the attenuated backscatter coefficient that has been normalized by the
molecular and ozone two-transmittance through the atmosphere as shown by,
(Feature Finder ATBD, eq. 6.5)
This image is useful for inferring information about the size of the particles in the scattering volume. Because the backscatter
coefficient is smaller at 1064 nm compared to 532 nm for small particles, the color ratio will often be ≤ 1 for aerosol
layers and > 1 for cloud layers.
For layers where there are large differences in the particulate extinction at the two wavelengths, (i.e. biomass burning aerosols)
then the attenuated color ratios can become very large. If,
then the attenuated backscatter at 532 nm becomes very small,
and the attenuated color ratio gets real big,
Attenuated Backscatter with Ancillary Information
The next three images overlay ancillary information onto the 532nm total attenuated backscatter image and
overlies ancillary information to aid in scene characterization and analysis. The first of these plot pressure,
potential temperature, temperature, and tropopause height contours. Pressure, temperature, and tropopause height
are extracted from the Level 1B files, while potential temperature is calculated.
The next two images overlay coincident CloudSat radar reflectivity (top) and cloud mask (bottom) onto the
532nm total attenuated backscatter image. The CloudSat data is taken from the most recent version of the 2B-GEOPROF
data products for both day and night browse images from June 2006 - April 2011, and then only daytime from November 2011
onward. Images are only generated when the CloudSat data is processed and publicly available, so there may be lags of
several months for the more recent images.
Vertical Feature Masks
The following five browse images show what we call a vertical feature mask (VFM)
this is a plot that shows the vertical and
horizontal locations of all layers, or features as we sometime refer to them, in the scene. The color-coding changes depending
on the information being conveyed such as layer type (e.g. cloud or aerosol) or the cloud ice/water phase. The data being displayed
are all available in the lidar level 2 vertical feature mask product, the data set is
All images below use the most recent version of the Lidar Level 2 algorithm. Users are strongly encouraged to refer to
data quality summaries
for these data products.
Even though all these images are vertical feature mask images, this one is colloquially referred to as 'The' vertical feature mask. The
colors in this image depict the type, (cloud, aerosol, stratospheric, surface, etc.) for each layer found by the level 2 feature
This VFM image shows the ice/water phase of all cloud layers in the scene, as such only cloud
layers are shown.
The cloud phase algorithm classifies detected cloud layers as water, randomly-oriented ice (ROI),
or horizontally-oriented ice (HOI) based on relations between depolarization, backscatter, and color ratio
(Hu et al. 2009,
Avery et al., 2020).
This VFM image shows the aerosol type (i.e. output from the aerosol classification algorithm) for all aerosol layers,
as such only aerosol layers are displayed in this image.
This VFM image shows the cloud subtyping (i.e. output from the cloud classification algorithm) for all
cloud layers, as such only cloud layers are displayed in this image.
Last Updated: January 04, 2022
Curator: Charles R. Trepte
NASA Official: Charles R. Trepte