CALIPSO: Data User's Guide - Data Product Descriptions - Lidar Level 2 5 km Vertical Feature Mask (VFM) Version 3.x Product
The CALIPSO Level 2 lidar vertical feature mask data product describes the
vertical and horizontal distribution of cloud and aerosol layers observed by
the CALIPSO lidar. Each range bin in the Lidar Level 0 data is characterized by
a single 16-bit integer, with the various bits in the integer representing
flags that describe some aspect of the data measured within the bin.
Instructions on how to decode these integer data are given in the sections
below. Also provided are quality summaries for each classification reported.
The data are recorded in nominal increments of 15 consecutive laser pulses,
which is nominally equivalent to a distance of 5-km along the laser
Standard data processing begins immediately upon delivery of all
required ancillary data sets. The ancillary data sets used in standard
processing (e.g., GMAO meteorological data from the National Snow and
Ice Data Center) must be spatially and temporally matched to the
CALIPSO data acquisition times, and thus the time lag latency between
data onboard acquisition and the start of standard processing can be
on the order of several days. The data in each data set are global,
but are produced in files by half orbit, with the day portion of an
orbit in one file and the night portion of the orbit in another.
Expedited Data Sets:
Expedited data are processed as soon as possible after following
downlink from the satellite and delivery to Langley Research Center
(LaRC). Latency between onboard acquisition and analysis expedited
processing is typically on the order or 6 to 28 hours. Expedited
processing uses the most recently current available set of ancillary
data (e.g., GMAO meteorological profiles) and calibration coefficients
available, which may lag the CALIPSO data acquisition time/date by
Expedited data files contain at the most, 90 minutes of data.
Therefore, each file may contain both day and night data.
NOTE: Users are strongly cautioned
against using Expedited data products as the basis for research
findings or journal publications. Standard data sets only should
be used for these purposes.
In the text below we provide brief descriptions of individual data fields
reported in the CALIPSO vertical feature mask product. Where appropriate,
we also provide an assessment of the quality and accuracy of the data in the
current release. The data descriptions are grouped into several major
categories, as follows:
Latitude, in degrees, of the laser footprint. One value is reported at
the temporal midpoint of each 15 shot data segment.
Longitude, in degrees, of the laser footprint. One value is reported at
the temporal midpoint of each 15 shot data segment.
Profile Time (TAI) (external)
Time expressed in
International Atomic Time (TAI). Units are in seconds,
starting from January 1, 1993. One value is reported at the temporal
midpoint of each 15 shot data segment.
Profile Time (UTC) (external)
Time expressed in
Universal Time (UTC), and formatted as 'yymmdd.ffffffff', where 'yy'
represents the last two digits of year, 'mm' and 'dd' represent month and
day, respectively, and 'ffffffff' is the fractional part of the day. One
value is reported at the temporal midpoint of each 15 shot data segment.
Day-Night Flag (external)
Indicates the lighting conditions at an altitude of ~24 km above mean sea
level: 0 = day, 1 = night.
Land-Water Mask (external)
This is a 30 arc second resolution land/water mask provided by the
SDP toolkit. The data is stored as an 8-bit integer.
The values indicate the surface type at the lidar footprint, and are
interpreted as follows:
For each layer detected in the CALIPSO backscatter data, we derive a set of
feature classification flags that report (a) feature type (e.g., cloud vs.
aerosol vs. stratospheric layer); (b) feature subtype; (c) layer ice-water
phase (clouds only); and (d) the amount of horizontal averaging required
for layer detection. Quality assessments are also provided for all layer
classifications. The complete set of flags is stored as a single 16-bit
integer. The following table is reproduced from the CALIPSO Data Products
Horizontal averaging required for detection (provides a
coarse measure of feature backscatter intensity)
0 = not applicable
1 = 1/3 km
2 = 1 km
3 = 5 km
4 = 20 km
5 = 80 km
User notes for the feature classification flags.
Bits 1-3, Feature Type
Invalid (bad or missing data)
Invalid features are defined as those layers for which the 532 nm
integrated attenuated backscatter, γ′532, is
less than zero. In version 3 of the CALIPSO Level 2 data products,
invalid layers will only occur in those very rare cases when (a) a
layer is detected at one of the coarser averaging resolutions (i.e.,
20 km or 80 km) and (b) above this layer there are also overlying
layers detected at finer resolutions (typically 5 km) that have large
and widely varying optical depths. The non-uniform attenuation
corrections applied to the backscatter signal within the lower layer
can result in an unfavorable redistribution of signal magnitudes;
e.g., large corrections applied to profiles with a substantial
fraction of negative attenuated backscatter values, and smaller
corrections applied to profiles with all positive values. In
especially perverse cases, γ′532 for the
rescaled and reaveraged data can be negative, even though prior to
rescaling γ′532 was comfortably above zero.
Regions are where the signal has not been totally attenuated by
overlying layers and in which no feature is detected are classified
as being clear air. Because the determination of clear air depends
not only on the cloud/aerosol content of the atmosphere, but also on
the minimum detectable backscatter of the CALIOP layer detection
scheme, regions containing especially weak layers can, on occasion,
be misclassified as clear air. Detection threshold issues are
addressed in detail in the CALIPSO
layer detection ATBD (PDF).
Cloud / Aerosol
The V3 cloud aerosol discrimination (CAD) algorithm uses the
altitude-and-latitude-dependent feature integrated color ratio,
χ′, the layer-integrated volume depolarization ratio,
δv, and the feature mean attenuated backscatter
coefficient, <β′532>, to compute the CAD
score. These parameters depend on the quality of the
532 nm and 1064 nm channel calibrations.
Significant errors in the calibration of either channel may result in
the misclassification of a particular feature.
The probability distribution functions (PDFs) of χ′ vs.
δv vs. <β′532> for clouds
and aerosols that are used by the V3 CAD algorithm were developed
based on a four month test data set. These PDFs are binned by
altitude (1 km increments between 0 km and 20 km) and latitude
(between 90°S and 90°N in 10° increments). Despite the
use of these updated 5D PDFs, which significantly enhance overall
V3 CAD algorithm (PDF) may continue to have some
difficulty correctly classifying moderately dense dust and smoke
layers presented in high latitudes and/or high altitudes as aerosol.
Users should also be aware that clouds embedded within optically
dense aerosols may be identified by the feature finder algorithm as a
single layer. While this happens less in version 3 than in the
earlier releases, these misidentified heterogeneous features will
likely be classified as clouds.
All layers with base altitudes lying above the tropopause are
classified as stratospheric features. The correctness of this
classification thus depends critically on the accuracy of the
tropopause heights obtained from the
GMAO GEOS-5 meteorological data set. Because base
height is the sole criterion used in determining stratospheric
features, there are occasions when high altitude cirrus clouds and/or
overshooting tops from deep convection are incorrectly classified as
being stratospheric layers. In uncertain situations, users are
advised to query the feature type QA bits (see below) and refer to
the measured optical properties reported in the aerosol layer
In the polar regions where polar stratospheric clouds (PSCs) are
observed, there may be times when stratospheric layers are
misclassified as cloud. This typically happens when the base of a PSC
drops below the GMAO-reported tropopause or when a PSC is vertically
adjacent to a cloud system in the troposphere.
All regions lying directly below the
lidar surface elevation (i.e., surface echoes
detected by the CALIOP layer detection algorithm) are classified as
Cloud phase is determined from relations between depolarization ratio,
backscatter intensity, temperature, and attenuated backscatter color
ratio. The cloud phase algorithm used in Version 3 is new and
completely different from that used in Version 2. The Version 3
algorithm classifies cloud layers as water, randomly-oriented ice, or
horizontally-oriented ice. In those cases where the classification is
ambiguous, the phase is reported as "unknown/not determined".
The classification of "mixed phase cloud" has been
eliminated; the version 3 algorithm does not attempt to determine if
more than one phase is present within a layer. The version 3 algorithm
distinguishes between two separate classes of ice clouds: those
dominated by randomly oriented particles, and those containing a
substantial fraction of horizontally oriented crystals.
Bits 8-9, Ice/Water Phase Quality Assessment
Changes in the interpretation of the ice-water phase QA flags between version 2 and
version 3 are described in the table below.
Version 3 Interpretation
Version 2 Interpretation
phase based on temperature only
Bits 10-12, Feature Sub-type
The selection scheme uses the observed backscatter strength and
depolarization to identify aerosol type, to the extent possible, from
among one of the six types. The volume depolarization is directly
related to the hydration state of the aerosol. The backscatter and
volume depolarization are not sufficient to fully constrain the model
selection, however. Therefore, the selection algorithm uses surface
type to aid in the type identification. The input parameters - the
magnitude of attenuated backscatter, altitude, location, surface type,
depolarization ratio, and mean attenuated backscatter coefficient
measurements - are used to identify the type following one of several
pathways. The volume depolarization ratio is used to identify aerosol
types that have a substantial mass fraction of non-spherical particles,
e.g., a mixture of smoke and dust. The integrated attenuated
backscatter (γ′) is used to discern instances of
transient high aerosol loading over surfaces where this is not usually
expected, e.g., a smoke or dust layer over the ocean. For aerosols in
polar regions, the algorithm takes into consideration the high aerosol
loading events caused by arctic haze. Once the type is identified, the
aerosol lidar ratio, Sa is chosen from a lookup table that
currently consists of six pairs of 532 nm and 1064 nm values.
In summary, the algorithm classifies aerosol layers that have volume
depolarization ratio (δv) greater than 0.2 as
desert dust and 0.075 < δv < 0.2 as
polluted dust. Note that polluted dust could be a component of urban
pollution, i.e., it is not confined to desert regions but is any type of
aerosol composed of some dust-like particles. Of the non-depolarizing
aerosols, layers lofted above 1 km are assumed to be smoke, and layers
less than 1 km above the surface are either clean continental if the
layer IAB is small or polluted continental if the layer IAB is large.
This flag classifies the cloud layers detected by CALIPSO into the
standard meteorological cloud types defined by the
ISCCP). The classification decisions are based on
CALIOP measurements of cloud top altitude, assessments of cloud
ice/water phase, cloud opacity, and cloud fraction within an 80 km segment.
Bit 13, Cloud / Aerosol / PSC Subtype Quality Assessment
Bits 14-16, Horizontal Averaging
Specifies the amount of horizontal averaging required for a feature to
be detected. For all data versions up to and including 3.01 release,
the values decoded from the bits in this field will be either 1/3 km, 1
km, 5 km, 20 km, or 80 km.
Layout of the Feature Classification Flag data block
The Feature_Classification_Flag values are stored as a sequence of 5515
element arrays (i.e., as an N x 5515 matrix, where N is the number of
separate records in the file). Each array represents a 5 km
"chunk" of data, and each array element contains the feature
classification information for a single range resolution element in the
Level 0 lidar data downlinked from the satellite. As shown in the table
below, the vertical and horizontal resolution of the CALIPSO data varies
as a function of altitude above mean sea level (see
Hunt et al., 2009). The image above provides a
pictorial representation of the mapping of the one-dimensional array of
Feature_Classification_Flag values into a two-dimensional array of
range-resolved lidar data samples. The numbers in each block of the image
indicate the 1-D array indices associated with each spatial averaging
regime in the 2-D lidar backscatter data. Only the starting and ending
indices are shown.
Example code for transforming a 1-D array of feature classification
flags into a rectangular, altitude-registered matrix is available for
an 80-byte (max) character string specifying the data product name. For all
CALIPSO Level 2 lidar data products, the value of this string will be
Date Time at Granule Start
a 27-byte character string that reports the date and time at the start of
the file orbit segment (i.e., granule). The format is yyyy-mm-ddThh:mm:ss.ffffffZ.
Date Time at Granule End
a 27-byte character string that reports the date and time at the end of the
file orbit segment (i.e., granule). The format is yyyy-mm-ddThh:mm:ss.ffffffZ.
Date Time at Granule Production
This is a 27-byte character string that defines the date at granule
production. The format is yyyy-mm-ddThh:mm:ss.ffffffZ.
Lidar L1 Production Date Time
For each CALIOP Lidar Level 2 data product, the Lidar Level 1 Production
Date Time field reports the file creation time and date for the CALIOP
Level 1 lidar data file that provided the source data used in the Level 2
Number of Good Profiles
This is a 32-bit integer specifying the number of good attenuated backscatter
profiles contained in the granule.
Number of Bad Profiles
This is a 32-bit integer specifying the number of bad attenuated backscatter
profiles contained in the granule.
The Version 3.30 CALIOP Lidar Level 1, Level 2, and Level 3 data products
incorporate the updated GMAO Forward Processing – Instrument Teams (FP-IT)
meteorological data, and the enhanced Air Force Weather Authority (AFWA) Snow
and Ice Data Set as ancillary inputs to the production of these data sets,
beginning with data date March 1, 2013.
Impacts on CALIOP data products caused by the transition to GEOS-5 FP-IT are
predicted to be minimal, based on a comparison of CALIOP Version 3.02 against
CALIOP Version 3.30, summarized below. Additional details are given in the
Impacts of Change in GEOS-5 Version on CALIOP Products (PDF).
GEOS-5 Changes and CALIOP Impact Summary:
Level 1B nighttime calibration:
GEOS-5 molecular number densities in the CALIOP nighttime calibration region
increased by roughly 0.6% on average which caused the nighttime calibration
coefficients to decrease on average by -0.6%. Since attenuated backscatter
is inversely proportional to the calibration coefficient, nighttime attenuated
backscatter will increase by 0.6% on average.
Level 1B daytime calibration:
GEOS-5 molecular number densities in the CALIOP daytime calibration region
increased by 0.1% near the equator and increased by up to 0.4 - 0.7% near the
poles which caused daytime calibration coefficients to decrease by
<-0.2% near the equator and decrease by roughly -0.8% near the poles.
Daytime attenuated backscatters will thereby increase by these same
Level 2 layer detection:
GEOS-5 molecular number densities increased in the CALIOP night and day
calibration regions subsequently increasing night and day attenuated
backscatters, causing the number of layers detected to increase slightly.
For the two months examined, the number of aerosol and cloud layers increased
by < 0.8% and < 0.2%, respectively.
Level 2 layer classification:
GEOS-5 tropopause height decreased by ∼1 km at 30°S and 40°N and
decreased by 1.5 km over the Antarctic in September 2011. Since CALIOP
classifies layers detected above the tropopause as stratospheric features,
about 3 - 5% of stratospheric features were instead classified as either
cloud or aerosol. These changes are considered minor except in Sep. 2011
over the Antarctic where a 1 - 1.5 km reduction in tropopause height caused
100% of cloud and aerosol layers to be re-classified as stratospheric
features. This latter effect may occur seasonally over the Antarctic.
Level 3 aerosol extinction and aerosol optical depth:
GEOS-5 molecular number densities increased by small amounts in the CALIPSO
calibration regions and by smaller amounts at other altitudes, slightly
increasing the number of aerosol layers detected and increasing their
attenuated backscatter. Consequent small increases in aerosol extinction and
aerosol optical depth are much smaller than uncertainties in these
The CALIPSO Team is releasing Version 3.02 which represents a transition of the Lidar, IIR, and WFC processing
and browse code to a new cluster computing system. No algorithm changes were introduced and very minor changes
were observed between V 3.01 and V 3.02 as a result of the compiler and computer architecture differences.
Version 3.02 is being released in a forward processing mode beginning November 1, 2011.
No new parameters have
been added for the version 3 VFM product. However, the interpretation assigned
to the bits describing cloud ice-water phase has changed slightly from version
2. These changes are described in detail below.
The sections below highlight important changes to the layer detection, scene
classification, and extinction algorithms that have implications for the
overall quality of the Lidar Level 2 data products.
Version 3.01 of the Lidar Level 2 data products is a significant improvement
over previous versions. Major code and algorithm improvements include
the elimination of a vicious, vile, and pernicious bug in the cloud
clearing code that caused a substantial overestimate of low cloud
fraction in earlier data releases (details given in
Vaughan et al., 2010 (PDF));
enhancements to the cloud-aerosol discrimination algorithm that increase
the number of diagnostic parameters used to make classification decisions
(details given in Liu et al., 2010 (PDF));
improved daytime calibration procedures, resulting in more accurate
estimates of layer spatial and optical properties (details given in
Powell et al., 2010 (PDF)); and
an entirely new algorithm for assessing cloud thermodynamic phase (details given in
Hu et al., 2009).
As in previous versions, the layer boundaries reported in the Lidar Level 2
Cloud and Aerosol Layer Products appear to be quite accurate. Some false
positives are still found beneath optically thick layers; these, however, can
generally be identified by their very low CAD
scores (e.g., |CAD score| ≤ 20).In opaque layers, the lowest
altitude where signal is reliably observed is reported as the base. In
actuality, this reported base may lie well above the true base. Opaque layers
are denoted by an opacity flag. In this
release, the layers which are reported represent a choice in favor of high
reliability over maximum sensitivity. Weakly scattering layers sometimes will
go unreported, in the interest of minimizing the number of false positives.
Figure 1A (below) compares the distributions of CAD scores derived from four
months of version 3 test data to the corresponding version 2.01 data. The V3
curve shows a smoother distribution and generally has fewer low CAD values
(i.e., values less than ~|95|), reflecting the better separation of clouds
and aerosols when using the version 3 5-D PDFs as compared to the separation
provided by 3-D PDFs in previous versions. One notable exception to this
observation is the bump between -10 and 20 in the V3 test curve, which
accounts for ~ 6% of the total features. The CAD scores in this region
identify both outlier features whose optical/physical properties are not
correctly measured or derived, and those features whose attributes fall
within the overlap region between the cloud and aerosol PDFs. In contrast,
these outliers are populated over the entire CAD span in the V2 release.
Figure 1A: Histograms of CAD scores for Version 2 (red) and Version 3 (blue)
Figure 2A (below) presents the relationship between the CAD score and the
layer IAB QA factor, which provides a
measure of the integrated attenuated backscatter overlying a cloud or an
aerosol layer. A layer IAB QA factor close to 1 indicates that the atmosphere
above the layer under is clear. Decreasing values indicate the increasing
likelihood of overlying layers that have attenuated the signal within the
layer under consideration, and thus decreased the SNR of the measurement. A
layer IAB QA factor of 0 would indicate total attenuation of the signal. As
seen in the figure, the IAB QA is highest for high magnitude CAD scores and
slopes down gradually for small CAD score magnitudes. This relationship
reflects the fact that the presence of overlying features tends to add
difficulty to the cloud-aerosol classification task, and therefore reduces
the confidence of the classifications made. The dip between -10 and 20
represents features that are outliers in the 5-D CAD PDFs, and indicates that
these outliers most often lie beneath other relatively dense features. The
cloud layers with special CAD scores (103 and 104) have the smallest IAB QA
values. The relatively big value at CAD = 0 corresponds to the features
having zero CAD values at high altitudes where the probability of the
presence of overlying features is low. At high altitudes the separation of
clouds and aerosols is not as good as at low altitudes because of the
presence of subvisible cirrus clouds.
Figure 2A: Relation between CAD score and Layer IAB QA Factor
Overall, because of the better separation between clouds and aerosols in the
5D space, the 5D CAD algorithm significantly improves the reliability of the
CAD scores. The improvements include:
Dense aerosol layers (primarily very dense dust and smoke over and
close to the source regions), which are sometimes labeled as cloud in the
V2 release, are now correctly identified as aerosol, largely because of
the addition of the integrated volume depolarization ratio to the
diagnostic parameters used for cloud-aerosol discrimination. In addition,
in the open oceans, dense aerosols that were previously classified as
clouds are now frequently observed in the marine boundary layer.
Improvements are also seen for these maritime aerosols. Note, however,
dense dust/smoke layers found at single-shot (0.333 km) resolution will
be classified as cloud by default. This issue will be revisited for
Because the V2 CAD algorithm used a latitude-independent set of 3D
PDFs, a class of optically thin clouds encountered in the polar regions
that can extend from the surface to several kilometers were sometimes
misclassified as aerosols. In version 3, these features are now correctly
classified as cloud.
Correct classification of heterogeneous layers is always difficult. An
example of a heterogeneous layer would be an aerosol layer that is
vertically adjacent to a cloud or contains an embedded cloud, but which
is nonetheless detected by the feature finder as a single entity in the
V2 release. By convention, heterogeneous layers should be classified as
clouds. The version 3 feature finding algorithm has also been improved
greatly, and can now much better separate the embedded or adjacent
single-shot cloud layers from the surrounding aerosol. This improvement
in layer detection contributes significantly to the improvement of the
Some so-called features identified by the layer detection scheme are
not legitimate layers, but instead are artifacts due to the noise in the
signal, multiple scattering effects, or to artificial signal
enhancements caused by non-ideal detector transient response or an over
estimate of the attenuation due to overlying layers. These erroneous
"pseudo-features" are neither cloud nor aerosol and are
distributed outside of the cloud and aerosol clusters in the PDF space.
The V3 CAD algorithm can better identify these outlier features by
assigning a small CAD score (the bump between -10 and 20 in the V3 CAD
histogram) and classify most of them as cloud by convention. A CAD
threshold of 20 can effectively filter out these outliers.
Some misclassification may still occur with the 5D algorithm. For example,
dust aerosols can be transported long distance to the Arctic. When moderately
dense dust layers are occasionally transported to high latitudes, where
cirrus clouds can present even in the low altitudes, they may be
misclassified. This is also the case for moderately dense smoke aerosols
occasionally transported to the high latitudes. Smoke can be mixed with ice
particles during the long range transport, which makes the smoke
identification even more difficult. When moderately dense dust and smoke are
transported vertically to high altitudes, even at low latitudes,
misclassifications can occur due to the presence of cirrus clouds. Volcanic
aerosol that is newly injected into the high altitudes may have a large
cross-polarized backscatter signal and thus may be misclassified as cloud.
The main objective of the aerosol subtyping scheme is to estimate the
appropriate value of the aerosol extinction-to-backscatter ratio
(Sa) to within 30% of the true value. Sa is an
important parameter used in the determination of the aerosol extinction and
subsequently the optical depth from CALIOP backscatter measurements.
Sa is an intensive aerosol property, i.e., a property that does
not depend on the number density of the aerosol but rather on such physical
and chemical properties as size distribution, shape and composition. These
properties depend primarily on the source of the aerosol and such factors as
mixing, transport, and in the case of hygroscopic aerosols, hydration.
The extinction products are produced by first identifying an aerosol type
and then using the appropriate values of Sa and the multiple
scattering factor, η(z). Note that multiple scattering
corrections have not yet been implemented for the current data release, so
that η(z) = 1 for all aerosol types. The accuracy of the
Sa value used in the lidar inversions depends on the correct
identification of the type of aerosol. In turn, the accuracy of the
subsequent optical depth estimate depends on the accuracy of Sa.
The underlying paradigm of the type classification is that a variety of
emission sources and atmospheric processes will act to produce air masses
with a typical, identifiable aerosol 'type'. This is an idealization, but one
that allows us to classify aerosols based on observations and location in a
way to gain insight into the geographic distribution of aerosol types and
constrain the possible values of Sa for use in aerosol extinction
The aerosol subtype product is generated downstream of the cloud-aerosol
discrimination (CAD) scheme and, therefore, depends on the cloud-aerosol
classification scheme in a very fundamental way. If a cloud feature is
misclassified as aerosol, the aerosol subtype algorithm will identify this
'aerosol' as one of the aerosol subtypes. The user must exercise caution
where the aerosol subtype looks suspicious or unreasonable. Such situations
can occur with some frequency in the southern oceans and the polar regions.
Cloud Ice/Water Phase Discrimination
The cloud phase algorithm used in Version 2 has been replaced with a new,
completely different algorithm. The Version 3 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). These classifications have not yet been
rigorously validated, which is difficult, but many of the obvious artifacts
found in the Version 2 data have been eliminated.
The version 2 algorithm included a rudimentary ability to identify a
specific subset of high confidence instances of HOI. These clouds were
classified as ice clouds, and flagged with a 'special CAD score' of 102,
indicating that they had been further classified as HOI. The new version 3
algorithm implements a much more sophisticated scheme for recognizing HOI
that correctly identifies many more instances of these sorts of ice clouds.
The special CAD score of 102 is no longer used to identify these layers.
Instead, the "ice cloud" and "mixed phase cloud"
classifications have been eliminated, and replaced as shown in the table below.
Version 2 Interpretation
Version 3 Interpretation
randomly oriented ice (ROI)
horizontally oriented ice (HOI)
The Ice/water Phase QA flags have also been redefined slightly for Version
3, as follows:
Version 2 Interpretation
Version 3 Interpretation
phase based on temperature only
A confidence flag of QA=1 indicates the phase classification is based on
temperature. Initial classification tests are based on layer depolarization,
layer-integrated backscatter, and layer-average color ratio. Layers
classified as water with temperature less than -40 C are forced to ROI and
given a confidence flag of QA=1. Layers classified as ROI or HOI with
temperature greater than 0 C are forced to water and also given a confidence
flag of QA=1. Clouds for which the phase is 'unknown/not determined' are
assigned a confidence value of 0 (no/low confidence).
Layers classified as HOI based on anomalously high backscatter and low
depolarization are assigned QA=3. These layer characteristics are rarely
detected after the CALIOP viewing angle was changed to 3° in November
2007. The Version 3 algorithm computes the spatial correlation of
depolarization and integrated backscatter, and uses this as an additional
test of cloud phase. Layers classified as HOI using this test are assigned
QA=2. The spatial correlation test is responsible for the majority of the
layers classified as HOI. These layers typically have higher backscatter than
ROI but similar depolarization, and are common even at a viewing angle of
3°. We interpret this as clouds with significant perpendicular
backscatter from ROI but containing enough HOI to produce enhanced
backscatter. These layers tend to be found at much colder temperatures than
the high confidence HOI (see
Hu et al. 2009).
Cloud and Aerosol Optical Depths
The reliability of cloud and aerosol optical depths reported in the version 3
data products is considerably improved over the version 2 release. Whereas
the version 2 optical depths were designated as a beta quality product, and
not yet suitable for use in scientific publications, the maturity level of
the version 3 optical depths has been upgraded to provisional. Several
algorithm improvements and bugs fixes factored into the decision to upgrade
the maturity level. Among these were the addition of the
aerosol layer base extension algorithm,
which greatly improves AOD estimates
in the planetary boundary layer (PBL), and several significant improvements
to the code responsible for rescaling the attenuated backscatter coefficients
in lower layers to compensate for the beam attenuation that occurs when
traversing transparent upper layers.
Users of the CALIOP optical depths should read and thoroughly understand the
information provided in the
Products Data Quality Summary. This summary contains an expanded
description of the extinction retrieval process from which the layer optical
depths are derived, and provides essential guidance in the appropriate use of
all CALIOP extinction-related data products. Validation and improvements to
the profile products QA are ongoing efforts, and additional data quality
information will be included with future releases.
Last Updated: November 19, 2020
Curator: Charles R. Trepte
NASA Official: Charles R. Trepte