The CALIPSO Cloud and Aerosol Profile Products report profiles of particle
extinction and backscatter and additional profile information (e.g.,
particulate depolarization ratios) derived from these fundamental products.
Layer optical depths are reported in the Cloud and Aerosol Layer Products. The
layer optical depths are derived from the same retrievals that are used to
compute the extinction and backscatter profiles in the profile products. All of
these extinction products are produced using the same basic algorithm (Young
and Vaughan, 2009).
The version 3 CALIPSO data processing system now generates profile products for clouds and
aerosols at the same spatial resolution. The cloud and aerosol profile products are reported at a
uniform spatial resolution of 60-m vertically and 5-km horizontally, over a nominal altitude range
from 20-km to -0.5-km for clouds, and over a broader altitude range of 30-km to -0.5-km for aerosols.
Profile data for all features detected in the stratosphere are reported in the aerosol profile product,
thus users seeking to obtain CALIPSO retrievals of backscatter and extinction profiles of polar
stratospheric clouds are directed to the aerosol profile products. Due to constraints imposed
by CALIPSO's on-board data averaging scheme, the vertical resolution of the aerosol profile data
varies as a function of altitude. In the tropospheric region between 20-km to -0.5-km,
the aerosol profile products are reported at a resolution of 60-m vertically, and in the
stratospheric region (above 20-km), the aerosol profile products are reported at a resolution
of 180-m vertically. In the text below we provide brief descriptions of individual data fields
reported in the CALIPSO cloud and aerosol profile products. Where appropriate, we also provide an
assessment of the quality and accuracy of the data in the current release.
Standard and Expedited Data Set Definitions
Standard Data Sets:
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
several days.
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 cloud and aerosol profile products. 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:
Time, expressed in International Atomic Time (TAI). Units are in
seconds, starting from January 1, 1993. For the 5 km profile products,
three values are reported: the time for the first pulse included in the
15 shot average; the time at the temporal midpoint (i.e., at the 8th of
15 consecutive laser shots); and the time for the final pulse.
Profile UTC
Similar to Profile Time, but for time expressed in
Coordinated
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.
Day Night Flag
Indicates the lighting conditions at an altitude of ~24 km above mean sea
level; 0 = day, 1 = night.
Geodetic latitude, in degrees, of the laser footprint. For the 5 km profile
products, three values are reported: the footprint latitude for the first
pulse included in the 15 shot average; the footprint latitude at the temporal
midpoint (i.e., at the 8th of 15 consecutive laser shots); and the footprint
latitude for the final pulse.
Longitude
Longitude, in degrees, of the laser footprint. For the 5 km profile products,
three values are reported: the footprint longitude for the first pulse
included in the 15 shot average; the footprint longitude at the temporal
midpoint (i.e., at the 8th of 15 consecutive laser shots); and the footprint
longitude for the final pulse.
Optical depth of all clouds, aerosol, or stratospheric layers within a 5
km column. The optical depths are obtained by integrating the 532 nm
cloud/aerosol/stratospheric extinction profile, reported in these profile
products. For aerosols and stratospheric layers the optical depth is provided
at both 532 and 1064 nm wavelengths.
The column optical depths are a column integral product. Any large
uncertainties or poor extinction retrievals from layers within the column
(i.e. clouds, aerosols, or stratospheric features) will propogate downward
and may impact the quality of all (i.e. cloud, aerosol, and
stratospheric) the column optical depths in the column. The following
paragraphs outline notes with regard to specific data products that users
should be aware of when using column optical depth data.
Opacity: We remind data users that the CALIPSO lidar is
only capable of penetrating to the surface if the total column optical depth
is less than ~5. (Note that this value takes into account the contribution of
multiple scattering.) If the column is opaque to the lidar, then the reported
column optical depths are a measure to the apparent base of the lowest
feature observed. Feature opacity can be determined by inspecting the
extinction QC flag for the lowest extinction
coefficient in any 5-km column; if bit 5 (value = 16) is set then the feature
is totally attenuating.
Extinction QC: The extinction QC values in
the column should be examined to determine if any of the extinction
retrievals were bad. Users are reminded that any poor extinction retrievals
in the column may impact the quality of all column optical depths. In
general, solutions where the final lidar ratio is unchanged (extinction QC =
0) or the extinction solution is constrained (extinction QC = 1) yield
physically plausible solutions more often. Conversely, retrievals tend to be
more uncertain in those cases where the lidar ratio for either wavelength
must be reduced.
CAD_Score and
feature sub-type: Features with low absolute CAD_Scores, "special"
CAD_Scores, or uncertain aerosol type classifications may impact the quality
of the column optical depths. For example, if the top-most feature in the
column has a low absolute CAD_Score it is possible that the assigned lidar
ratio may be incorrect; this would impact the extinction retrieval for that
feature which would lead to an incorrect rescaling of all the data below that
feature.
Cloud phase: If there are
clouds in the column that are found to have horizontal oriented ice (HOI)
crystals it is likely that the quality of the column optical depths are low.
The anomalously high backscatter from HOI clouds generally makes the
extinction retrieval more difficult. Because all the data below the HOI cloud
is rescaled by the retrieved optical depth, the extinction data below could
be suspect.
Estimated uncertainty in the column optical depth at each wavelength,
computed according to the formulas give in the
CALIPSO Version 3 Extinction Uncertainty Document (PDF).
Ignoring multiple scattering concerns for the moment, errors in column
optical depth calculations typically arise from three main sources:
signal-to-noise ratio (SNR) within a layer, calibration accuracy, and the
accuracy of the lidar ratio specified for use in the solution. Except for
constrained solutions, where a lidar ratio estimate can be obtained directly
from the attenuated backscatter data, lidar ratio uncertainties are almost
always the dominant contributor to optical depth uncertainties, and the
relative error in the layer optical depth will always be at least as large as
the relative error in the layer lidar ratio.
Calculation of the layer optical depth uncertainty is an iterative
process. On some occasions when the SNR is poor, or an inappropriate lidar
ratio is being used, the iteration will attempt to converge asymptotically to
positive infinity. Whenever this situation is detected, the iteration is
terminated, and the layer optical depth uncertainty is assigned a fixed value
of 99.99. Any time an uncertainty of 99.99 is reported, the extinction
calculation should be considered to have failed. The associated optical
depths cannot be considered reliable, and should therefore be excluded from
all science studies.
Note: optical depth uncertainties are reported as absolute errors, not
relative errors.
The integral with respect to altitude of the 532 nm total attenuated
backscatter coefficients. The limits of integration are from the onset of the
backscatter signal at ~40-km, down to the range bin immediately prior to the
surface elevation specified by the
digital elevation map. This quantity
represents the total attenuated backscatter measured within a column.
Physically meaningful values of the column integrated attenuated backscatter
(hereafter, γ′column) range from ~0.01 sr
(completely clear air), to greater than 1.5 sr (e.g., due to anomalous
backscatter from horizontally oriented ice crystals; see
Hu et al. (Optics Express 15, 2007)).
The cumulative probability of measuring a total column
integrated attenuated backscatter value equal to the value computed for the
current profile. Values in this field range between 0 and 1. The cumulative
probability distribution function, shown below in Figure 1, was compiled
using all CALIOP total column IAB measurements acquired between 15 June, 2006
and 18 October, 2006.
Mean tropopause height, in kilometers above local mean sea level; derived
from the GEOS-5 data product provided to the CALIPSO project by the
GMAO Data
Assimilation System
Tropopause Temperature (external)
Mean tropopause temperature, in degrees C; derived from the GEOS-5 data
product provided to the CALIPSO project by the
GMAO Data
Assimilation System
Surface Winds (external; aerosol products only)
Provides the mean zonal and meridional component of the surface wind speed
computed over the horizontal distance spanned by the averaged profile; units
are meters per second.
Mean temperature, in degrees C, reported for the midpoint of each range bin
in the profile; derived from the GEOS-5 data product provided to the CALIPSO
project by the GMAO Data Assimilation System
Pressure (external)
Mean pressure, in hectopascals, reported for the midpoint of each range bin
in the profile; derived from the GEOS-5 data product provided to the CALIPSO
project by the GMAO Data Assimilation System
Molecular Number Density (external)
Mean molecular number density, in molecules per cubic meter, reported for the
midpoint of each range bin in the profile; derived from the GEOS-5 data
product provided to the CALIPSO project by the
GMAO Data
Assimilation System
Relative Humidity (external)
Mean relative humidity, reported for the midpoint of each range bin in the
profile; derived from the GEOS-5 data product provided to the CALIPSO project
by the GMAO
Data Assimilation System
Provides the minimum, maximum, mean, and standard deviation of the surface
elevation obtained from the
GTOPO30 digital elevation map (DEM) for the horizontal
distance spanned by the averaged profile; units are kilometers.
The fraction of the 5-km horizontally averaged profile, between 30-km and the
DEM surface elevation, which has been
identified as containing a feature (i.e., either a cloud, an aerosol, or a
stratospheric layer)
Samples Averaged
Specifies the number of full resolution samples averaged for each profile
range bin; for the purposes of this computation, 'full resolution' is taken
to mean 30 meters vertically, and a single shot (~1/3-km) horizontally. Thus
a single range bin below an altitude of ~20.2 km (resolution = 60-m vertical,
5-km horizontal) will have at most 480 samples averaged (i.e., for those
layers that required 80-km averaging for detection, 240 shots horizontally by
two 30-m range bins vertically).
Reports the fraction (by area) within each 5 km horizontal * 60 m vertical
range bin containing aerosols or clouds in the profile products. The Aerosol
and Cloud Layer Fractions are conceptually identical and the same procedure
is used to calculate both quantities.
Since the array elements of the profile products can be larger than the
native resolution of the extinction retrieval (5 km * 30 m vs. 5 km * 60 m in
the lower troposphere), and because atmospheric features can be identified at
horizontal resolutions of 1 km and 1/3 km, the atmospheric composition within
each profile range bin is not guaranteed to be homogeneous. Thus, the Aerosol
and Cloud Layer Fractions report the fraction (by area) of each 5 km * 60 m
profile range bin identified as containing aerosols or clouds by the Scene
Classification Algorithms. By referencing Cloud Layer Fraction, the
fractional amount of cloud clearing performed within each profile range bin
of aerosol backscatter and extinction can be determined.
Figure 4: Cloud clearing scenarios for strongly scattering clouds
detected at single shot resolution. Red indicates clouds detected at 1/3
km resolution, blue indicates clouds found at 1 km or coarser resolution,
yellow indicates an aerosol layer found at 5 km resolution, and white
indicates clear air. Scenarios: Clouds embedded in aerosol (upper panel),
clouds embedded in clear air (middle panel), and dense clouds embedded in
within a weakly scattering cloud layer (lower panel). Each row extends 5
km horizontally and 30 m vertically. Each column extends 1/3 km
horizontally.
Shown in Figure 4 are 3 possible scenarios illustrating how the Cloud
Layer Fraction would be reported for each 5 km x 60 m cloud profile range
bin. There are at most 30 single shot (1/3 km x 30 m) cloud layers in each 5
km x 60 m cloud profile range bin - fifteen 1/3 km horizontally and two 30 m
vertically. In the top panel, red indicates clouds found at 1/3 km resolution
and the yellow indicates an aerosol layer found at a 5 km horizontal
resolution after the 1/3 km clouds had been removed. In this case, the cloud
fraction for the top row would be 11/30 = 0.36. In the middle panel, no
features were detected at any coarser spatial resolution after the 1/3 km
features were removed. The Atmospheric Volume Description
for the 5 km average would report the cell as being "clear air",
but the cloud fraction for the top row would still be 11/30 = 0.36. In the
lower panel, a cloud was detected in the data remaining after all 1/3 km
features had been removed. In this case the cloud fraction would be 1 for all
rows shown.
The Aerosol and Cloud Layer Fractions must be values between zero and one,
yet both layer fractions are reported as integers between 0 and 30. For
example, a value for the Aerosol_Layer_Fraction reported as 11 would indicate
a fraction of 11/30 = 0.367.
Atmospheric Volume Description is a profile descriptive flag containing
theFeature Classification Flags
associated with each 5 km x 60 m (or 5 km x 180 m) range bin in the
Profile Products. The Feature Classification Flags provide assessments of (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. Note that the
interpretation of final three bits in the atmospheric volume description
(i.e., the averaging required for detection) is slightly different from the
interpretation that would be used for the feature classification flags. These
differences are summarized in the table below.
Value
Atmospheric volume description
Feature Classification flag
0
not applicable
not applicable
1
5 km
1/3 km
2
20 km
1 km
3
80 km
5 km
4
5 km w/ subgrid feature detected at 1/3 km
20 km
5
20 km w/ subgrid feature detected at 1/3 km
80 km
6
80 km w/ subgrid feature detected at 1/3 km
not used
7, 8
not used
not used
How profile descriptive flags are stored
Atmospheric Volume Description, CAD Score
and Extinction QC [532|1064] are all profile
descriptive flags that are stored in the Level 2 Profile Products in the same
manner explained here.
Ideally, each profile descriptive flag would be an array of the size [#
altitude bins, # profiles] with each array element providing a complete
description of the range-resolved atmospheric state. However, because the
range resolution of the Level 1 profile data below ~8.3-km is 30 m, and
because the feature-finder, scene classification, and extinction algorithms
all operate at this finer spatial resolution, providing a genuinely complete
description of the atmospheric state for each 60 m Level 2 range bin requires
that the profile descriptive flags be stored as 3-D arrays of the size
[#profiles, # altitude bins, 2]. The first dimension, [ : , : , 1],
corresponds to the standard altitude array of the Profile Products. Thus,
below 8.3 km, the first dimension contains the descriptive flags of the
higher of the two full resolution (30 m) bins that comprise the single 60 m
bin reported in the Profile Products. Meanwhile, below 8.3 km, the second
dimension [: , : , 2] contains the descriptive flags for the lower of the two
30 m range bins. Above 8.3 km, where the range resolution of the Level 1 data
is 60 m or greater, the descriptive flags for each single 60 m (or 180 m)
range bin are replicated in both array elements.
Figure 5: Wholly fictitious but heuristically useful schematic of
layer detection results for a data segment extending 80-km
horizontally and 480-m vertically. Yellow/orange/brown indicates an
aerosol layer detected at horizontal averaging resolutions of,
respectively, 80, 20 or 5 km. Shades of blue likewise represent
clouds at 80, 20, and 5 km. Red represents a surface detected layer
at 5 km, and the white regions are (presumably) clear air, where no
features were found. The right-hand side of the figure shows the
atmospheric volume descriptor for columns 1 and 16.
The extinction QC flags are bit-mapped 16-bit integers, reported for each
layer and for each wavelength for which an extinction retrieval was
attempted. Aerosol extinction is computed for both wavelengths; cloud
extinction is only reported at 532 nm. The information content of each bit is
as follows
Bit
Value
Interpretation
1
0
unconstrained retrieval; initial lidar ratio unchanged during solution process
1
1
constrained retrieval
2
2
Initial lidar ratio reduced to prevent divergence of extinction solution
3
4
Initial lidar ratio increased to reduce the number of negative
extinction coefficients in the derived solution
4
8
Calculated backscatter coefficient exceeds the maximum allowable value
5
16
Layer being analyzed has been identified by the feature finder as
being totally attenuating (i.e., opaque)
6
32
Estimated optical depth error exceeds the maximum allowable value
7
64
Solution converges, but with an unacceptably large number of negative values
8
128
Retrieval terminated at maximum iterations
9
256
No solution possible within allowable lidar ratio bounds
16
32768
Fill value or no solution attempted
The bit assignments are additive, so that (for example) an extinction QC
value of 18 represents an unconstrained retrieval (bit 1 is NOT set) for
which the lidar ratio was reduced to prevent divergence (+2; bit 2 is set),
and for which the feature finder has indicated that the layer is opaque (+16;
bit 5 is set). For the version 2.01 release, bits 10-15 are not used.
Complete information about the conditions under which each extinction QC bit
is toggled can be found in the
CALIPSO Extinction Retrieval ATBD (PDF)
The cloud-aerosol discrimination (CAD) score, which is reported in the 1-km
and 5-km layer products, and now in version 3, the 5 km cloud and aerosol
profile products, provides a numerical confidence level for the
classification of layers by the CALIOP cloud-aerosol discrimination
algorithm. The CAD algorithm separates clouds and aerosols based on
multi-dimensional histograms of scattering properties (e.g., intensity and
spectral dependence) as a function of geophysical location. In areas where
there is no overlap or intersection between these histograms, features can be
classified with complete confidence (i.e., |CAD score| = 100).
In the current release (version 3), the CAD algorithm uses newly developed
five-dimensional (5D) probability density functions (PDFs), rather than the
three-dimensional (3D) PDFs used in previous versions. In addition to the
parameters used in the earlier 3D version of the algorithm
(layer mean attenuated backscatter at 532 nm,
layer-integrated attenuated backscatter color ratio, and
altitude), the new 5D PDFs also include feature latitude and the
layer-integrated volume depolarization ratio. Detailed
descriptions of the CAD algorithm can be found in Sections 4 and 5 of the
CALIPSO Scene Classification ATBD (PDF). Enhancements
made to incorporate the 5D PDFs used in version 3 release are described in
Liu et al., 2010 (PDF). For further information on the
CAD algorithm architecture and the three-dimensional (3D) PDFs used in
versions 1 and 2 of the data products, please see
Liu et al., 2004 (PDF) and/or
Liu et al., 2009.
The standard CAD scores reported in the CALIPSO layer products range
between -100 and 100. The sign of the CAD score indicates the feature type:
positive values signify clouds, whereas negative values signify aerosols. The
absolute value of the CAD score provides a confidence level for the
classification. The larger the magnitude of the CAD score, the higher our
confidence that the classification is correct. An absolute value of 100
therefore indicates complete confidence. Absolute values less that 100
indicate some ambiguity in the classification; that is, the scattering
properties of the feature are represented to some degree in both the cloud
PDF and in the aerosol PDF. In this case, a definitive classification cannot
be made; that is, although we can provide a "best guess"
classification, this guess could be wrong, with a probability of error
related to the absolute value of the CAD score. A value of 0 indicates that a
feature has an equal likelihood of being a cloud and an aerosol. Users are
encouraged to refer to the CAD score when the cloud and aerosol
classification results are used and interpreted.
Beginning with the version 2.01 release, several "special" CAD
score values have been added. These are listed in the table below. Each of
these new values represents a classification result that is based on
additional information beyond that normally considered in the standard CAD
algorithm.
CAD score
Interpretation
-101
negative mean attenuated backscatter encountered; layer is most likely
an artifact, and its spatial and optical properties should be excluded from
all science analyses.
101
initially classified as aerosol, but layer integrated depolarization
mandates classifying layer as cloud
(version 2 only; obsolete
in version 3)
102
layer exhibits very high integrated backscatter and very low
depolarization characteristic of oriented ice crystals
(version 2 only; obsolete
in version 3)
103
layer integrated attenuated backscatter at 532 nm is suspiciously high;
feature authenticity and classification are both highly uncertain
104
layers with CAD scores of 104 are boundary layer clouds that were found
to be opaque at the initial 5-km horizontal averaging resolution used by
the layer detection algorithm; however, these layers are not uniformly
filled with high-resolution clouds (i.e., layers detected at a 1/3-km
horizontal resolution), and the 532 nm mean attenuated backscatter
coefficient of the data that remains after cloud clearing is negative.
Studies examining the spatial properties and distributions of clouds can
safely include the spatial properties of these layers; however, the
associated measured and derived optical properties should be excluded from
all science studies.
105
a CAD score of 105 designates a layer detected at one of the coarser
averaging resolutions (20-km or 80-km) for which the initial estimates of
measured properties have been negatively impacted by either (a) the
attenuation corrections applied to account for the optical depths of
overlying layers, or (b) the extension of the layer base altitude are
boundary layer clouds that were found to be opaque at the initial 5-km
horizontal averaging resolution used by the layer detection algorithm;
however, these layers are not uniformly filled with high-resolution clouds
(i.e., layers detected at a 1/3-km horizontal resolution), and the 532 nm
mean attenuated backscatter coefficient of the data that remains after
cloud clearing is negative. Studies examining the spatial properties and
distributions of clouds can safely include the spatial properties of these
layers; however, the associated measured and derived optical properties
should be excluded from all science studies.
Particulate total backscatter coefficients reported for each profile range
bin in which the appropriate particulates (i.e., clouds or aerosols) were
detected; those range bins in which no particulates were detected contain
fill values (-9999). Units are kilometers-1
steradians-1. For the 532 nm data, the particulate total
backscatter coefficients are derived from the sum of the parallel and
perpendicular backscatter measurements recorded aboard the CALIPSO satellite
(i.e., β532 total = β532 parallel +
β532 perp).
Total Backscatter Coefficient Uncertainty 532 (Provisional)
Total Backscatter Coefficient Uncertainty 1064 (Provisional;
aerosol products only)
Uncertainty in the particulate total backscatter coefficients reported for
each profile range bin in which the appropriate particulates were detected;
these are absolute uncertainties, not relative, thus the units are identical
to the units of the total backscatter coefficients (i.e.,
kilometers-1 steradians-1); those range bins in which
no particulates were detected contain fill values (-9999). Uncertainties are
computed according to the procedures described in the
CALIPSO Version 3 Extinction Uncertainty Document (PDF).
Perpendicular Backscatter Coefficient 532
Particulate backscatter coefficients derived from the 532 nm perpendicular
channel measurements, reported for each profile range bin in which the
appropriate particulates (i.e., clouds or aerosols) were detected; those
range bins in which no particulates were detected contain fill values
(-9999). Units are kilometers-1 steradians-1.
Uncertainty in the perpendicular channel backscatter coefficients reported
for each profile range bin in which the appropriate particulates were
detected; these are absolute uncertainties, not relative, thus the units are
identical to the units of the total backscatter coefficients (i.e.,
kilometers-1 steradians-1); those range bins in which
no particulates were detected contain fill values (-9999). Uncertainties are
computed according to the procedures described in the
CALIPSO Version 3 Extinction Uncertainty Document (PDF).
Particulate extinction coefficients reported for each profile range bin in
which the appropriate particulates (i.e., clouds or aerosols) were detected;
those range bins in which no particulates were detected contain fill values
(-9999). Units are kilometers-1.
Uncertainty in the particulate extinction coefficients reported for each
profile range bin in which the appropriate particulates were detected; these
are absolute uncertainties, not relative, thus the units are identical to the
units of the particulate extinction coefficients (i.e.,
kilometers-1); those range bins in which no particulates were
detected contain fill values (-9999). Uncertainties are computed according to
the procedures described in the
CALIPSO Version 3 Extinction Uncertainty Document (PDF)
Particulate Depolarization Ratio Profile 532 (Provisional)
The particulate depolarization ratio, δp(z), is a
post-extinction quantity, calculated from ratio of the layer integrated
perpendicular and parallel polarization components of particulate backscatter
coefficient at a given altitude z, using
Here β⊥,P and
β||,P perpendicular and parallel components of
particulate backscatter coefficient at 532 nm, respectively. The quality of
the estimate for δp is determined not only by the
SNR of the backscatter measurements in parallel and perpendicular channels,
but also the accuracy of the range-resolved two-way transmittance estimates
within the layer. The two-way transmittances due to molecules and ozone can
be well characterized via the model data obtained from the
GMAO.
The two-way transmittances due to particulates, however, are only as accurate
as the CALIOP extinction retrieval. Opaque cirrus cloud layers can be
particularly prone to errors in the particulate depolarization ratio, as very
large attenuation corrections are applied to the weak signals at the base of
the layers, and on those occasions where one channel or the other becomes
totally attenuated, this situation can generate very large, negative
particulate depolarization ratio estimates. For layers that are not opaque,
δp is generally reliable. However, in weakly
scattering layers, the quality of the daytime estimate can be degraded by a
factor of 2-4 due to the larger background noise compared with the nighttime
estimate.
The uncertainties reported for the particulate depolarization ratios provide
an estimate for random error in the particulate depolarization ratio for each
range bin (i.e., the ratio of perpendicular and parallel components of
retrieved particulate backscatter coefficient within the feature). Based on
an assessment of several days of test data (January 1-3, 2007), the
uncertainty for aerosol profile products is typically (a median value) ~0.18
and ~0.7 for nighttime and daytime measurements, respectively. For cloud
profile products, it is typically ~0.33 and ~0.58 during night and day,
respectively.
The multiple scattering profiles, η532(z) and
η1064(z), are specified at each wavelength
according to layer type and subtype. Values range between 0 and 1; 1
corresponds to the limit of single scattering only, with smaller values
indicating increasing contributions to the backscatter signal from multiple
scattering. Multiple scattering effects are different in aerosols, ice
clouds, and water clouds. A discussion of multiple scattering factors for
ice clouds and several aerosol types can be found in
Winker, 2003 (PDF). Multiple scattering in water clouds
is discussed in
Winker and Poole (1995).
Ice clouds: for the CALIOP viewing geometry, simulations show
multiple scattering effects are nearly independent of range and, as
parameterized in the CALIOP retrieval algorithm (Winker et al. 2009), are
nearly independent of extinction. In version 2, ice clouds were assigned a
range-independent multiple scattering factor of
η532 = 0.6. Validation comparisons indicate this
is an appropriate value and the same value is used in version 3.
Water clouds: in version 2 the multiple scattering factor for
water clouds was set to unity, resulting in large errors in retrievals of
extinction and optical depth. In version 3, a value of
η532 = 0.6 is used. Based on Monte Carlo
simulations of multiple scattering, this value appears to be appropriate for
semitransparent water clouds (τ < 1). (It is purely
coincidental this is the same value used for ice clouds.) For denser water
clouds (τ > 1) the multiply-scattered component of the
signal becomes much larger than the single-scattered component,
η532 becomes dependent on both cloud extinction
and range into the cloud, and the retrieval becomes very sensitive to errors
in the multiple scattering factor used. In these cases the multiple
scattering cannot be properly accounted for in the current retrieval
algorithm and retrieval results are unreliable.
Aerosols: simulations of multiple scattering effects on retrievals
of aerosol layer optical depth indicate the effects are small in most cases.
There is uncertainty in these estimates, however, due to poor knowledge of
aerosol scattering phase functions. Validation comparisons conducted to date
do not indicate significant multiple scattering effects on aerosol extinction
profile retrievals. Multiple scattering effects may become significant in
dense aerosol layers (σ > 1 /km), but in these cases
retrieval errors are usually dominated by uncertainties in the lidar ratio or
failure to fully penetrate the layer. In version 3, as in version 2, multiple
scattering factors for both wavelengths are set to unity.
Ice Water Content Profile (Provisional; cloud products only)
Ice water content (IWC) is reported for all ice clouds detected in the
CALIPSO measurements. IWC values of 0-0.54 gm,-3 account for 99.5%
of the values measured by CALIOP. Above 8.25 km, IWC values > 0.767
gm-3 cannot be measured.
Background: Cloud ice water content is a provisional data product that is
calculated as a parameterized function of the CALIOP retrieved extinction by
ice cloud particles:
(1)
Here, σ is the 532 nm volume extinction coefficient in
km-1, and c0 = 119 gm-3 and c1 =
1.22 are coefficients derived from an observed empirical relationship between
lidar extinction and an extensive set of in situ measurements of cloud
particle properties from numerous field campaigns [1]. The relationship
between 532 nm extinction and IWC was developed using IWC data between 0-1.0
gm-3 with temperatures between -70 and 0 °C. Cloud ice amount
has been shown to vary with temperature, cloud particle size distribution,
and by location inside a cloud. A temperature-dependent parameterization is
being considered and tested for the next CALIOP data release. The effect of
particle size distribution on IWC as seen by CALIOP is also currently being
evaluated by comparison with in situ cloud data. Preliminary results show
that CALIOP IWC has sufficient spatial resolution and precision to
realistically resolve cloud morphology. A more detailed preliminary
evaluation of the CALIOP version 3 IWC is available as an ILRC extended
abstract [2], which includes CALIOP IWC probability distributions and example
browse images. For a brief discussion containing critical information needed
to intelligently use CALIOP IWC, please see the "data screening"
section, below.
Resolution
IWC is reported at 60 m vertically, with a horizontal spatial resolution
of 5 km along-track, and effectively the width of the laser beam
across-track.
Precision
The precision of IWC is directly linked to the precision of the
associated extinction retrieval. The precision of the extinction retrieval is
ultimately limited by signal-to-noise ratio, and this varies between night
and day and according to the overhead two-way 532 nm transmission. Therefore,
the precision of CALIOP IWC has to be evaluated for each individual case. The
team is currently developing a best-case precision estimate for nighttime
high altitude Cirrus clouds.
Accuracy
Because this is a provisional data product, assessment of IWC accuracy is
currently underway. This assessment can be approached in two different ways;
(1) by establishing the accuracy of the 532 nm extinction on which it is
based, or (2) by assessing the IWC product directly. Direct comparison of
CALIOP IWC with other measured IWC values includes evaluation of both the
extinction retrieval and the IWC parameterization.
Data screening
CALIOP IWC is a highly derived data product. Besides the
parameterization, it relies on these activities:
Cloud determination
Bits 6 and 7 in the atmospheric volume descriptor
indicates a feature of type 2=cloud, determined using 5-dimensional
probability distribution functions as described in [3].
Cloud phase determination
The atmospheric volume descriptor indicates that the cloud phase is
1=randomly oriented ice (ROI) or 3=horizontally oriented ice (HOI) as
determined for cloud particles (type=2) using temperature and
depolarization [4]. Although extinction is available, IWC is not calculated
for cloud particle phase 2=water. Users should use caution with HOI data
because the preferred horizontal orientation of ice particles causes
anomalously large backscatter that makes the extinction retrievals more
difficult. This in turn may affect the accuracy of the IWC.
Extinction retrievals
Users that wish to understand the nuances and details of CALIOP
extinction retrievals are referred to [5]. Extinction retrievals that are
constrained provide the most accuracy. Unconstrained extinction retrievals
require an a priori estimate of the extinction to backscatter ratio. If the
a priori is adjusted significantly by the extinction retrieval algorithm
then the retrieval becomes less accurate. The extinction quality flag
provides this information about the extinction retrievals. An IWC user may
wish to use retrievals with a converged (extinction flag=1) or near a
priori (extinction flag=0) solution.
To summarize, users that do not wish to dig more deeply are recommended to
use data of type=2 with phase=1 (atmospheric volume descriptor) and an
extinction quality flag value of 0 or 1. IWC data from the highest cloud
layer (highest signal-to-noise ratio), relative uncertainty of < 2 and IWC
of < 0.026 gm-3 are likely to be the most reliable.
References:
Heymsfield, A. J., D. Winker and G.-J. van Zadelhoff, 2005:
"Extinction-ice water content-effective radius algorithms for
CALIPSO", Geophysical Research Letters, 32, L10807, pp.
1-4.
Avery, M., D. Winker, M. Vaughan, S. Young, R. Kuehn, Y. Hu, J.
Tackett, B. Getzewich, Z. Liu, A. Omar, K. Powell, C. Trepte, and K.-P.
Lee, (2010), "A first look at CALIOP/CALIPSO cloud ice water
content", submitted to the 25th International Laser and Radar
Conference.
Liu, Z., et. al. 2009: "The CALISPO Lidar Cloud and Aerosol
Discrimination: Version 2 Algorithm and Initial Assessment of
Performance", Journal of Atmospheric and Oceanic Technology,
26, pp. 1198-1213.
Hu, Y., et. al., 2009: "CALIPSO/CALIOP Cloud Phase Discrimination
Algorithm", Journal of Atmospheric and Oceanic Technology,
26, pp. 2293-2309.
Young, S. A. and M. A. Vaughan, 2009: "The Retrieval of Profiles
of Particulate Extinction from Cloud-Aerosol Lidar Infrared Pathfinder
Satellite Observations (CALIPSO) Data: Algorithm Description",
Journal of Atmospheric and Oceanic Technology, 26, pp. 1105-1119.
Protat, A., J. Delanoe, E.J. O’Connor and T.S.L’Ecuyer,
2010: "The evaluation of CloudSat and CALIPSO ice microphysical
products using ground-based cloud radar and lidar observations",
Journal of Atmospheric and Oceanic Technology, accepted, pp. 1-50.
Ice Water Content Profile Uncertainty (Provisional; cloud
products only)
IWC uncertainty has a range of 0-99.99 gm-3, and is derived
directly from the extinction retrieval uncertainty. The IWC fractional
uncertainty probability distribution is shown in [1]. This is the estimated
CALIOP measurement uncertainty, and does not characterize uncertainty in the
IWC parameterization. Users wishing to understand extinction retrieval
uncertainty should consult the summary descriptions at the top of this
page.
Comparison of CALIOP IWC with in situ data from various field campaigns,
and with CPL lidar data is underway. Further, we are working on comparisons
between CALIOP IWC and IWC from CLOUDSAT and MLS. We anticipate sharing the
results from these comparisons in future quality summary updates.
Future IWC parameterizations may include temperature dependency, based on
further in situ data comparisons. Currently suggested temperature-dependant
parameterizations do not produce good results for high-altitude tropical
clouds [6] and therefore are not used.
References:
Heymsfield, A. J., D. Winker and G.-J. van Zadelhoff, 2005:
"Extinction-ice water content-effective radius algorithms for
CALIPSO", Geophysical Research Letters;, 32, L10807, pp. 1-4.
Avery, M., D. Winker, M. Vaughan, S. Young, R. Kuehn, Y. Hu, J.
Tackett, B. Getzewich, Z. Liu, A. Omar, K. Powell, C. Trepte, and K.-P.
Lee, (2010), "A first look at CALIOP/CALIPSO cloud ice water
content", submitted to the 25th International Laser and Radar Conference.
Liu, Z., et. al. 2009: "The CALISPO Lidar Cloud and Aerosol
Discrimination: Version 2 Algorithm and Initial Assessment of
Performance", Journal of Atmospheric and Oceanic Technology,
26, pp. 1198-1213.
Hu, Y., et. al., 2009: "CALIPSO/CALIOP Cloud Phase Discrimination Algorithm",
Journal of Atmospheric and Oceanic Technology, 26, pp. 2293-2309.
Young, S. A. and M. A. Vaughan, 2009: "The Retrieval of Profiles
of Particulate Extinction from Cloud-Aerosol Lidar Infrared Pathfinder
Satellite Observations (CALIPSO) Data: Algorithm Description",
Journal of Atmospheric and Oceanic Technology, 26, pp. 1105-1119.
Protat, A., J. Delanoe, E.J. O’Connor and T.S. L’Ecuyer,
2010: "The evaluation of CloudSat and CALIPSO ice microphysical
products using ground-based cloud radar and lidar observations",
Journal of Atmospheric and Oceanic Technology, accepted, pp. 1-50.
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
"L2_Lidar".
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.
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.
This field reports the orbit number at the
granule start time.
Orbit Number at Granule End
This field reports the orbit number at the
granule stop time.
Orbit Number Change Time
This field reports the time at which the
orbit number changes in the granule.
Path Number at Granule Start
This field reports the path number at the granule start time.
Path Number at Granule End
This field reports the path number at the granule stop time.
Path Number Change Time
This field reports the time at which the path number changes in the granule.
Lidar Level 1 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 analyses.
Number of Single Shot Records in File
for internal use only
Number of Average Records in File
for internal use only
Number of Features Found
for internal use only
Number of Cloud Features Found
for internal use only
Number of Aerosol Features Found
for internal use only
Number of Indeterminate Features Found
for internal use only
Lidar Data Altitude
This field defines the lidar data altitudes (583 range bins) to which Lidar
Level 1 profile products are registered.
GEOS Version
This is a 64-byte character that reports the version of the GEOS data
product provided by the GMAO.
Provides the configuration information and command sequences that were
executed during the processing of the CALIOP Lidar Level 2 data products.
Documentation for many of the control constants found within this field is
contained in the
CALIPSO Lidar Level 2 Algorithm Theoretical Basis Documents
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
following document:
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
magnitudes.
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
parameters.
Summary Statement for the release of the
CALIPSO Lidar Level 2 Cloud and Aerosol Profile Products Version 3.02,
December 2011
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.
Summary Statement for the release of the
CALIPSO Lidar Level 2 Cloud and Aerosol Profile Products Version 3.01,
May 2010
The quality and depth of the information provided in version 3 of the CALIOP Lidar Level 2 Cloud
and Aerosol Profile Products are substantially improved over previous releases.
The improvements are attributed to a number of factors, including refinements to
layer base detection (PDF), more reliable separation of clouds and aerosols (PDF),
implementation of a new algorithm for determining cloud thermodynamic phase,
and bug fixes in the CALIOP extinction retrieval code. Data usability
is greatly enhanced by the addition of new diagnostic and quality assurance
parameters (e.g., the atmospheric volume description flags). In addition, several
new optical parameters have been added (e.g., particulate depolarization ratio profiles),
and range-resolved uncertainty estimates are now provided for all optical profile data.
The organization and structure of the version 3 profile products has also changed
significantly. In particular, the aerosol profile products have been completely restructured,
and now are reported on the same spatial grid as the cloud profile products. The horizontal
resolution of the cloud and aerosol profile products is now identical to the horizontal
resolution of the 5-km cloud and aerosol layer products, thus enabling a one-to-one match
between the bulk optical properties of any layer (e.g., optical depth) and the profile data
(e.g., extinction coefficients) from which the bulk properties were derived. The profile times
and latitude/longitude coordinates are now reported identically in the 5-km profile products
and the 5-km layer products.
Partly as a result of these many changes, and partly based on preliminary results from several
validation studies, the data product maturity level of the profile products has been
upgraded to provisional. Previous profile product releases were classified as beta.
In addition to the numerous algorithm updates, several new parameters have
been added to the profile products. These include
The structure and content of the version 3 profile products is significantly
different from previous releases. In particular, the aerosol profile products
have been completely restructured, and now are reported on the same 5-km
spatial grid as the cloud profile products. The horizontal resolution of the
cloud and aerosol profile products is now identical to the horizontal
resolution of the 5-km cloud and aerosol layer products, thus enabling a
one-to-one match between the bulk optical properties of any layer (e.g.,
optical depth) and the profile data (e.g., extinction coefficients) from which
the bulk properties were derived. The profile times and latitude/longitude
coordinates are now reported identically in the 5-km profile products and the
5-km layer products. Uncertainty estimates are now provided for all profile
data derived from the Level 1 lidar attenuated backscatter data, and several
new diagnostic and quality assurance parameters have been added. The full
extent of the changes made is identified and explained in the paragraphs below.
There are layers for which the optical depth can be reliably measured
directly from the CALIOP backscatter signal. For these layers, the measured
optical depths are reported in the layer products, and the layer two-way
transmittance is used to constrain the extinction solution, so that an optimal
estimate of the layer lidar ratio is retrieved. These constrained solutions are
the most reliable retrievals, and can be identified by examining the
corresponding profile of extinction QC flags. The
version 3 retrieval analysis has been improved to increase the amount of
constrained retrievals while still maintaining good data quality. Uncertainties
of the other, unconstrained, retrievals are larger, and primarily depend on how
closely the lidar ratio initially assumed by the algorithm agrees with the true
lidar ratio, and on how well the attenuation of overlying layers has been
estimated. Errors in the initial lidar ratio and attenuation correction
propagate systematically and non-linearly into subsequent retrievals, so that
extinction errors do not average out but instead produce biases.
In the case of opaque layers - i.e., where the lidar signal does not
penetrate to the base of the layer - the reported optical depth refers only to
the upper portion of the layer where there is measurable lidar signal. About
20% of cloud layers are identified as being opaque, as are a few very dense
aerosol layers. In these cases CALIOP cannot measure the true layer base height
and thus underestimates the true layer optical depth. The
Opacity Flag in the
Cloud and Aerosol
Layer Products identifies layers which are opaque. In the profile products,
opaque layers are identified by the extinction QC
flags. About 90% of cloud retrievals and 99% of aerosol retrievals
conclude successfully, in the sense that a physically possible solution is
found (retrieved extinction is finite and non-negative for non-negative
inputs). The results of the remaining retrievals can also be found in the data
products, but are non-physical. For these cases, the
extinction QC flags will enumerate the condition(s)
that caused the failure. New in version 3.01, the layer Extinction QC Flags are
reported on a profile-by-profile basis. Users are highly encouraged to make use
of the extinction QC values when using the data products.
The extinction retrieval will, if necessary to avoid a non-physical
solution, adjust the initial lidar ratio as required to produce a physically
plausible solution. The most common reason for adjusting the lidar ratio is
that the extinction solution diverges and the retrieved values tend toward
infinity when the assumed lidar ratio is too large. For weakly scattering
layers the lidar ratio is most often left unchanged, as a physical solution is
usually obtained on the first iteration. However, if the initial lidar ratio
is much larger than the true value, divergence can occur even for optically
thin (optical depth < 0.5) layers. When divergence occurs the lidar ratio is
automatically decreased and the retrieval is repeated until a convergent
solution is obtained.
In the current implementation of the extinction retrieval algorithm, the
lidar ratio adjustment scheme may result in unrealistic retrievals in aerosol
layers in cases where the initial divergence is due to a misclassification of
aerosol type. When divergence occurs, it can be because the true lidar ratio is
much less than the lidar ratio selected by the algorithm, or that the
attenuation of overlying layers was overestimated, or both. Aerosol retrievals
where the final lidar ratio is not equal to the initial lidar ratio should be
regarded carefully by the data user. The lidar ratio is adjusted in only about
5% of the aerosol retrievals.
Users should not be unduly pessimistic about the quality and usability of
the CALIPSO optical depth estimates. Figure 1 (below) shows a preliminary
comparison of CALIPSO version 3 aerosol optical depths with the optical depths
derived from MODIS for all daytime measurements acquired during January 2007.
The comparison is generally good, with MODIS appearing to slightly
over-estimate values at the lower end of the optical depth range.
Figure 1: Comparison of CALIPSO aerosol optical depths to those derived from MODIS
(Preliminary - January 2007, daytime data,
final
lidar ratio = initial lidar ratio only)
A histogram of cirrus optical depths from the Cloud Layer Product shows a
bimodal distribution (see Figure 2, left panel). The peak on the right, at an
optical depth of ~2.5, is an artifact due to the behavior of the retrieval
algorithm when the initial retrieval diverges and the lidar ratio is reduced to
produce a convergent solution. This happens most often in totally attenuating
(i.e.,
opaque)
clouds, and when the true cirrus lidar ratio is significantly smaller than the
initial value assumed by the algorithm. The second peak disappears when we
consider only those solutions for which the lidar ratio was unchanged (i.e., the
extinction
QC flag = 0; see Figure 2, right panel). In any case, errors grow rapidly
when the true cirrus lidar ratio is different from the assumed value. Errors
in optical depth for thin cirrus (optical depth < 0.5) are on the order of 41%
for unconstrained retrievals for clouds composed of random oriented crystals.
Although the production code applies the extinction retrieval algorithm to
all layers detected, the CALIOP extinction retrieval algorithm was developed
for retrievals of aerosol and ice clouds, not water clouds. In version 2 the
multiple scattering factor for water clouds was set to unity, resulting in
large errors in retrievals of extinction and optical depth. In version 3, a
value of η532 = 0.6 is used. Based on Monte Carlo
simulations of multiple scattering, this value appears to be appropriate for
semitransparent water clouds (τ < 1). (It is purely
coincidental this is the same value used for ice clouds.) For denser water
clouds (τ > 1) the multiply-scattered component of the
signal becomes much larger than the single-scattered component,
η532 becomes dependent on both cloud extinction and
range into the cloud, and the retrieval becomes very sensitive to errors in the
multiple scattering factor used. In these cases the multiple scattering cannot
be properly accounted for in the current retrieval algorithm and retrieval
results are unreliable.
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).
Layer Detection
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.
Cloud-Aerosol Discrimination
Figure 7 (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 7: Histograms of CAD scores for Version 2 (red) and Version 3 (blue)
Figure 8 (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 8: 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
post-V3 releases.
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
CAD performance.
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
retrievals.
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.
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.
Value
Version 2 Interpretation
Version 3 Interpretation
0
unknown/not determined
unknown/not determined
1
ice
randomly oriented ice (ROI)
2
water
water
3
mixed phase
horizontally oriented ice (HOI)
The Ice/water Phase QA flags have also been redefined slightly for Version
3, as follows:
Value
Version 2 Interpretation
Version 3 Interpretation
0
no confidence
no/low confidence
1
low confidence
phase based on temperature only
2
medium confidence
medium confidence
3
high confidence
high confidence
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.
PLEASE NOTE:
Users of the CALIOP optical depths should read and thoroughly understand the
information provided in the
Profile
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
