Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP) data products from Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of
the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been
developed. The IIR Level 2 data products include cloud effective
emissivities and cloud microphysical properties such as effective diameter
(De) and water path estimates for ice and liquid clouds. This paper
(Part II) shows retrievals over ocean and describes the improvements made
with respect to version 3 (V3) as a result of the significant changes
implemented in the V4 algorithms, which are presented in a companion paper
(Part I). The analysis of the three-channel IIR observations (08.65,
10.6, and 12.05 µm) is informed by the scene classification
provided in the V4 CALIOP 5 km cloud layer and aerosol layer products.
Thanks to the reduction of inter-channel effective emissivity biases in
semi-transparent (ST) clouds when the oceanic background radiance is derived
from model computations, the number of unbiased emissivity retrievals is
increased by a factor of 3 in V4. In V3, these biases caused inconsistencies
between the effective diameters retrieved from the 12/10
(βeff12/10=τa,12/τa,10)
and 12/08 (βeff12/08=τa,12/τa,08)
pairs of
channels at emissivities smaller than 0.5. In V4, microphysical retrievals
in ST ice clouds are possible in more than 80 % of the pixels down to
effective emissivities of 0.05 (or visible optical depth ∼0.1). For the month of January 2008, which was chosen to illustrate the results, median
ice De and ice water path (IWP) are, respectively, 38 µm
and 3 g m-2 in ST clouds, with random uncertainty estimates of 50 %.
The relationship between the V4 IIR 12/10 and 12/08 microphysical
indices is in better agreement with the “severely roughened single column”
ice habit model than with the “severely roughened eight-element aggregate”
model for 80 % of the pixels in the coldest clouds (<210 K) and
60 % in the warmest clouds (>230 K). Retrievals in opaque ice
clouds are improved in V4, especially at night and for 12/10 pair of
channels, due to corrections of the V3 radiative temperature estimates
derived from CALIOP geometric altitudes. Median ice De and IWP are
58 µm and 97 g m-2 at night in opaque clouds, with again
random uncertainty estimates of 50 %. Comparisons of ice retrievals with
Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua in the tropics
show a better agreement of IIR De with MODIS visible–3.7 µm than
with MODIS visible–2.1 µm in the coldest ST clouds and the opposite
for opaque clouds. In prevailingly supercooled liquid water clouds with
centroid altitudes above 4 km, retrieved median De and liquid water
path are 13 µm and 3.4 g m-2 in ST clouds, with estimated random
uncertainties of 45 % and 35 %, respectively. In opaque liquid clouds,
these values are 18 µm and 31 g m-2 at night, with estimated
uncertainties of 50 %. IIR De in opaque liquid clouds is smaller
than MODIS visible–2.1 µm and visible–3.7 µm by 8 and 3 µm,
respectively.
Introduction
The Imaging Infrared Radiometer (IIR) is one of the three instruments on
board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) satellite which has been in quasi-continuous operation since
mid-June 2006 (Winker et al., 2010). IIR is co-aligned with Cloud-Aerosol
Lidar with Orthogonal Polarization (CALIOP) and with the Wide Field Camera (WFC),
which are all arranged in a staring, near-nadir-looking
configuration. The IIR instrument includes three medium resolution channels
in the atmospheric window centered at 08.65, 10.6, and 12.05 µm with
1 km2 pixel size. Geolocated and calibrated radiances for
all channels are reported in IIR Level 1 products. The IIR Level 2 data
products include clouds effective emissivities and cloud microphysical
properties such as effective diameters and ice or liquid water path
estimates. Following the release of the version 2 IIR Level 1 products
(Garnier et al., 2018) and of the version 4 (V4) CALIOP data products, a new
version 4 (V4) of the IIR Level 2 data products has been developed and is
now available publicly.
The V4 algorithm and its changes with respect to version 3 (V3) are
presented in a companion paper (Garnier et al., 2021, hereafter “Part I”).
Cloud microphysical properties are derived using the split-window method
relying on the analysis of inter-channel effective absorption optical depth
ratios, or microphysical indices, from which effective diameter is inferred.
The concept of the microphysical index was introduced by Parol et al. (1991)
and has been widely used for operational retrievals (Heidinger and
Pavolonis, 2009; Pavolonis, 2010). Ice cloud absorption is stronger at
12.05 µm than at 10.6 µm or 08.65 µm. As a result, the brightness
temperatures are smaller at 12.05 µm; hence, a well-known split-window
retrieval approach is used in the analysis of inter-channel brightness
temperature differences (Inoue, 1985). Hyperspectral infrared sensors such
as Atmospheric Infrared Sounder (AIRS) or Infrared Atmospheric Sounder
Interferometer (IASI) allow advanced multi-channel analyses using
optimization techniques (Kahn et al., 2014) and the analysis of the spectral
coherence of the retrieved cloud emissivities (Stubenrauch et al., 2017).
The split-window technique in the thermal infrared spectral domain is very
sensitive to the presence of small particles having a maximum dimension
smaller than approximately 50 µm in the size distribution (Mitchell
et al., 2010). It was shown using the Moderate Resolution Imaging
Spectroradiometer (MODIS) thermal infrared bands that observations in this
spectral domain are perfectly suited to unambiguously identify the presence
of small ice crystals in cold cirrus clouds (Cooper and Garrett, 2010). As
such, thermal infrared techniques can provide insights into the observations
of small crystals by some in situ instruments when measurements of sizes
smaller than 15 µm are uncertain (Mitchell et al., 2018) and help
evaluate the possible effects of crystal shattering (Cooper and Garrett,
2011).
Regardless of the retrieval approach, the split-window technique is best
adapted for retrievals in clouds of medium effective emissivity.
Uncertainties are minimum for cloud effective emissivities between 0.2 and
0.9 (Garnier et al., 2013, hereafter G13), or cloud optical depth between
about 0.45 and 4.6, where the information content is the largest (Iwabuchi
et al., 2014; Wang et al., 2016). Given sufficiently accurate emissivity
estimates, retrievals of cloud properties beyond these lower and upper
limits remain possible until the emissivities are either too close to 0 for
subvisible clouds or too close to 1 for clouds behaving as blackbody sources,
at which points the technique totally loses sensitivity. In addition, the
logarithmic relationship between cloud optical depth and infrared emissivity
causes a saturation of the cloud optical depths retrievals. For instance,
emissivities larger than 0.99 correspond to cloud optical depth larger than
only 9. Techniques relying on the combination of visible and near-infrared
bands, as used in MODIS operational retrievals (Nakajima and King, 1990;
Platnick et al., 2017), are better suited than thermal infrared techniques
for cloud optical depths larger than 5 (Wang et al., 2011), but these
methods are limited to daytime observations only.
Due to its sensitivity to small particles, the split-window technique is an
attractive option for retrievals of liquid droplet sizes (Rathke and
Fisher, 2000), and microphysical retrievals in liquid water clouds are now
included in the V4 IIR products. All other things being equal, the
performance of the split-window technique increases with the radiative
contrast between the cloud and the surface. Consequently, retrieval
uncertainties are larger for liquid water clouds, which typically form
relatively close to the Earth's surface, and hence these retrievals were not
included in V3. Liquid water clouds such as marine stratocumulus clouds,
which are an important component of the Earth system, have optical depths
typically larger than 10, well beyond the range of applicability of the
technique. However, infrared observations have the potential to provide new
insight into the microphysical properties of thin liquid water clouds
(Turner et al., 2007; Marke et al., 2016) and supercooled mid-level
liquid water clouds.
The IIR analyses start with the retrieval of cloud effective emissivities in
each channel, which are then converted to effective absorption optical
depths as τa,k=-ln(1-εeff,k), where
εeff,08, εeff,10, and
εeff,12 are the effective emissivities retrieved in IIR channels 08.65
(k=08), 10.6 (k=10), and 12.05 (k=12), respectively. Effective
emissivity is mostly a measure of cloud absorption, and the term
“effective” refers to the contribution from scattering, which is the most
significant at 08.65 µm. The first IIR microphysical index,
βeff12/10=τa,12/τa,10, is the ratio of the
effective absorption optical depths at 12.05 and 10.6 µm and the
second one, βeff12/08=τa,12/τa,08, is
the ratio of the effective absorption optical depths at 12.05 and 08.65 µm.
Two main pieces of information are needed to retrieve these
quantities: the cloud top-of-atmosphere (TOA) blackbody radiance, which
requires a good estimate of the cloud radiative temperature, and the TOA
background radiance that would be observed if no cloud were present. The
former drives the accuracy at large emissivities and the latter the accuracy
at small emissivities.
The first step into any retrieval approach is the detection of a cloud and
the determination of its thermodynamic phase and radiative temperature. The
ability to ascertain cloud amounts and characteristics varies with the
observing capabilities of different passive sensors (Stubenrauch et al.,
2013). Even though IIR has only three medium resolution channels, its
crucial advantage is the quasi-perfect co-location with CALIOP observations.
Indeed, as emphasized by Cooper et al. (2003), cloud boundaries measured by
active instruments provide an invaluable piece of information for obtaining
accurate estimates of cloud radiative temperatures. The IIR algorithm relies
on CALIOP's highly sensitive layer detection to characterize the atmospheric
column seen by each IIR pixel. CALIOP provides geometrical altitudes which
are converted into radiative temperatures. The radiative temperature,
Tr, of a multi-layer cloud system is estimated as the thermodynamic
temperature, Tc, at the centroid altitude of the CALIOP attenuated
backscatter at 532 nm. In the V4 algorithm, this estimate is further
corrected when single- or multi-layer ice cloud systems are observed (Part
I). The thermodynamic temperature is derived from interpolated temperatures
profiles of the Global Modeling and Assimilation Office (GMAO) Modern-Era
Retrospective analysis for Research and Applications version 2 (MERRA-2)
model (Gelaro et al., 2017).
The second retrieval step is the determination of the TOA background
radiance, which often requires simulations using ancillary meteorological
profiles and surface data. These simulations are generally more accurate
over oceans than over land because the surface emissivities in the various
channels are better known and less variable over oceans, and the skin
temperature data are usually more accurate. In this paper, we therefore
focus on retrievals over oceans. In the IIR algorithm, the TOA background
radiance is preferentially determined using observations in neighboring
pixels in those cases when clear-sky conditions, as determined by CALIOP,
can be found. Otherwise, it is computed using the fast-calculation radiative transfer (FASRAD)
model (Garnier et al., 2012; Dubuisson et al., 2005). In V3, IIR
microphysical retrievals over oceans were possible down to
εeff,12∼0.05 (or optical depth ∼0.1)
when the background radiance could be measured in neighboring pixels (G13).
When the background radiance had to be computed by FASRAD, which represents
about 75 % of the cases, inter-channel biases in the model simulations
caused discernable flaws in the microphysical retrievals. The inter-channel
biases in the FASRAD simulations have been significantly reduced in V4, as
discussed in Part I.
This paper aims at demonstrating the improved accuracy of the V4 effective
emissivities and of the subsequent microphysical indices that result from
the changes implemented in the V4 algorithm (Part I) and at illustrating
the changes in the retrieved microphysical properties. Our assessment is
carried out after carefully selecting the relevant cloudy scenes, following
the rationale presented in Sect. 2. Retrievals in ice clouds are presented
in Sect. 3, which includes step-by-step comparisons between V3 and V4,
examples of V4 retrievals, and comparisons with MODIS retrievals. Section 4
is dedicated to retrievals in liquid water clouds that were added in V4, and
Sect. 5 concludes the presentation.
Cloudy scene selection
The analysis of the IIR observations is informed by the scene classification
provided by the V4 CALIOP cloud and aerosol 5 km layer products. This scene
classification is established for layers detected by the CALIOP algorithm at
5 and 20 km horizontal averaging intervals (Vaughan et al., 2009). An
example is shown in Fig. 1, which was extracted from nighttime granule
2008-01-30T09-15-45ZN on 30 January 2008.
Example of the CALIOP scene classification information
used for effective emissivity retrievals on 30 January 2008 (granule 2008-01-30T09-15-45ZN). (a)
CALIOP attenuated backscatter with top and base altitudes of the
cloud system highlighted in black; (b) number of cloud layers in the
cloud system; cases with Earth surface as a reference are denoted with black
lines (thin: semi-transparent (ST) layers; thick: one opaque layer), and in red
are the cases with the lowest opaque cloud as a reference; (c) CALIOP “Was
Cleared” flag at 1 km IIR pixel resolution; (d) ice water flag of the
cloud system; (e) temperatures at cloud top and cloud base (black) and
radiative temperature used by the IIR algorithm (red); (f) effective
emissivity of the cloud system at 12.05 µm. See text for details.
Figure 1a shows the Level 1 CALIOP attenuated backscatter averaged at 5 km
horizontal resolution with the top and base altitudes of the cloud system
shown in black. Cloudy scenes can include one or several layers (Fig. 1b).
When the lowest of at least two layers is opaque to CALIOP, this opaque
layer is used as a reference assuming it behaves as a blackbody source and
the algorithm retrieves the properties of the overlying semi-transparent
(ST) layers. An example is found between latitudes -36.45 and -36.7∘, highlighted in red in Fig. 1b, where the algorithm
retrieves the properties of two ST layers overlying the opaque cloud located
at about 8 km altitude. South of -36.7 and down to -37.2∘,
the portion of this cloud which is used as an opaque
reference between -36.45 and -36.7∘ is included in a
single opaque cloud of top altitude equal to 11.5 km, which extends down to
the southernmost latitudes. North of -36.45 and up to
-34.45∘, the atmospheric column includes one to three semi-transparent
clouds. Finally, no cloud layers are seen north of -34.45∘, where
the scenes contain only low ST non-depolarizing aerosol layers (not shown).
The atmospheric column might also contain clouds having top altitudes lower
than 4 km that are detected at single-shot resolution and then cleared
before searching for the more tenuous layers typically reported in the 5 km
products (Vaughan et al., 2009). These single-shot detections are not
included in Fig. 1b. The number of these single-shot cleared clouds seen
within each IIR pixel is shown in Fig. 1c. We showed in Part I (Fig. 5 in
Part I) that the presence of these cleared clouds modifies the background
radiance compared to the radiance due to the ocean surface and ultimately
biases the effective emissivity retrievals. Because these biases cannot be
quantified a priori, scenes that contain single-shot cleared clouds should
be treated with caution. The ice water flag shown in Fig. 1d characterizes
the ice–water phase of the cloud layers included in the cloud system. These
layers are classified either as ice, liquid water, or “unknown” by the V4
CALIOP ice–water phase algorithm (Avery et al., 2020). Most of the ice
clouds are composed of randomly oriented ice (ROI) crystals. Clouds
containing significant fractions of horizontally oriented ice (HOI) crystals
are also detected, mainly before the end of November 2007, when the platform
tilt angle was changed from its initial 0.3∘ orientation to a view
angle of 3∘ (Avery et al., 2020). In Fig. 1, we find cloud systems
composed of ROI only (flag = 1), liquid water (WAT) only (flag = 2), ice
and WAT (flag = 6), and some systems that include at least one layer of
unknown phase (flag = 9). IIR effective emissivities are reported for all
single- or multi-layer scenes, regardless of the phase. In V4, the phase
information is used to adjust the radiative temperature (Fig. 1e) estimates
in cases containing ice clouds (Part I). For illustration purposes, the V4
retrieved effective emissivities at 12.05 µm are shown in Fig. 1f. In
this example, emissivity values in the opaque cloud are mostly around 1, the
lowest value being 0.91 at -39.5∘ where the CALIOP image suggests
the presence of a faint signal below the cloud. Effective emissivities in ST
clouds vary between 0 and 0.9. The only exception is between
-36.45 and -36.52∘, where non-physical negative
effective emissivities are retrieved because the computed background
radiances are smaller than the observed radiances and are therefore
underestimated. In this case, the reference is a cloud classified as opaque
by CALIOP (see area highlighted in red in Fig. 1b), which is likely not
sufficiently dense to behave as a blackbody reference.
This example shows that a cloudy scene can include a variety of conditions
for the IIR retrievals. Because the goal here is to present the cloud
microphysical properties as retrieved with the IIR V4 algorithm and
improvements with respect to V3, we chose to limit the analyses to scenes
that contain only ROIs, only HOIs, or only WAT clouds with background
radiances from the ocean surface. Furthermore, in order to facilitate the
interpretation of the results, we require that the CALIOP cloud–aerosol
discrimination algorithm (Liu et al., 2019) assign high confidence to the
cloud classifications and likewise that the ice–water phase algorithm
determined the phase classifications with high confidence. Finally, scenes
containing single-shot cleared clouds are discarded. Table 1 reports the
fraction of scenes that fall into these categories. The statistics are for
IIR pixels between 60∘ S and 60∘ N in January and July 2008.
The ROI scenes represent 13 % to 16 % of all the IIR pixels. The
HOI scenes represent less than 0.1 % of all the IIR pixels, and we found
that they represent less than 1 % at the beginning of the mission when
the platform tilt angle was 0.3∘. Thus, in the rest of the paper,
ice clouds will refer to scenes containing only ROI layers. The WAT scenes
represent 14 % to 19 % of all the IIR pixels.
Total number of IIR pixels, fraction of IIR pixels with only
high-confidence ROI, WAT, and HOI layers in the column and no single-shot cleared
clouds for retrievals with background radiance from the ocean surface between
60∘ S and 60∘ N, and fraction of clear-sky pixels.
January 2008 July 2008 NightDayNightDayNo. of IIR pixels4.2 × 1064.2 × 1063.8 × 1063.9 × 106Fraction of IIR pixels ROI0.1320.1600.1270.155HOI<0.001<0.001<0.001<0.001WAT0.1750.1920.1430.182Clear sky0.1430.2040.1650.208Clear sky rejected in V40.0830.0630.0970.074
Clear-sky conditions are defined as cloud-free scenes with the “Was Cleared” flag
at 1 km resolution equal to zero, with no aerosol layers or only low
(<7 km) semi-transparent “not dusty” layers. Dusty layers are
those identified as dust, polluted dust, or dusty marine (Kim et al., 2018)
and are discarded because they may have a signature in the IIR channels
(Chen et al., 2010). For comparison with the previous categories, the
clear-sky conditions represent 20 % of the cases for daytime data and 15 %
for nighttime data. It is noted that 6 % to 10 % of the pixels are rejected
as “clear sky” in V4 due to the presence of single-shot cleared clouds.
These pixels would have been accepted by the V3 algorithm: they represent
25 % and 35 % of the V3 clear-sky conditions for daytime and nighttime
data, respectively.
Scenes composed of only high-confidence ROI layers or only WAT layers can
include either one opaque layer or a number of ST layers. This is quantified
in Table 2 for the months of January and July 2008. For these months, 45 to
53 % of the selected ROIs are opaque to CALIOP, while opaque clouds
represent 67 % to 90 % of the WATs. Daytime fractions of opaque clouds are
larger than nighttime ones, which is likely due daytime surface detection
issues. Scenes with only ST layers are spread into three main categories:
only one layer, two vertically overlapping layers detected at different
horizontal averaging resolutions where the top altitude of the lower layer
is greater than the base altitude of the higher layer, and multi-layer
configurations with two non-overlapping layers or more than two layers. For
both ROI and WAT clouds, the vast majority of the ST scenes have only one
layer in the column, which is explained by the fact that we required all the
layers to be characterized with high confidence. Thus, the study will be
carried out for single-layer cases for simplicity.
Detailed statistics for IIR pixels with only ROI or WAT
high-confidence layer(s) in the column and no cleared clouds for retrievals with
background radiance from the ocean surface between 60∘ S and
60∘ N in January and July 2008: fraction of opaque clouds,
single-layered ST clouds, ST clouds with two overlapping layers, and
multi-layered ST clouds.
ROI WAT January 2008 July 2008 January 2008 July 2008 NightDayNightDayNightDayNightDayOpaque0.4520.4870.4700.5330.7860.8990.6720.864ST one layer0.4940.4580.4820.4200.2000.0970.3130.131ST overlap0.0070.0060.0080.0040.006<0.0010.007<0.001ST multi-layered0.0470.0490.0400.0430.0080.0030.0080.004Retrievals in ice clouds
The accuracy of the effective emissivity in each IIR channel and of the
subsequent microphysical indices is a prerequisite for successful retrievals
of cloud microphysical properties. In Sect. 3.1, we use internal quality
criteria to demonstrate the improvements in the V4 effective emissivities in
ice clouds that result from the revised computed background radiances over
oceans and from the revised radiative temperature estimates (Part I). After
examining the changes in εeff,12 (at 12.05 µm),
inter-channel effective emissivity differences,
Δεeff12-k=εeff,12-εeff,k, are
assessed, keeping in mind that they should tend towards zero on average when
εeff,12 tends towards 0 and towards 1 (G13; Part I).
Changes in the visible cloud optical depth, τvis, inferred from
the summation of absorption optical depths at 12.05 µm and
10.6 µm (τa,12+τa,10; Part I), are shown in Sect. 3.2.
The subsequent improvements in the microphysical indices and in the
performance of the microphysical algorithm are discussed in Sect. 3.3,
where we also illustrate changes in the effective diameters (De)
reported in V3 and V4. We recall that De is defined as De=(3/2)×(V/A), where V is the total volume of the size distribution
and A is the corresponding projected area (Foot, 1988; Mitchell,
2002). The V4 algorithm uses two ice habit models from the “TAMUice2016”
database (Bi and Yang, 2017; Yang et al., 2013), namely the severely
roughened solid column (SCO) and severely roughened eight-element column
aggregate (CO8) models, and the model used for the retrievals is selected
according to the relationship between βeff12/10 and βeff12/08.
IIR retrieved De is the mean of the De12/10 and
De12/08 effective diameters when these two values can be retrieved from
the respective βeff12/k; that is, De=De12/10+De12/08/2. Both
De12/10 and De12/08 are reported in the product for users
interested in specific analyses. The V4 look-up tables (LUTs) that relate
microphysical index and effective diameter are computed using the fast discrete ordinate method (FASDOM)
(Dubuisson et al., 2008) model and bulk single scattering properties derived
using an idealized gamma particle size distribution. In V3, the LUTs were
derived using single scattering properties of the “solid column” and
“aggregate” ice habit models from the database described in Yang et al. (2005),
with no particle size distribution. We showed in Part I that,
everything else being equal, the size distribution introduced in V4
increases retrieved De. As illustrated in Part I, the microphysical
indices are very sensitive to De smaller than 50 µm and the
sensitivity decreases progressively up to De=120µm, which is
considered the sensitivity limit of our retrievals in ice clouds.
In Sect. 3.4, we show examples of V4 De and ice water path (IWP)
microphysical retrievals and comparisons with MODIS retrievals are presented
in Sect. 3.5.
Effective emissivity: V4 vs. V3
Because of numerous changes in the CALIOP V4 algorithms, the cloud layers
reported in the V3 and V4 CALIOP data products are not identical, so
direct comparisons of the V3 and V4 IIR data products could be misleading.
In order to isolate the changes due to the IIR algorithm, the V3
emissivities (hereafter V3_comp) for clouds reported in
CALIOP V4 were recomputed using the V3 computed background radiances
reported in the V3 product and the V3-like blackbody temperatures derived
directly from the centroid temperatures, Tc, which are available in the
V4 product along with the V4 blackbody temperatures. The exercise was
carried out for V4 scenes over oceans that contain a single cloud layer
classified as high-confidence ROI with no cleared clouds, as discussed
previously in Sect. 2. Illustrations are shown for the month of January 2008
between 60∘ S and 60∘ N.
Effective emissivity in channel 12.05
The nighttime (blue) and daytime (red) distributions of εeff,12
are shown in Fig. 2, where V4 (solid lines) is compared with
V3_comp (dashed lines). Figure 2a and b show the
distributions for ST and opaque clouds, respectively. The V4 median random
uncertainty estimates shown in Fig. 2c and d are of the order of 0.015 at
εeff,12<0.6 and increase up to 0.03 at the
largest emissivities, where the uncertainty in εeff,12 is
prevailingly due to the uncertainty in the radiative temperature taken equal
to ±2 K (Part I). Because of retrieval errors, εeff,12
can be found outside the range of physically possible values
(i.e., 0 to 1). For ST clouds (Fig. 2a), the V3 and the V4 histograms differ
mostly at εeff,12<0.05, where the changes in the
background radiances have the largest impact. In this example, the fraction
of ST clouds with negative εeff,12 values is reduced from
12 % in V3 to 3.5 % in V4. For opaque clouds (Fig. 2b), the larger V4
εeff,12 values are due to the radiative temperature
corrections introduced in the V4 algorithm (these corrections have
essentially no impact on ST clouds). For the range of εeff,12
values found in opaque clouds, the corrections are prevailingly
a function of the “apparent” cloud thickness, which is larger and closer
to the true geometric thickness at night (Part I). Nighttime and daytime
εeff,12 distributions peak at larger εeff,12
in V4 (εeff,12=0.99 and 0.97,
respectively) than in V3 (εeff,12=0.94).
Consequently, random uncertainties and possible overcorrections cause an
increase of the fraction of samples with εeff,12>1, from 3 % in V3 to 12 % in V4 at night, and from 1.2 % to
3.3 % for daytime data. At night, 98 % of the opaque clouds have V4
εeff,12>0.8 or cloud optical depth
>3.2. This lower range of optical depths is consistent with V4
CALIOP optical depth retrievals, even though it is recognized that direct
comparisons with V4 CALIOP optical depths in opaque clouds are difficult
(Young et al., 2018). Nighttime εeff,12 distributions for
ST and opaque clouds are essentially mutually exclusive, with a
εeff,12 threshold around 0.7. In contrast, these distributions
overlap between 0.4 and 0.7 for daytime data. The tail down to
εeff,12=0.4 (τvis∼1) for daytime opaque
cloud data is explained by a greater difficulty for the CALIOP algorithm to
detect faint surface echoes during the day due to large solar background
noise, so some clouds of moderate emissivity may be misclassified as
opaque by CALIOP. Effective emissivities close to 1 are found in clouds
where the CALIOP integrated attenuated backscatter (IAB) is larger than
0.04 sr-1, which is in the upper range of values typically observed in
opaque ice clouds (Young et al., 2018). Platt et al. (2011) showed that
these large IABs, which are often coupled with small apparent geometric
thicknesses, are observed when the CALIPSO overpass is close to the center
of a mesoscale convective system. Using cloud retrievals based on AIRS
thermal infrared data, Protopapadaki et al. (2017) demonstrated that
emissivities close to 1 in the tropics are most often indicative of
convection cores reaching the upper troposphere, which confirms our
observations based on CALIPSO.
Effective emissivity distributions at 12.05 µm in
(a) ST and (b) opaque single-layered ice clouds over oceans between
60∘ S and 60∘ N in January 2008 in V4 (solid lines) and
in V3_comp (dashed lines). The blue and red curves are for
nighttime and daytime data, respectively. Panels (c) and (d) are the V4
median random uncertainty estimates corresponding to panels (a) and (b),
respectively.
Inter-channel effective emissivity differences
We recall that effective emissivity retrievals preferably use background
radiances observed in neighboring clear-sky pixels and otherwise use
radiances computed by FASRAD. In order to evaluate V4 computed background
radiances, we first examined Δεeff12-k at
εeff,12∼0 in ST clouds by separating
retrievals that used observed radiances (V4_obs) and those
that used computed radiances (V4_comp). The results are
reported in Table 3, where Δεeff12-k at
εeff,12≈0 is also reported for
V3_comp for reference. As in V3 (G13), V4 inter-channel
biases are minimum when the background radiance can be determined from
observations (V4_obs), which represents 30 % of the
retrievals in ST clouds for this dataset. When the background radiance is
computed (V4_comp, 70 % of the cases), median Δεeff12-k is similar for both channel pairs and smaller
than 0.0025 in absolute value. This indicates residual inter-channel biases
smaller than 0.1 K in V4 according to the simulations shown in Fig. 1b of
Part I, which is consistent with the residual inter-channel differences seen
in clear-sky conditions (Part I). Because these biases are very small,
retrievals using computed and observed radiances are consistent in V4, and
hereafter the two methods will be referred to collectively as “V4” for
clarity. The Δεeff12-k differences were
unambiguously too low in V3_comp, especially for the 12–08
pair, so reliable retrievals were possible only when observed radiances
were available (G13). Including retrievals using computed radiances in V4
increases the number of retrievals in ST clouds by a factor of 3.3.
Inter-channel effective emissivity differences at
εeff,12∼ 0 for retrievals in single-layered ST ice
clouds over oceans between 60∘ S and 60∘ N in January 2008. N/A stands for “not applicable”.
Fraction of Δεeff (12–10) Δεeff (12–08) retrievals -0.005<εeff,12<0.005-0.005<εeff,12<0.005NightDayNightDayNightDayV4_obs0.270.33Median0.00000.00020.00050.000825th-0.003-0.003-0.002-0.00275th0.0030.0030.0030.004V4_comp0.730.67Median-0.0010.001-0.00250.000425th-0.004-0.002-0.0053-0.00375th0.0020.0050.00030.0045V3_compN/AN/AMedian-0.006-0.004-0.018-0.01525th-0.009-0.007-0.023-0.02175th-0.003-0.0001-0.014-0.010
The variations with εeff,12 of the
Δεeff12-k inter-channel effective emissivity differences for the 12–10
and 12–08 pairs are shown in Fig. 3a and b, respectively. The curves are
median values, and the shaded gray areas are between the V4 nighttime
25th and 75th percentiles. The first observation is that median
Δεeff12-k are larger in V4 (solid lines) than in
V3_comp (dashed lines) at any emissivity. When εeff,12
tends towards 1, Δεeff12-k is minimum
at εeff,12 corresponding to the peak of the
distributions shown in Fig. 2, which suggests that the peaks should be
closer to εeff,12=1. This shows that V4 is improved
compared to V3, more convincingly for nighttime data, but also that the
radiative temperature corrections are likely not sufficient. Consistent with
the simulations shown in Fig. 1 of Part I, Δεeff12-k
are increased from V3 to V4 at large emissivities, because the
radiative temperatures are increased, and the changes are more important in
the 12/08 pair than in the 12/10 pair.
IIR inter-channel (a)Δεeff12–10
and (b)Δεeff12–08 effective emissivity differences vs.
effective emissivity at 12.05 µm in single-layered ice clouds over
oceans between 60∘ S and 60∘ N in January 2008 in V4
(solid lines) and in V3_comp (dashed lines). The blue and red
curves are median values for nighttime and daytime data, respectively. The
shaded gray areas are between the V4 nighttime 25th
and 75th percentiles.
Visible cloud optical depth: V4 vs. V3
The V3–V4 changes in the visible cloud optical depths inferred from
εeff,12 and εeff,10 are
shown in Fig. 4a and b for nighttime and daytime data, respectively. The large plots
where τvis ranges between 0 and 15 are built using bins equal to
0.2, and the small embedded plots show details for τvis smaller
than 1 and bins equal to 0.02. The changes in τvis are smaller
than 0.02 on average and not significant for τvis smaller than 2
(or εeff,12<∼0.6), that is for
most of the ST clouds. For τvis>2,
V4 τvis is increasingly larger than V3 τvis, due to the
warmer radiative temperature estimates in V4. Consistent with previous
observations regarding εeff,12, the τvis
increase from V3 to V4 is larger at night (Fig. 4a) than during the day
(Fig. 4b).
(a) Nighttime and (b) daytime comparisons of V3 and V4 IIR
cloud optical depth (τvis) in single-layered
ice clouds over oceans between 60∘ S and 60∘ N in
January 2008. The small embedded plots show details for τvis between 0 and 1.
Microphysical indices and effective diameter retrievals: V4 vs. V3
The changes in the βeff12/10 and βeff12/08
microphysical indices resulting from the changes in
Δεeff12–10 and Δεeff12–08 (Fig. 3) are
illustrated in Fig. 5a and b. The sharp variations of the V4 median
microphysical indices (solid lines) at εeff,12<0.03 and εeff,12>0.96 are due to the
increasing truncation of the distributions, because both βeff12/k
indices can be computed only when
0<εeff,k<1 in the three channels. Overplotted in Fig. 5 are
the median V4 random absolute uncertainty estimates, which are the minimum and
around 0.02 for intermediate emissivity values (G13). The noticeable large
dispersion of the βeff12/k values at εeff,12<0.1
is largely explained by the random uncertainties. The median
βeff12/k values are overall larger in V4 than in
V3_comp, with larger changes for the 12/08 pair than for the
12/10 pair. The consequences for the De retrievals are two-fold. First,
the fraction of βeff12/k values that are larger than the low
sensitivity limit (close to 1) is increased in V4, which means that the
fraction of samples for which microphysical retrievals can be attempted is
augmented. Secondly, the larger V4 βeff12/k values yield smaller
De12/k. These two main changes are detailed and quantified in the
following subsections.
(a)βeff12/10 and (b)βeff12/08 microphysical indices vs.
effective emissivity at 12.05 µm in single-layered ice clouds over
oceans between 60∘ S and 60∘ N in January 2008 in V4
(solid lines) and in V3_comp (dashed lines). The blue and red
curves are the median values for nighttime (blue) and daytime (red), and the
shaded gray areas are between the V4 nighttime 25th and
75th percentiles. The blue (night) and red (day) thin
dashed–dotted lines are the V4 random absolute uncertainty estimates with the
vertical axis on the right-hand side of each panel.
Fraction of samples in sensitivity range
Figure 6a and b show fractions of samples for which βeff12/10
and βeff12/08 are larger than their respective theoretical lower
ranges, which were derived for De=120µm using the V4 SCO
LUT, and in practice are close to 1. For both βeff12/10 and
βeff12/08, V4 retrievals are possible more than 80 % of the
time for εeff,12 between 0.05 and 0.80 (or about
0.1–3.2 in terms of τvis). In contrast, the εeff,12
80 % range in V3_comp was only 0.15–0.7 for the 12/10
pair and only 0.25–0.7 for the 12/08 pair. As εeff,12
increases from 0.8 to 0.95 (τvis∼6), which
corresponds to clouds that are opaque to CALIOP (see Fig. 2), the
βeff12/k indices decrease and approach the sensitivity limit, and the
fraction of possible retrievals in opaque clouds decreases. This fraction is
notably increased in V4 and is larger at night than for daytime data,
reflecting the impact of the cloud radiative temperature corrections
introduced in V4. As in V3, this fraction remains lower for the 12/08 pair.
One hypothesis is that cloud heterogeneities in dense clouds could induce a
larger low bias in the 12/08 pair than in the 12/10 pair (Fauchez et al.,
2015). The V4 nighttime retrieval rate is larger than 70 % up to
εeff,12=0.95 for the 12/10 pair and up to
εeff,12=0.9 (τvis∼4.6)
for the 12/08 pair.
Fraction of (a)βeff12/10
and (b)βeff12/08 values above the
effective diameter retrieval sensitivity limit vs. effective emissivity at
12.05 µm in single-layered ice clouds over oceans between
60∘ S and 60∘ N in January 2008 in V4 (solid lines) and
in V3_comp (dashed lines) during night (blue) and day (red).
Changes in effective diameters
Because the changes in the microphysical indices are larger for the 12/08
pair than for the 12/10 pair, we now assess the changes in the respective
diameters, De12/08 and De12/10. For meaningful comparisons, the
exercise is carried out only for clouds for which both βeff12/10
and βeff12/08 are found above the lower sensitivity limit, both
in V3 and in V4. The changes in De12/10 and in De12/08 are
illustrated in Fig. 7a and b, respectively. The solid lines represent
median De12/k derived from V4 βeff12/k and the V4 SCO LUT.
The dashed lines represent median De12/k derived from V3_comp
βeff12/k and the same V4 SCO LUT, so the differences
between the solid and the dashed lines are due only to the different
microphysical indices. As a result of changes of different amplitude for
De12/10 and De12/08, the consistency between these two diameters
is drastically improved in V4 at εeff,12 smaller than 0.5.
Similar conclusions would be drawn using the V4 CO8 model.
(a) Median De12/10 and (b) median
De12/08 vs. effective emissivity at 12.05 µm
for the cloud population used in Fig. 6, except that both βeff12/10 and βeff12/08
are in the range of possible retrievals, both in V3_comp and
in V4. Solid line: V4 with SCO LUT; dashed lines: V3_comp
with V4 SCO LUT; dashed–dotted line: V3_comp with V3 solid
column LUT. Blue: night; red: day.
For a complete analysis of the differences between the V3_comp and V4 diameters,
the dashed–dotted lines show De12/k derived
using V3_comp and the V3 solid column LUT (Part I), so
the differences between the dashed–dotted lines and the dashed lines are due
only to the different LUTs. The changes resulting from the LUTs and from the
microphysical indices have an opposite effect, regardless of the specific V3
and V4 LUTs chosen for the analysis. As a result, De12/10 is overall
not changed significantly in V4 (solid lines) compared to V3_comp (dashed–dotted lines).
In contrast, De12/08 is smaller in V4 by up
to 15 µm at εeff,12<0.2, because the
improved (and increased) βeff12/08 has the largest impact, and
conversely V4 De12/08 is larger by up to 10 µm at
εeff,12 between 0.2 and 0.9.
V4 microphysical retrievals
We showed in Sect. 3.3 that the fraction of samples with possible
microphysical retrievals is significantly increased in V4 (Fig. 6), and that
the consistency between the De12/10 and De12/08 diameters is
drastically improved (Fig. 7). The significant disagreement between
De12/10 and De12/08 in V3_comp was due to biases of
different amplitude in βeff12/10 and βeff12/08, and
could not be explained by the possible use of an inappropriate ice habit
model. Both in V3 and in V4, De is retrieved using the ice habit model
found in best agreement with IIR in terms of relationship between
βeff12/10 and βeff12/08. Because the accuracy
of IIR βeff12/k is improved in V4, the residual discrepancies with respect to
the ice habit models are expected to be a genuine piece of information about
ice crystal shape. This requires both βeff12/k to be found
within the sensitivity range, which hereafter will be called “confident”
retrievals. Because the population of clouds meeting this requirement is
larger in V4 than in V3 and covers a larger range of optical depths, the
results in this section will be shown for V4 only.
Theoretically, confident retrievals should be found when De is smaller
than 120 µm and βeff12/k should tend to the upper
sensitivity limit for De>120µm. In practice,
uncertainties in βeff12/k can trigger non-confident retrievals
even if De is truly smaller than the sensitivity limit, and this is
more likely to occur when De is close to this limit. Requiring both
βeff12/k to be in the expected range of values is meant to
reinforce the confidence in the retrievals, but doing so implies no
systematic bias between both pairs of channels. This is not exactly true for
opaque clouds with εeff,12>∼0.8 (Fig. 6), and consequently the fraction of confident retrievals in
opaque clouds is often constrained by the 12/08 pair. Furthermore, the
fraction of confident retrievals at large emissivities is larger at night.
Effective diameter and ice water path
The histograms of confident De and ice water path retrievals (IWP) are
shown in Fig. 8a and b, respectively, for ST and opaque clouds, and
statistics are reported in Table 4. The IWP histograms are computed in
logarithmic scale between 0.01 and 1000 g m-2, with log10(IWP)
bins equal to 0.1. The random uncertainty in De, noted ΔDe, is computed based on the LUT selected for the retrieval and the
estimated random uncertainty in the βeff12/k indices. Median
ΔDe/De values reported in Table 4 are between 34 % and
49 %. The uncertainty in IWP is in large part driven by the uncertainty
in De.
Histograms of V4 confident retrievals of (a)De and (b) ice water path in single-layered
semi-transparent (ST; night: navy blue; day: red) and opaque (OP; night:
light blue; day: orange) ice clouds between 60∘ S and
60∘ N over oceans in January 2008.
Statistics associated with V4 effective diameter (De) and ice
water path (IWP) retrievals in single-layered ice clouds between
60∘ S and 60∘ N over oceans in January 2008 (see Fig. 8).
The ST clouds are optically thin, with median IIR τvis of only
0.2–0.26. Their nighttime (navy blue) and daytime (red) De
distributions are nearly identical, with median De=38–39 µm
and a peak around De=35µm. This peak compares well with the
mode at 36 µm noted by Dolinar et al. (2019) for single-layered ice
clouds with no detectable precipitation as retrieved using the combined
CloudSat-CALIPSO 2C-ICE product. IWP (Fig. 8b) is found between 0.03 and
100 g m-2 in ST clouds, with the slightly larger daytime values
being explained by the cloud selection and the slightly larger optical
depths in the daytime dataset (Table 4). The medium values are around 3 g m-2,
with peaks in the distributions at 3
and 8 g m-2 for nighttime and daytime data, respectively, and
the median relative uncertainty is 50 %. As noted by Berry and Mace (2014),
the CloudSat radar is typically insensitive to these thin layers, so
microphysical retrievals in combined CloudSat-CALIPSO products such as
2C-ICE rely on parameterization of the radar reflectivity (Deng et al.,
2015) rather than on actual observations. Combining CALIOP and IIR
observations appears to be a suitable alternative approach to characterize
these thin layers.
The estimated cloud radiative temperature (Tr) is at an equivalent
altitude located between the CALIOP cloud base and cloud top (Part I). While
in the case of ST clouds, IIR De is a layer average diameter, IIR De
in opaque clouds is mostly representative of the portion of the cloud seen
by CALIOP before the signal is totally attenuated. These opaque clouds have
median εeff,12 equal to 0.95 at night but only 0.86 for
daytime data, with median IIR τvis equal to 5.6 and 3.8,
respectively. Median De in opaque clouds is around 60 µm and the
distributions peak at 50 µm. It is larger than in ST clouds, which is
consistent with retrievals based on AIRS thermal infrared data (Guignard et
al., 2012; Kahn et al., 2018). The different nighttime and daytime De
and IWP distributions in opaque clouds are explained by the different ranges
of optical depth and the different amplitudes of the radiative temperature
correction (Figs. 2 and 4). In opaque clouds, the retrieved IWP lies between
10 and only 300 g m-2. The upper limit is due to the fact that
De cannot be larger than 120 µm and because cloud optical depths
inferred from IIR effective emissivities saturate and are typically smaller
than 15 (Fig. 4).
Ice habit model selection
Recall that De is retrieved using the habit model (SCO or CO8) that
agrees the best with IIR in terms of the relationship between
βeff12/10 and βeff12/08. As seen in Fig. 9, the SCO habit
model is selected in 80 % of the ST clouds of Tr<205 K.
This fraction steadily decreases down to 60 % as Tr increases up to
230 K (Fig. 9b) and remains stable above 230 K. This result is qualitatively
consistent with previous findings using V3 (Garnier et al., 2015), and, as
was discussed in this paper, both the IIR model selection and the mean
CALIOP integrated particulate depolarization ratio (in black in Fig. 9b)
indicate changes of crystal habit with temperature. In opaque clouds (Fig. 10),
both the IIR model selection and the CALIOP depolarization ratio
between 200 and 230 K are less temperature dependent than in ST clouds.
The difference between mean De12/10 and mean De12/08 in black and
gray in Figs. 9c and 10c is a measure of the residual mismatch between IIR
observations and the selected model. We see two temperature regimes, that
is, below and above 225 K, with a better agreement between IIR and the LUTs
at the warmer temperatures. This suggests that the V4 models are better
suited for warmer clouds and that they do not perfectly reproduce the
infrared spectral signatures of colder clouds composed of small crystals. It
is acknowledged that the highly variable ice particle shapes found in ice
clouds (Lawson et al., 2019 and references therein) are likely not fully
reproduced through the two models chosen for the V4 algorithm. It is further
noted that the Clouds and the Earth's Radiant Energy System (CERES) science
team is planning to use a two-habit model for retrievals in the
visible–near-infrared spectral domain (Liu et al., 2014; Loeb et al., 2018). This model
would be a mixture of two habits (single column and an ensemble of
aggregates) whose mixing ratio would vary with ice crystal maximum
dimension, with single columns prevailing for the smaller dimensions.
Interestingly, our findings appear to be consistent with this approach.
IIR V4 confident retrievals vs. radiative temperature in
semi-transparent ice clouds over oceans between 60∘ S and
60∘ N in January 2008. (a) Pixel count; (b) fraction of retrievals
using the SCO model (green) and mean CALIOP integrated particulate
depolarization ratio (black); (c) mean De (red)
± mean absolute deviation (shaded area), mean De12/10 (black) and mean
De12/08 (gray).
Same as Fig. 9 but for opaque clouds.
In both thin ST clouds (Fig. 9c) and opaque clouds (Fig. 10c), De
increases with cloud radiative temperature until it reaches a maximum value
around 250 K in ST clouds and 230 K in opaque clouds. Kahn et al. (2018)
found that for clouds of emissivity smaller than 0.98 (or τvis
smaller than about 8), De is maximum and around 50 µm at 230 K,
which is consistent with our findings, keeping in mind that clouds with
emissivity smaller than 0.98 are found in both our ST and opaque clouds. The
increase of cloud average De with cloud radiative temperature in ST
clouds (Fig. 9c) is in general agreement with numerous previous findings
(e.g., Hong and Liu, 2015). The decrease of De between
Tr=250 and 260 K for ST clouds is possibly due to an increasing fraction of small
liquid droplets in these prevailingly ice layers, which would be consistent
with the fact that the CALIOP integrated particulate depolarization ratio
decreases from 0.37 to 0.30 (Fig. 9b). Similar comments apply for opaque
clouds for Tr between 230 and 260 K. Using combined POLDER
(POLarization and Directionality of the Earth's Reflectances) and MODIS
data, Van Diedenhoven et al. (2020) found that De at the top of thick
clouds of optical depth larger than 5 is maximum at cloud top temperature
equal to 250 K, rather than Tr=230 K for opaque clouds. This
discrepancy might be partly explained if the cloud radiative altitude is
higher in the cloud than the cloud top derived from the visible
observations, which could also explain that De shown in van Diedenhoven
et al. (2020) is larger than that in this study.
Retrievals using parameterizations from in situ formulation
The IIR algorithm takes advantage of the relationship between
βeff12/10 and βeff12/08 to identify the ice habit model
that best matches the observations and thereby provide information about
both ice crystal shape and effective diameter. Another approach would be to
use only βeff12/10 and prescribed LUTs. This approach was
adopted by Mitchell et al. (2018), who derived four sets of LUTs using
extensive in situ measurements rather than pure modeling. In Part I, we
compared these four sets of βeff12/10–De relationships with
the relationships derived from the V4 SCO and CO8 models. The four sets of
De derived from βeff12/10 using this independent approach are
reported in the IIR product for the user's convenience. Figure 11 compares
De computed by the analytic function derived by Mitchell et al. (2018)
with De12/10 from the CO8 and the SCO models. Relationships derived
from the Small Particles in Cirrus
Science and Operations Plan (SPARTICUS) (blue) and the Tropical Composition,
Cloud, and Climate Coupling (TC4) (red) field campaign were computed in
two ways: by setting the first bin of the measured particle size
distribution (PSD) (D<15µm) to 0 (i.e., N(D)1=0,
dashed lines) and without modifying the distribution (i.e., N(D)1
unmodified, solid lines). As discussed in Part I, the differences between
the six sets of retrievals illustrate the possible impacts of the LUTs and
of the PSDs. Because the presence of small particles in the unmodified PSD
causes βeff12/10 to increase faster than De, assuming
N(D)1=0 yields smaller values of De for a given
βeff12/10 than when N(D)1 is not modified. Even though this was
not the original intent, comparing median De with or without setting
N(D)1 to 0 also illustrates the impact of possible vertical
inhomogeneities of De within the cloud layer (Zhang et al., 2010).
Nevertheless, the overall impact of vertical variations on βeff12/10
also depends on the in-cloud IIR weighting function, which is
related to the cloud extinction profile (Part I). In Mitchell et al. (2020),
the mean De calculated from the SPARTICUS unmodified βeff12/10–De relationship (applied at midlatitudes) and the TC4 N(D)1=0βeff12/10–De relationship (applied in the tropics)
was compared against the in situ climatology of mean volume radius, Rv,
reported in Krämer et al. (2020) after converting De to Rv.
The retrieved Rv tended to be no more than ∼20 %
smaller than the in situ Rv for temperatures between 208 and 233 K.
Median De12/10 from the V4 CO8
(purple) and SCO (green) models, and from analytical functions derived by
Mitchell et al. (2018) during the SPARTICUS (blue) and TC4 (red) field
experiments using N(D)1 unmodified (solid) or
N(D)1=0 (dashed). This is the same dataset as the one in
Fig. 9.
Comparisons with MODIS
Figure 12 compares IIR confident retrievals and co-located MODIS/Aqua
Collection 6 daytime retrievals from the visible–2.1 µm and visible–3.7 µm
pairs of channels (Platnick et al., 2017, and references therein) in
single-layered clouds classified as high-confidence ROIs by CALIOP and as ice
clouds by MODIS. MODIS τvis and De at 1 km resolution are
from the MYD06 product and co-location with CALIPSO is from the AERIS/Cloud-Aerosol-Water-Radiation Interactions (AERIS/ICARE)
CALTRACK product. Analyses are over oceans between 30∘ S and
30∘ N in January 2008 separately for CALIPSO ST and opaque clouds.
Figure 12a and b show the population of clouds with IIR retrievals
(black), with MODIS retrievals at both 2.1 and 3.7 µm (brown), and
with both IIR and MODIS retrievals (orange) for which comparisons in Fig. 12c–f are shown.
Figure 12a and b characterize these cloud populations as
a function of IIR Tr and IIR εeff,12, respectively.
For ST clouds (thin lines), the IIR–MODIS comparisons are constrained by the
availability of MODIS retrievals, and the compared ST clouds have
εeff,12 typically larger than 0.2 (Fig. 12b). In
contrast, comparisons in opaque clouds (thick lines) are limited by the
availability of IIR retrievals. Figure 12c and e show median De from
IIR (red), MODIS 2.1 (green) and MODIS 3.7 (blue) vs. Tr for ST (Fig. 12c)
and opaque (Fig. 12e) clouds. The vertical lines are between the
25th and 75th percentiles. Similarly, Fig. 12d and f show the
corresponding τvis values. Only one MODIS τvis is
shown because the retrievals from both pairs of MODIS channels are nearly
identical.
IIR and MODIS comparisons over oceans between
30∘ S and 30∘ N in January 2008 for single-layered
high-confidence ROI clouds with MODIS ice phase. Distributions of (a) IIR
radiative temperature and (b) IIR effective emissivity at 12.05 µm in
ST (thin lines) and opaque (thick lines) clouds where IIR has confident
retrievals (black), MODIS has successful retrievals at 2.1 and 3.7 µm (brown),
and both IIR and MODIS retrievals are successful and can
be compared (orange). Median De vs.
Tr from IIR (red), MODIS 2.1 (green) and MODIS 3.7
(blue) in ST (c) and opaque (e) clouds; median τvis
from IIR (red) and MODIS (green) in ST (d) and
opaque (f) clouds. The vertical bars in panels (c–f) are between the
25th and 75th percentiles.
For ST clouds, MODIS 2.1 De is larger than IIR by 15 µm on
average. IIR and MODIS 3.7 De are in good agreement for Tr<205 K,
where De is <40µm and IIR τvis is <0.5,
and they progressively depart from each other as
Tr increases and MODIS 3.7 increases and approaches MODIS 2.1. MODIS
τvis is larger than IIR by 0.3 to 0.2. This small but systematic
bias is not seen when comparing CALIOP and IIR (not shown). The MODIS 2.1
De–Tr relationships are similar for ST and opaque clouds, which
is not the case for MODIS 3.7 and IIR. For opaque clouds, IIR De is
larger than in ST clouds and is in good agreement with MODIS 2.1 at
Tr<225 K. MODIS 3.7 De exhibits a similar increase with
temperature as seen with the two other datasets, but it is shifted by
-10µm. At Tr>225 K, MODIS De 2.1 continues to
increase up to 100 µm at 255 K, whereas IIR remains stable around
60 µm and MODIS 3.7 increases slowly to approach the same plateau as IIR
around 60 µm. As seen in Fig. 12f, both MODIS and IIR indicate moderate
optical depths in these opaque clouds where comparisons are possible, with
median values ranging between 2.5 and 6 at Tr<250 K, IIR
being smaller than MODIS by about 0.4.
Kahn et al. (2015) found that MODIS 2.1 De is typically larger than
AIRS De by 10–20 µm, and that MODIS 3.7 is in better agreement
with AIRS on average. These results, which were for clouds of optical depth
between 0.5 and 2 over oceans, are consistent with our findings for ST
clouds. The MODIS and IIR techniques exhibit different non-linear
sensitivities to particle size, so vertical inhomogeneities of the
effective diameter can yield three different retrieved De values (Zhang et
al., 2010). This could explain that IIR De is found in better agreement
with MODIS 3.7 in ST clouds while MODIS 2.1 is clearly larger (Zhang et al.,
2010). At Tr>220 K, IIR De is around 50–60 µm
and smaller than both MODIS 2.1 and 3.7. We note that the agreement with
MODIS would be improved using the parameterized functions derived from the
unmodified in situ PSDs that were presented in Sect. 3.4.3 but that the
modified PSDs would yield similar results. For clouds of moderate optical
depth as found in our population of opaque clouds, MODIS 3.7 is very
sensitive to cloud top while MODIS 2.1 senses deeper into the cloud (Zhang
et al., 2010; Platnick, 2000), and the smaller MODIS 3.7 De as observed
in Fig. 12e suggests that the effective diameter is smaller at cloud top
than deeper into the cloud. IIR De might be larger than MODIS 3.7 and
in better agreement with MODIS 2.1 for opaque clouds at Tr<220 K because the IIR weighting function is deeper in the cloud than at
3.7 µm, which is agreement with simulations by Zhang et al. (2010).
In conclusion, distinct sensitivity to possible cloud vertical and
horizontal (Fauchez et al., 2018) inhomogeneity likely contributes to the
observed differences.
V4 retrievals in liquid water clouds
The only difference between effective emissivity V4 retrievals in liquid and
ice clouds is that Tr is taken as the temperature at the CALIOP
centroid altitude (Tc) in the case of liquid water clouds, whereas this
initial temperature estimate is further corrected in the case of ice clouds. It
is recalled that De of liquid droplets are retrieved using the water
LUTs (Part I) and that liquid water path is derived from De and
εeff,12 (Eq. 12 in Part I).
Following a similar approach as for ice clouds, the results are shown for
scenes over oceans between 60∘ S and 60∘ N that contain
a single cloud layer classified as high-confidence water by the CALIOP
phase algorithm. Because liquid water clouds are statistically warmer than
ice clouds, the radiative contrast is typically smaller than for ice clouds.
Because uncertainties are inversely proportional to this radiative contrast
(Part I), they increase very rapidly when the radiative temperature
contrast, that is the difference between the clear air TOA background
brightness temperature and the TOA blackbody brightness temperature, is
smaller than 10 K. In order to prevent very large uncertainties associated
with very small radiative contrast, the results are presented for clouds in
the free troposphere with centroid altitude above 4 km. For this cloud
population, the radiative temperature contrast is larger than 10 K, and it
increases on average from 15 K at 4 km to 50 K at 10 km where the highest
water clouds are found (not shown). Most of these sampled liquid clouds are
composed of supercooled droplets.
Our results are presented in Sect. 4.1 to 4.3 and comparisons with MODIS are
shown in Sect. 4.4.
Effective emissivity in channel 12.05
Figure 13a and b show the distributions of V4 εeff,12
in ST and opaque liquid water clouds, respectively, for the month of January 2008
between 60∘ S and 60∘ N over ocean, for clouds with
centroid altitude >4 km. Figure 13c and d show the respective
median random uncertainties, which are about twice as large as the
uncertainties in ice clouds (Fig. 2c and d) because of the smaller
radiative contrast. Only 17 % of these clouds are ST (Fig. 13a and c).
Unlike in ST ice clouds, the distributions peak at εeff,12∼0.2,
and non-physical negative emissivity values are found
in only 2 % of the pixels. The εeff,12 distributions
in opaque clouds peak at 1.02 at night and at 0.99 for daytime data, with an
estimated uncertainty of ±0.06. The spread around these peaks is
larger than for ice clouds, which is explained by the larger uncertainties
and specifically to a larger sensitivity to a wrong estimate of Tr.
Thus, the nighttime and daytime fractions of samples with
εeff,12>1, for which no microphysical retrievals are
possible, are 45 % and 27 %, respectively. The daytime distributions
in opaque clouds exhibit a tail down to εeff,12∼0.4,
while at night, the lowest εeff,12 is ∼0.65, which is very similar to what was observed for
opaque ice clouds (Fig. 2b). This similarity suggests that emissivity
retrievals in ice and liquid water clouds are consistent, notwithstanding
the unavoidable larger uncertainties in the latter ones.
V4 effective emissivity distribution at 12.05 µm
in (a) ST and (b) opaque single-layered liquid water clouds of centroid
altitude >4 km over oceans between 60∘ S and
60∘ N in January 2008 for nighttime (blue) and daytime (red) data.
Panels (c) and (d) are the V4 median random uncertainties corresponding to
panels (a) and (b), respectively.
Inter-channel effective emissivity differences
The variations with εeff,12 of the V4 Δεeff12-k inter-channel effective emissivity differences
for the 12–10 and 12–08 pairs are shown in Fig. 14a and b, respectively.
The nighttime (blue) and daytime (red) curves are median values, and the
shaded gray areas are between the V4 nighttime 25th and 75th
percentiles. As for ice clouds, both Δεeff12-k
tend nicely to 0 at εeff,12∼0, due to
the improved computed background radiances demonstrated previously, which
has a beneficial effect on retrievals in any ST layer. Both Δεeff12-k have a second minimum at εeff,12∼1,
as expected, and this minimum is found
slightly larger than 0. Both Δεeff12-k values and
therefore both βeff12/k values are notably larger than for ice clouds
(see Fig. 3), reflecting the presence of smaller particles in the liquid
water distributions (Giraud et al., 2001; Mitchell and d'Entremont, 2012).
As shown by Avery et al. (2020), the IIR microphysical indices are
unambiguously larger in clouds classified as liquid water by the CALIOP
phase algorithm than in clouds classified as ice.
V4 IIR inter-channel (a)Δεeff12–10
and (b)Δεeff12–08 effective emissivity differences vs.
effective emissivity at 12.05 µm in single-layered liquid water
clouds of centroid altitude >4 km over oceans between
60∘ S and 60∘ N in January 2008. The blue and red curves
are median values for nighttime and daytime data, respectively. The shaded
gray areas are between the V4 nighttime 25th and
75th percentiles.
Microphysical retrievalsEffective diameter and liquid water path
As previously, retrievals are deemed confident when both βeff12/k
are found within the sensitivity range, which corresponds to
De=60µm for liquid clouds. The fraction of confident
retrievals is found similar in liquid water clouds of centroid altitude
>4 km and in ice clouds. Following the same presentation as for
ice clouds, the histograms of confident De and liquid water path (LWP)
retrievals are shown in Fig. 15a and b, respectively, for ST and
opaque clouds, and statistics are reported in Table 5.
Histograms of V4 confident retrievals of (a)De and (b) liquid water path in single-layered
semi-transparent (ST; night: navy blue; day: red) and opaque (OP; night:
light blue; day: orange) liquid water clouds of centroid altitude
>4 km between 60∘ S and 60∘ N over oceans in
January 2008.
Statistics associated to V4 effective diameter (De) and LWP retrievals in single-layered liquid water clouds of
centroid altitude >4 km between 60∘ S and
60∘ N over oceans in January 2008 (see Fig. 15).
Note that the IIR retrievals shown in Fig. 15 are for a population of
optically thin water clouds: median τvis is only 0.9 in ST clouds
and between 4 and 5 in opaque clouds. Both in ST and in opaque clouds, the
nighttime and daytime De histograms are similar. In ST clouds, median
De is 13 µm and median liquid water path is 3.4 g m-2 with
a median random uncertainty of 1.2 g m-2. In opaque clouds,
median De is 18 µm and median liquid water path is
25–31 g m-2 with a median random uncertainty of 10–15 g m-2. The maximum
retrieved LWP is about 100 g m-2, consistent with the infrared
saturation range of 40–60 g m-2 reported by Marke et al. (2016) who
combined microwave and infrared ground-based observations to improve LWP and
De retrievals in “thin” clouds that they defined as
LWP <100 g m-2. The authors report De between 10 and 14 µm in “thin”
clouds of top altitude <∼1 km, which agrees well
with the peaks of our distributions.
Analyses vs. radiative temperature
IIR retrievals in ST liquid water clouds are shown in Fig. 16 as a function
of Tr, highlighting that most of these liquid clouds of centroid
altitude >4 km are supercooled, with Tr ranging between 235
and 280 K (Fig. 16a). Mean IIR De (Fig. 16b, red) increases steadily
from 11 µm at 242 K to 18 µm at 270 K, while mean CALIOP
particulate depolarization ratio (Fig. 16c) is constant and around 0.1.
These thin clouds are likely radiation driven, and the increase of layer
average De with layer radiative temperature could indicate growth
through vapor deposition. In addition, there is an increasing probability
for supercooled droplets to freeze as temperature decreases. As Tr
decreases from 242 to 235 K, the number of samples drops quickly, De
increases up to 24 µm, and CALIOP depolarization ratio increases very
significantly, confirming a rapid transition to ice phase. At Tr>270 K, De continues to increase slightly up to 20 µm,
while CALIOP integrated particulate depolarization ratio decreases. As
seen in Fig. 16b, De12/10 and De12/08 are in fair agreement. The
mean De12/10-De12/08 difference
increases from -2µm at 275 K to +3µm at 245 K. This slight temperature-dependent discrepancy
between the IIR observations and the water LUT could be explained by the
fact that the complex refractive index is temperature dependent, as reported
by Zasetsky et al. (2005) and Wagner et al. (2005), the complex refractive
index of supercooled water being intermediate between warm water and ice
(Rowe et al., 2013). Further investigations will be carried out to establish
whether the residual discrepancy between De12/10 and De12/08 would
be reduced by using a new set of temperature-dependent indices, following
the approach in Rowe et al. (2013). Nevertheless, these simple observations
give confidence in the new V4 IIR De retrievals in ST liquid clouds.
IIR confident retrievals vs. radiative temperature in ST
liquid water clouds of centroid altitude >4 km over
oceans between 60∘ S and 60∘ N in January 2008.
(a) Pixel count; (b) mean De (red),
De12/10 (black) and De12/08
(gray); (c) mean CALIOP integrated particulate depolarization ratio. The
shaded areas in panels (b) and (c) represent mean ± mean absolute
deviation.
Comparisons with MODIS
IIR confident retrievals in liquid water clouds were compared with MODIS
Collection 6 retrievals from the visible–2.1 µm and
visible–3.7 µm pairs of channels for clouds also classified as liquid water by
MODIS. The results are shown in Fig. 17, following the same presentation as
in Fig. 12 for ice clouds. Again, cloud centroid altitude is chosen to be
higher than 4 km, and, as previously for ice clouds, the comparisons shown
in Fig. 17c–f are limited to those pixels for which the IIR, MODIS 2.1 and
MODIS 3.7 retrievals (orange curves in Fig. 17a and b) were all
successful. As seen in Fig. 17a, Tr spans between 235 and 280 K, and
most of these sampled clouds are composed of supercooled droplets. In ST
clouds, the three datasets show an increase of median De (Fig. 17c) as
Tr increases from 243 to 270 K but with different slopes: IIR
De increases with Tr from 10 to 20 µm, whereas both MODIS
2.1 and 3.7 are larger than about 20 µm, and the differences between
IIR and MODIS decrease as temperature increases. As seen in Fig. 17d, these
supercooled water clouds have optical depths between 1.5 and 2 according to
MODIS, whereas IIR τvis is 30 % to 40 % smaller. In contrast,
the three sets of De exhibit similar variations with Tr in opaque
clouds (Fig. 17e). IIR De (red) is systematically smaller than MODIS
2.1 (green), by 8 µm on average. This is fairly consistent with
findings by Di Noia et al. (2019) who compared MODIS 2.1 with new retrievals
from POLDER-3 measurements, and found that MODIS 2.1 effective radius was
larger by about 3 µm (De larger by 6 µm) for high oceanic
clouds having pressures lower than 600 hPa. MODIS 3.7 retrievals (blue) are
weighted closer to the top of the cloud than the corresponding MODIS 2.1
retrievals (Platnick, 2000), and are larger than IIR De estimates by
only 3 µm. This is encouraging, despite of the seemingly
temperature-dependent discrepancy between MODIS and IIR τvis
(Fig. 17f), where median IIR τvis (red) saturates around
τvis=5, while median MODIS increases up to 15 at 240 K. More work is
necessary to understand these differences.
IIR and MODIS comparisons over oceans between
60∘ S and 60∘ N in January 2008 for single-layered
high-confidence liquid water clouds of centroid altitude >4 km with
MODIS water phase. Distributions of (a) IIR radiative temperature and
(b) IIR effective emissivity at 12.05 µm in ST (thin lines) and
opaque (thick lines) clouds where IIR has confident retrievals (black),
MODIS has successful retrievals at 2.1 and 3.7 µm (brown), and
both IIR and MODIS retrievals are successful and can be compared (orange).
Median De vs. Tr from IIR
(red), MODIS 2.1 (green) and MODIS 3.7 (blue) in ST (c) and opaque (e)
clouds; median τvis from IIR (red) and MODIS
(green) in ST (d) and opaque (f) clouds. The vertical bars in panels (c–f)
are between the 25th and 75th
percentiles.
Conclusions and perspectives
This paper describes the impacts of the various changes implemented in the
V4 IIR Level 2 algorithm on the effective emissivities and microphysical
retrievals in ice clouds. We chose to illustrate and discuss the changes for
1 month's worth of data over ocean using a step-by-step approach so that
data users can understand the differences and improvements that they should
expect when using the recently released V4 IIR Level 2 data products.
Retrievals in liquid water clouds, which were added in V4, are also
presented. The IIR retrievals rely heavily on the scene classification
reported for exactly co-located CALIOP observations. The results are
presented for single-layer cases having the ocean surface as a reference and
for which the CALIOP cloud classification and ice–water phase identification
are determined with high confidence.
We show that in tenuous ST clouds, emissivity retrievals derived from both
observed and computed background radiances are fully consistent in V4,
whereas the inter-channel biases that were observed in V3 when the
background radiance had to be computed introduced significant biases into
the V3 microphysical retrievals. Our assessment is based on internal control
criteria; i.e., the analysis of retrieved inter-channel effective emissivity
differences at εeff,12∼0. Because the
background radiance has to be computed for approximately 70 % of the
retrievals in ST clouds, the number of unbiased emissivity retrievals is
increased by a factor of 3 in V4. In V4, the lowest effective emissivity for
which microphysical retrievals are possible in more than 80 % of the
pixels is reduced to ∼0.05 (or τvis∼0.1). In contrast, this lowest emissivity limit in V3 was as
high as 0.25 in those cases of computed background radiances and was driven
by the large biases in the 12/08 pair. Furthermore, when microphysical
retrievals were possible in V3, the different 12–10 and 12–08 inter-channel
biases induced large differences between the De12/10 and De12/08
diameters retrieved from the respective microphysical indices.
Perhaps one unique feature of the IIR algorithm is that the ice habit model
is selected according to the relationship between the βeff12/10
and βeff12/08 inter-channel microphysical indices. In V4, the
TAMUice2016 SCO (severely roughened single column) model is selected in
80 % of the cases in ST clouds at Tr<210 K, and this
fraction decreases at larger temperatures. The TAMUice2016 CO8
(severely roughened eight-element column aggregate) model is selected in 40 %
of the cases when clouds have radiative temperatures larger than 230 K. In
ice clouds, De12/10 is on average smaller than De12/08, with
larger discrepancies below 230 K than above. Employing a technique similar
to the IIR algorithm, Heidinger et al. (2015) also noticed differences
between effective diameters retrieved from the MODIS/Aqua 32/31 and 31/29
pairs of channels when using the TAMUice2013 CO8 model (Yang et al.,
2013), which was chosen for the MODIS Collection 6 data products for its
consistency between visible and thermal infrared optical depth retrievals
(Holz et al., 2016). We could not find a perfect agreement between
De12/10 and De12/08 in liquid water clouds supposedly composed of
spherical droplets. In the range of temperature between 240 and 260 K,
where both ice and liquid water clouds are found, De12/10 is larger
than De12/08 in liquid water clouds, while it is smaller in ice clouds,
suggesting that these mismatches are not due to undetected residual biases
in the IIR microphysical indices but instead to our LUTs. As noted earlier,
the residual mismatch in liquid water clouds could be explained by
inaccuracies in the refractive indices, which are taken constant, whereas
temperature-dependent indices have been reported (Zasetsky et al., 2005;
Wagner et al., 2005). Likewise, the TAMUice2016 single scattering
properties are derived using refractive indices at 266 K (Warren and Brandt,
2008), but Iwabuchi and Yang (2011) reported that the temperature dependence
of these properties in the thermal infrared is small but not negligible.
While in V3 mismatches between IIR retrievals and the LUTs were largely due
to inter-channel biases in the IIR retrievals, the improved accuracy in V4
opens the possibility for more detailed comparisons with the theory or
modeling.
Retrievals in opaque ice clouds are improved in V4, especially at night and
for 12/10 pair of channels, due to corrections of the radiative
temperature estimates. Refining the relationship between lidar geometric
altitudes and infrared radiative temperature based on theoretical
considerations (Part I) is deemed important per se, and quasi-perfectly
co-located IIR and CALIOP observations offer a unique opportunity to test
our theoretical approach. To make further progress in this topic and assess
the V4 radiative temperature estimates in opaque clouds, the next step will
be to use CloudSat extinction profiles from the lower parts of the clouds
not seen by CALIOP.
Daytime comparisons with MODIS/Aqua Collection 6 data products are presented
for co-located pixels where V4 IIR, MODIS 2.1 and MODIS 3.7 all have
successful retrievals. This comparison demonstrated that IIR is best suited
for retrievals in tenuous clouds of emissivity <0.2, while MODIS is
more efficient for denser clouds of emissivity >0.8. IIR De
is in better agreement with MODIS 3.7 than with MODIS 2.1 in tropical ST ice
clouds at Tr<200 K. In contrast, IIR De is in agreement
with MODIS 2.1 in tropical opaque ice clouds at Tr<205 K and
in fair agreement with MODIS 3.7 at warmer temperatures. For opaque liquid
water clouds having centroid altitudes greater than 4 km, so chosen to
ensure sufficient radiative temperature contrast for the IIR retrievals, IIR
De is systematically smaller than MODIS 2.1 by 8 µm and smaller
than MODIS 3.7 by 3 µm. The IIR technique appears to be perfectly
suited for retrievals in ST supercooled liquid water clouds.
Data availability
The version 3 IIR Level 2 track products used in this paper are available at
https://doi.org/10.5067/IIR/CALIPSO/L2_Track-Beta-V3-01 (last
access: 14 September 2020) (NASA, 2011) and the version 4 IIR Level 2 track products are
available at https://doi.org/10.5067/CALIOP/CALIPSO/CAL_IIR_L2_Track-Standard-V4-20
(last access: 14 September 2020) (NASA, 2020).
The IIR Level 2 track products are also available from the AERIS/ICARE Data and Services Center (http://www.icare.univ-lille.fr, AERIS/ICARE, last access: 22 April 2021) (AERIS/ICARE, 2021).
For comparisons with MODIS Collection 6, co-location and MODIS visible–2.1 µm data are from the CALTRACK-5km_MYD06.v1.01 products and MODIS visible–3.7 µm data were extracted from the Collection 6 MYD06 products. These products are available from the AERIS/ICARE Data and Services Center (http://www.icare.univ-lille.fr, AERIS/ICARE, last access: 22 April 2021) (AERIS/ICARE, 2021).
Author contributions
AG and JP defined the content and methodology of the paper and wrote the
original draft. AG performed the data analysis and prepared the figures. NP
was in charge of software development. MAV provided assistance for the use of
the CALIOP data. PD provided the FASRAD and FASDOM radiative transfer models
and bulk scattering properties. PY provided the ice habit models from the
TAMUice2016 database. DLM provided the analytical functions derived from
in situ measurements. All authors contributed to the review and editing of
this paper.
Competing interests
Jacques Pelon is a co-guest editor for the “CALIPSO Version 4
Algorithms and Data Products” special issue in Atmospheric Measurement Techniques but did not participate in any aspects of the editorial review
of this paper. All other authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “CALIPSO version 4 algorithms and data products”. It is not associated with a conference.
Acknowledgements
The authors are grateful to NASA LaRC, SSAI (Science Systems and
Applications, Inc.), the Centre National d'Etudes Spatiales (CNES), and
Institut National des Sciences de l'Univers (INSU) for their support. We
thank the AERIS infrastructure for providing access to the CALIPSO products
and for data processing during the development phase. We thank Brian Getzewich
and Tim Murray for the processing of the version 4 IIR Level 2
data at NASA LaRC.
Review statement
This paper was edited by Vassilis Amiridis and reviewed by four anonymous referees.
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