AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-3215-2017Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensingEhrlichAndréa.ehrlich@uni-leipzig.dehttps://orcid.org/0000-0003-0860-8216BierwirthEikeIstominaLarysaWendischManfredhttps://orcid.org/0000-0002-4652-5561Leipzig Institute for Meteorology (LIM), University of Leipzig, Leipzig, GermanyInstitute of Environmental Physics, University of Bremen, Bremen, Germanynow at: PIER-ELECTRONIC GmbH, Nassaustr. 33–35, 65719 Hofheim-Wallau, GermanyAndré Ehrlich (a.ehrlich@uni-leipzig.de)4September20171093215323016February201714March201730June201731July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/3215/2017/amt-10-3215-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/3215/2017/amt-10-3215-2017.pdf
The passive solar remote sensing of cloud properties over highly
reflecting ground is challenging, mostly due to the low contrast
between the cloud reflectivity and that of the underlying
surfaces (sea ice and snow). Uncertainties in the retrieved
cloud optical thickness τ and cloud droplet effective
radius reff,C may arise from uncertainties in the
assumed spectral surface albedo, which is mainly determined by
the generally unknown effective snow grain size reff,S.
Therefore, in a first step the effects of the assumed snow grain
size are systematically quantified for the conventional
bispectral retrieval technique of τ and reff,C
for liquid water clouds. In general, the impact of uncertainties
of reff,S is largest for small snow grain sizes. While
the uncertainties of retrieved τ are independent of the
cloud optical thickness and solar zenith angle, the bias of
retrieved reff,C increases for optically thin clouds
and high Sun. The largest deviations between the retrieved and
true original values are found with 83 % for τ and 62 %
for reff,C.
In the second part of the paper a retrieval method is presented that
simultaneously derives all three parameters (τ, reff,C, reff,S) and therefore accounts for changes
in the snow grain size. Ratios of spectral cloud
reflectivity measurements at the three wavelengths
λ1=1040nm (sensitive to reff,S),
λ2=1650nm (sensitive to τ), and
λ3=2100nm (sensitive to reff,C) are
combined in a trispectral retrieval algorithm. In a feasibility
study, spectral cloud reflectivity measurements collected by the
Spectral Modular Airborne Radiation measurement sysTem (SMART)
during the research campaign Vertical Distribution of Ice in
Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to
test the retrieval procedure. Two cases of observations above
the Canadian Beaufort Sea, one with dense snow-covered sea ice
and another with a distinct snow-covered sea ice edge are
analysed. The retrieved values of τ, reff,C, and
reff,S show a continuous transition of cloud properties
across snow-covered sea ice and open water and are consistent
with estimates based on satellite data. It is shown that the
uncertainties of the trispectral retrieval increase for high
values of τ, and low reff,S but nevertheless allow
the effective snow grain size in cloud-covered areas to be estimated.
Introduction
During boreal winter, 15 % of the Earth's surface is covered by
snow and sea ice , while clouds cover roughly
two-thirds of the globe . Both snow and clouds
considerably increase the reflected solar radiation at the top of
the atmosphere and therefore are essential for determining the
Earth's radiative energy budget. Changes in ice, snow, and cloud
cover are both the result and cause of climate warming in Arctic
areas . However, while snow and sea ice
exhibit considerable seasonal variation, clouds usually vary on
shorter timescales and smaller spatial scales. Therefore, continuous
observations of cloud and snow properties are important. Commonly,
satellite remote sensing techniques are applied to monitor the
relevant radiative properties of clouds and snow, such as cloud
optical thickness, cloud droplet effective radius, snow cover,
effective snow grain size, and soot concentration e.g.
.
In polar regions clouds cover large areas of snow, glaciers, and
sea ice, which complicates the retrieval of their properties
because the contrast between these bright surfaces and the clouds
is low. At wavelengths typically used to retrieve cloud properties
from solar spectral reflectivity measurements, the optical
properties of clouds and snow are similar. Therefore, cloud
retrieval algorithms utilize observations at wavelengths larger
than 1000 nm where the snow albedo is lower. Measurements
at 1500 nm wavelength, where snow strongly absorbs solar
radiation, were used by and
to improve the cloud detection above snow surfaces. Cloud optical
thickness and droplet effective radius were successfully retrieved
above snow surfaces by selecting a channel combination of the
retrieval algorithm in the near-infrared spectral region. For example
the combination of the 1640 and 2130 nm band
was used for the Moderate Resolution Imaging Spectroradiometer
(MODIS), while a combination of the 1240 and
1640 nm band was applied for the Visible Infrared Imaging
Radiometer Suite (VIIRS) .
However, the cloud reflectivity in this spectral range is also affected by
changes in the spectral albedo of the snow surface .
The smaller the snow grains, the higher the surface albedo and the more the
radiation is reflected by the surface. Therefore, the retrieval of cloud
properties (optical thickness and droplet effective radius) over snow
surfaces requires a precise assumption of the effective snow grain size below
the clouds. Snow grain size varies temporally and spatially due to precipitation
that decreases the snow grain size and because of the snow metamorphism that slowly increases the snow grain size e.g.
. In polar areas, the effective snow grain size
typically ranges between 50 µm for freshly fallen snow and
1000 µm for aged snow . This snow metamorphism
changes the broadband surface albedo by 14 % from 0.89 to 0.77. Most of
this effect on albedo occurs at longer wavelengths at which the imaginary
part of the refractive index of ice is high. Therefore, the decrease of the
spectral albedo at 1300 nm is enhanced to 65 % from 0.75 to 0.26
. In midlatitude areas the snow metamorphism is often
accelerated by higher temperatures, which can lead to effective snow grain
sizes of up to 3000 µm causing an even stronger reduction of snow
albedo . Also white sea ice that is not
covered by snow and not water saturated, i.e. dry white ice, can be
characterized by larger effective snow grain sizes . This
is because at wavelengths larger than 1000 nm the albedo of white sea
ice is lower than that of snow-covered sea ice. However, most cloud retrieval
techniques do not consider a variation of the snow and sea ice albedo. For
the MODIS cloud product Collection 6, a fixed surface albedo of 0.03 for both
wavelength bands (1640 and 2130 nm) is assumed over sea ice or
snow-covered areas . In contrast to land surfaces, no
spatial or temporal changes in the snow and sea ice albedo are considered.
Uncertainties of the surface albedo affect the retrieval of cloud
optical properties, which has been shown by ,
, and . These studies
focus on optically thin clouds over typical land surfaces with a
high variability of the spectral albedo at wavelengths below
1 µm. For an optically thin cirrus,
estimated retrieval uncertainties of up to 50 % for the ice
crystal effective radius depending on cloud optical thickness.
showed that the retrieval uncertainties of
thin cirrus can be reduced by 20 % for optical thickness and by
45 % for ice crystal effective radius when a reasonable estimate
of the surface albedo is applied. For snow-covered areas, only the
difference between snow/sea ice albedo and the albedo of the sea
ice-free ocean has been addressed in improved retrieval algorithms
. So far, no estimates of the effect of a
varying snow albedo on cloud retrievals have been reported in the
literature.
For satellite observations of spectral solar radiation, retrieval
algorithms that provide effective snow grain size have been
developed by and . These
techniques exploit the dependence of spectral snow albedo and
reflectivity and effective snow grain size. A larger effective
snow grain size increases the photon path length within the snow
grain and hence the probability that radiation is absorbed in the
snow layer, which reduces the snow albedo. As thin snow
layers already dominate the radiation reflection by the surface, the
retrievals are most sensitive to the uppermost snow layer and,
thus, changes by precipitation, snow metamorphism, and the
concentration of impurities are of utmost importance
. The retrieval methods by
and use these sensitivities
and estimate the black carbon concentration in the snow, which
mostly affects the visible range of the spectral albedo.
Unfortunately, these satellite retrievals of snow properties do
not cover the full spatial and temporal evolution of effective
snow grain size and snow albedo as they are limited to cloud-free
areas . However, cloud layers in
polar regions are often observed to prevail for several days
. In such a case, the
cloud retrieval may suffer from an outdated assumption of snow or
sea ice albedo. A correct solution is only possible if snow and
cloud properties are determined in combination as suggested in
this paper.
Measurements of spectral cloud reflectivity have been successfully
applied to distinguish between liquid and ice water clouds
. Making use of
differences in the spectral absorption of liquid water and ice,
which are manifested in the spectral shape of the cloud
reflectivity, several indices are defined to identify the dominant cloud phase.
Similarly, this study makes use of the different
spectral absorption characteristics for snow surfaces and liquid
water clouds to retrieve cloud and snow properties simultaneously.
To illustrate the need for such retrieval methods, the uncertainties
due to uncertainties in the assumed snow albedo on the retrieval
of cloud properties will be quantified in
Sect. . The new retrieval method will be
introduced in Sects. and
including the identification of the most suitable wavelengths applied
in the retrieval as well as the forward simulations of cloud
reflectivity. In a feasibility study in Sect. ,
the algorithm that is limited to cases of liquid water clouds is
applied to two specific cases, which have been observed by
airborne spectral cloud reflectivity measurements during the field
campaign Vertical Distribution of Ice in Arctic Clouds (VERDI)
over the Canadian Beaufort Sea in 2012.
Comparison of spectral snow albedo ρλ(a) and
cloud reflectivity γλ(b) for different reff,S and τ, respectively. Cloud reflectivity has been
simulated for clouds with reff,C=10µm that
are located above a snow surface with reff,S=100µm. The imaginary part of the refractive
index of ice and liquid water is given in panel (c). Vertical
lines indicate the wavelength used in the bispectral cloud
retrieval.
Uncertainties of bispectral cloud retrieval over snowForward simulations
Based on radiative transfer simulations the impact of
uncertainties of the snow albedo on the retrieved cloud properties
is quantified. The spectral solar reflectivity above a liquid
water cloud layer was simulated using the DISORT 2 radiative
transfer solver embedded in the library for radiative transfer
libRadtran, . A solar zenith angle of
63∘ representative of Arctic conditions around spring was
chosen in the simulations. Typical Arctic boundary layer liquid
water clouds located between 200 and 500 m
altitude were assumed. Cloud optical thickness τ was varied
from 1 to 20 and cloud droplet effective radius reff,C
between 2 and 25 µm. For all clouds,
simulations with different surface albedo covering effective snow
grain sizes reff,S between 10 and
800 µm were performed. The spectral snow albedo
ρλ was calculated with the parameterization by
using the refractive index of ice by
and a form factor A=5.8. This form factor is
adopted from and accounts for the non-sphericity
of snow grains; it represents a mixture of randomly oriented
hexagonal plates and columns with rough surfaces. This mixture
and therefore the form factor A may vary depending on the
local snow properties. However, an uncertainty of A can be
attributed to an uncertainty in the effective snow grain size as
both properties have the same spectral impact on the snow
reflection characteristics, such as spectral albedo. Snow
impurities by black carbon were neglected as the absorption by
black carbon is typically limited to wavelengths less than
1000 nme.g. that are
not used in cloud retrieval over snow surfaces. A set of
calculated spectral snow albedo ρλ is presented in
Fig. a, which illustrates the decrease of
ρλ with increasing values of reff,S for
wavelengths λ>1000nm where the imaginary part of
the refractive index of ice is high (Fig. c).
The simulated upward nadir radiance Iλ and downward
irradiance Fλ were converted into spectral cloud
reflectivity γλ defined by
γλ=π⋅IλFλ.
In Fig. b a set of γλ for typical
values of τ between 2 and 16 is shown for a fixed cloud
effective droplet size of reff,C=10µm and a
typical effective snow grain size of reff,S=100µm. The simulations illustrate that τ
impacts γλ primarily at wavelengths larger than
1000nm where the snow albedo is lower than 0.8, while
lower wavelengths are less sensitive to τ.
Based on the simulated cloud reflectivities, which are used as
synthetic measurements, the commonly used bispectral cloud
retrieval algorithm was applied to obtain cloud optical thickness
and droplet effective radius. This retrieval method is similar to
the cloud product of MODIS . The retrieval
uses the different dependencies of γ1600nm
(less-absorbing wavelength) and γ2100nm
(high-absorbing wavelength) on cloud optical thickness and cloud
droplet effective radius and basically follows the method by
.
Bispectral retrieval grids of cloud top nadir
reflectivity γ1600nm and γ2100nm
assuming three different effective snow grain sizes reff,S of 50, 100, and 500 µm. The simulated
reflectivities cover cloud optical thickness τ between 3 and
20 and cloud droplet effective radius reff,C between
6 and 25 µm.
The bispectral retrieval grid obtained from the simulated cloud
reflectivities is presented in Fig. for three
effective snow grain sizes: 50, 100, and 500 µm. The
grids significantly differ and show a considerable snow grain size
effect, especially for low values of γ1600nm, while
at higher reflectivities the grids tend to converge. The
reflectivity γ2100nm is less affected by changes
of reff,S as the snow albedo is close to zero for reff,S>100µm (see Fig. ). As the
retrieval of τ is strongly linked to γ1600nm,
the effect of the snow albedo on the retrieved τ is obvious.
However, the non-rectangular shape of the grids indicates that
both reflectivities are coupled to both cloud parameters and,
thus, also the retrieved reff,C will be affected by
changes in the effective snow grain size.
Snow grain size effect on cloud retrieval results
For liquid water cloud retrievals obtained over snow surfaces with
unknown grain size, the snow grain size effect on uncertainties of
retrieval results was quantified. Therefore, synthetic
measurements obtained from the retrieval forward simulations as
introduced in Sect. are applied. For each
synthetic measurement defined by τ, reff,C, and
reff,S, a set of retrievals assuming different values of
effective snow grain sizes were performed. The purpose of this
exercise is to use the synthetic measurement (for which the
original reff,S is known), and start the retrieval
assuming reff,S is not known. The impact of these wrong
assumptions about the retrieved cloud properties is then quantified.
In Fig. a and b the retrieval results
are compared to the original cloud properties for synthetic
measurements calculated with an original effective snow grain size
of 50 µm, but retrieved from forward simulations
assuming a effective snow grain size of 200 µm
(crosses). The asterisks symbols in Fig. a and
b indicate the opposite case: originally reff,S=200µm is used to produce the synthetic
measurement and then reff,S=50µm is assumed
in the retrieval of the cloud properties. While
Fig. a shows retrieved τ for different reff,C indicated by the colour code, Fig. b presents
retrieved reff,C for clouds of different τ also
indicated by a colour code.
Assuming reff,S to be larger than originally present, the
retrieved τ is systematically overestimated because the
surface albedo is underestimated in this case (larger reff,S assumes lower snow albedo at 1600 nm; see also
Fig. ). If reff,S is underestimated
(surface albedo overestimated) the snow grain size effect is
inverted, leading to an underestimation of τ. This is in
agreement with the general surface albedo sensitivity of cloud
retrieval as discussed by and
. For the case presented in
Fig. , the snow grain size effect, expressed by the
percentage deviation of the retrieved from the original true
value, ranges up to 83 % for low optical thickness τ=3.
Comparison of synthetically retrieved τ(a, c) and
reff,C(b, d) with the original parameter value.
Calculations in (a) and (b) are performed, assuming a
larger effective snow grain size of reff,S=200µm
instead of the original reff,S=50µm (crosses) and a
smaller effective snow grain size of reff,S=50µm
instead of the original reff,S=200µm (asterisks). In
(c) and (d) all combinations of assumed and original
reff,S are analysed for a specific cloud of τ=4 and
reff,C=10µm. The red dots in (c) and
(d) indicate the cases included in (a) and (b),
where results for the same cloud are indicated by green circles.
The results for τ do not significantly depend on reff,C. By contrast, the uncertainties introduced in the
retrieval of reff,C strongly depend on τ as
illustrated in Fig. b. Especially for clouds of low
optical thickness, the retrieved reff,C is significantly
overestimated/underestimated when the effective snow grain size is
assumed to be higher/lower than originally present. The snow grain
size effect ranges up to 62 % for optically thin clouds
(τ=3) with small reff,C=5µm. If
larger effective snow grain sizes are assumed, the absorption
observed in γ1600nm is overestimated while
γ2100nm is almost unchanged. This combination
leads to an estimate of too low reff,C in the retrieval.
While the retrieval of τ is always biased due to
uncertainties of reff,S, independently of reff,C, no snow grain size effect is observed for the retrieval
of reff,C in the case of τ larger than about 10. For
optically thick clouds, the high extinction of incoming radiation
inside the cloud layer leads to a low amount of radiation that
reaches the surface and interacts with the snow and is transmitted
back to the cloud top. In this case the interaction of radiation
with the surface can be neglected and therefore the surface
albedo, or the assumption of reff,S, is not
relevant.
The cases discussed in Fig. a and b
represent the typical range of reff,S from 50 to 200 µm as expected in Arctic areas for snow
surfaces. However, white sea ice and snow cover in midlatitudes
may exhibit a higher variability leading to larger uncertainties.
Therefore, Fig. c and d summarize the
snow grain size effect on the retrieval of τ
(Fig. c) and reff,C (Fig. d)
for a set of combinations of assumed and original reff,S.
A typical cloud with low optical thickness of τ=4 and reff,C=10µm (green circles in Fig. a
and b) was analysed. The red dots indicate the cases
that are included in Fig. a and b.
The over- and underestimation of reff,S leads to almost symmetric
effects for the clouds investigated here. The maximum snow grain size effect
on the retrieval of τ covered by the simulations leads to a retrieval of
τ=2 or τ=6, significantly deviating from the original value of
τ=4. Compared to the original reff,C=10µm the
results for reff,C range between 6 and 13 µm. The
effects are most pronounced when either smaller effective snow grain sizes
are assumed or originally present; e.g. 50 µm is assumed but
300 µm is present, or 300 µm is assumed and 50 µm
is present. Similar mismatches between assumed and original reff,S at
larger grain sizes, e.g. 300 µm and 500 µm, cause
lower errors in the retrieved τ and reff,C. This indicates
that, especially in polar areas where snow grain sizes are typically smaller,
the retrieval biases due to a wrong assumption of reff,S cannot
be neglected.
The numbers presented here were obtained for a solar zenith angle
of θ0=63∘. For simulations with different solar
zenith angles in the range between 45 and 80∘
similar grain size effects are observed. In general, the magnitude
of the grain size effect of τ does not significantly change
with θ0. However, for low Sun (large θ0), the grain
size effect on the retrieved reff,C was slightly reduced,
while for higher Sun, small θ0, the effects increase. This
is caused by the increased probability that radiation interacts
with the surface in the case of a decreasing solar zenith angle.
Separating the spectral signatures of liquid water clouds and snow
In cases where liquid water clouds are located above a snow
surface, the spectral differences of absorption of solar radiation
by snow (ice water) and clouds (liquid water) as illustrated in
Fig. can be used to separate the surface and cloud
contributions to the reflected radiation above the cloud. In
addition, the different size ranges of cloud droplets (typically
reff,C<20µm) and snow grains (typically
reff,S>50µm) amplify these spectral
signatures of ice and liquid water. The photon path length inside
large snow grains is prolonged, which leads to stronger
absorption and a lower reflectivity compared to the smaller liquid
water droplets.
Mean standard deviations of spectral cloud reflectivity
στ, σreff,C, and σreff,S with respect to a single cloud or snow parameter τ,
reff,C, and reff,S calculated for the sets of
radiative transfer simulations (a). The first three spectral
weights Γ1, Γ2, and Γ3 of a principle
component analysis are given in (b).
Using the cloud reflectivity simulations introduced in
Sect. , two metrics are derived to identify
wavelengths that are most sensitive to only one single parameter,
either τ, reff,C or reff,S.
The first parameter σ is provided by the mean standard
deviation of γλ with respect to a single parameter
τ, reff,C, and reff,S. For example, for each
cloud, a standard deviation of all simulations with different
reff,S was calculated. σreff,S is then
derived by averaging these standard deviations for all different
clouds. The second parameter is the spectral weighting of a
principle component analysis (PCA) applied to the full set of
simulations. Corresponding to the cloud and snow parameters
changed in the simulations, the spectral weights Γ1,
Γ2, and Γ3 of the first three principle components
are found to be associated with τ (Γ1), reff,C (Γ2), and reff,S (Γ3).
Both metrics are shown in Fig. for τ
(στ and Γ1), for reff,C
(σreff,C and Γ2), and for reff,S
(σreff,S and Γ3). The results are
presented for a solar zenith angle of θ0=63∘ but are
applicable to larger and smaller θ0. The three calculated
functions of σ show that the maximum variability of the
cloud reflectivity with respect to the three parameters is located
in different spectral regions. While reff,S mostly
affects the cloud reflectivity at wavelengths between
930 and 1350nm, τ introduces high
standard deviations in the wavelength range of
1500–1800 nm. However, this spectral range is also
influenced by reff,C with σreff,C
showing values only moderately lower than στ. Values of
similar magnitude are observed for σreff,C at
wavelengths larger then 2000nm. In this spectral range
reff,C is the dominating parameter determining the cloud
reflectivity.
Similar spectral patterns result from the PCA, which delivers the
spectral weights Γ1, Γ2, and Γ3, are
presented in Fig. b. A comparison with the three
calculated σ in Fig. b reveals that
Γ1 can be associated with τ, Γ3 with reff,C and Γ2 with reff,S. The major
contribution to Γ1 is located in the wavelength range
930–1350 nm dominated by the changes in snow albedo,
reff,S. The impact of τ is spectrally neutral,
showing a similar magnitude of Γ1 in all analysed
wavelengths except for the water vapour absorption bands. By
contrast, wavelengths above 1500nm contribute most to
the weight of the third principle component Γ3, but with
opposite signs for the 1500–1800 nm and the
2000–2200 nm wavelength range.
It has to be mentioned that these sensitivities might change for
different scenarios assumed in the radiative transfer simulations,
e.g. different solar zenith angle, cloud altitude, profile of
cloud droplet size, or aerosol concentration. Similarly, the use
of subsamples of the full cloud and snow parameter range
investigated here might change the derived values. However, the
general separation of the three parameters by different spectral
ranges will not essentially differ.
Trispectral retrieval algorithmSelected wavelengths and radiance-ratios
Based on the spectral footprint of τ, reff,C and
reff,S in the cloud reflectivity, a trispectral
retrieval algorithm is proposed to derive the three cloud and snow
parameter simultaneously. In this way, the conventional
bispectral cloud retrieval was extended by a third measurement at
a wavelength sensitive to reff,S, which adds the
information on the snow grain size. Compared to retrieval
algorithms that rely on a fixed assumption of reff,S,
this new trispectral approach reduces the uncertainty of the
retrieved cloud parameters. In particular, measurements at
λ1=1040nm which are most sensitive to reff,S, λ2=1650nm most sensitive to τ, and
λ3=2100nm most sensitive to reff,C were
chosen in the retrieval algorithm.
In the new trispectral retrieval algorithm, the radiance-ratio
method introduced by , ,
and was applied. A normalization with the
cloud reflectivity at λ0=860nm was chosen. The
corresponding ratios R1, R2, and R3 are calculated as
R1=γλ1γλ0,R2=γλ2γλ1,R3=γλ3γλ2,withλ0=860nm,λ1=1040nm,λ2=1650nm,andλ3=2100nm.
The normalizations additionally reduce the uncertainties of the retrieval by
cancelling potential biases in the radiometric calibration of the
measurements. Except for λ1, all wavelengths that were chosen for
the algorithm are covered by the satellite imagers MODIS and VIIRS. To apply
the algorithm to global observations by these instruments, λ1 can be
exchanged by the 1240 nm wavelength band where cloud reflectivity is
still most sensitive to reff,S.
Three-dimensional retrieval grid using the ratios R1,
R2, and R3 obtained from simulations covering τ=1–20,
reff,C=5–25 µm and reff,S=25–800 µm. Panels (a)–(c) show each two selected
two-dimensional section of the retrieval grid, while (d)
covers the full 3-D grid. Representative measurements obtained
during VERDI (29 April 2012) are represented by black dots.
Similarly to Sect. , the mean standard
deviation σ with respect to τ, reff,C, and
reff,S was calculated for the three reflectivity ratios.
Table shows σ for all possible parameter
combinations. The higher σ, the more sensitive R1,
R2, and R3 are with respect to changes of an individual
cloud or snow parameter. One ratio with significantly higher
σ is found for each parameter, τ, reff,C, and
reff,S. For the cloud optical thickness the highest
σ=0.145 is found for R2, which is almost twice as high
as the sensitivity of R1 and R3. Similarly, R1 shows the
highest sensitivity to reff,S and R3 the highest
σ for reff,S and reff,C with
σ=0.074 and σ=0.121, respectively. This indicates
that R1, R2, and R3 are well suited to separate the
information of τ, reff,C, and reff,S from
spectral reflectivity measurements.
Standard deviation of the reflectivity ratios R1,
R2, and R3 with respect to one of the three cloud and snow
parameters τ, reff,C, and reff,S.
The set of cloud reflectivity simulations introduced in
Sect. served for the forward simulations (solar
zenith angle of 63∘, liquid water cloud layer between 200 and
500 m). The simulated grids of R1, R2, and R3 used for the
trispectral retrieval are presented in Fig. . While
Fig. d shows the full 3-dimensional (3-D) grid covering
τ=1–20, reff,C=5–25 µm and
reff,S=25–800 µm, Fig. a–c presents
two-dimensional projections of reflectivity ratios with one parameter being
fixed. In Fig. a, reff,S is set to 50 µm
(black) and 500 µm (red), while in Fig. b
reff,C is fixed at 5 µm (black) and 16 µm
(red) and in Fig. c τ is fixed to values of 4 (black) and 16
(red). The simulated grids show that the individual lines almost align
orthogonally for wide ranges of the simulated parameter space. The different
surfaces do not overlap. This indicates that the three chosen ratios R1,
R2, and R3 separate the influence of the three parameters τ,
reff,C, and reff,S on the cloud reflectivity and
allow a retrieval of the cloud and snow parameters leaving few ambiguities.
Only for reff,S lower than 50 µm is the grid more
narrow, as seen in Fig. d (uppermost black grid) and
Fig. a (black grid). Such a narrow grid increases the uncertainty
of the retrieved τ and reff,C, especially for clouds of low
optical thickness. This grid characteristic is caused by the higher surface
albedo of snow for small values of reff,S, which reduces the
contrast between cloud and snow surface for the wavelengths used to calculate
the three ratios. However, a retrieval of cloud and snow properties is still
possible in these ranges if the measurement uncertainties are sufficiently
small. A more general quantification of the retrieval sensitivities and
uncertainties can be derived by optimal estimation techniques, which is,
however, beyond of the scope this paper.
Ambiguities appear in the retrieval for small cloud droplet
effective radii of reff,C<5µm (not shown in
Fig. ). In that case the absorption of cloud droplets
is weak and the cloud reflectivity is similar to a cloud with
larger reff,C but smaller τ. Therefore, all
solutions with reff,C<5µm were excluded
from the retrieval.
Adjustments and uncertainty estimation
The retrieval algorithm was additionally adjusted to Arctic
conditions where open water and ice floes can occur in close
proximity. First, the algorithm determines whether the
measurements are obtained over snow-covered sea ice or open water
surfaces. Even in the case of cloud-covered scenes, the border
between sea ice and open water is clearly discernable in the cloud
top reflectivity as shown by . A surface with
a high albedo always enhances the upward radiance above a cloud
even in the case of optically thick clouds. Therefore, a fixed
threshold of cloud reflectivity γ(λ0)>0.65 at a
wavelength of 860 nm is applied to distinguish
measurements obtained above snow or white sea ice from those over
open water. This value may need to be adjusted for conditions of
different solar zenith angles or clouds with higher optical
thickness. The threshold assumes that the field of view of the
radiance measurement (pixel size) is fully covered by a single
surface type that is reasonable for airborne measurements.
However, for satellite observations with typically larger pixel
sizes, scenes dominated by broken sea ice might be misclassified.
If the cloud reflectivity at 860 nm is below the threshold, an open
water surface is assumed. In that case the bispectral retrieval following
the radiance ratio approach of is applied. Cloud
reflectivities γ(λ2) and the reflectivity ratio
R3=γ(λ3)/γ(λ2) are used to retrieve τ and
reff,C. If γ(λ0) is above the threshold, a snow
surface is assumed. Then the measurements are converted into the three
reflectivity ratios R1, R2, and R3 and interpolated to the 3-D grid
of simulated values introduced in Sect. .
However, 3-D radiative effects in the vicinity of sea ice edges significantly
influence the reflected radiation and bias the retrieved cloud properties
. Depending on cloud and surface geometry, this effect
is important up to several kilometres from the ice edge. Therefore,
measurements collected close to sea ice edges have been removed from the
analysis.
The retrieval uncertainties are estimated considering the
measurement errors of the reflectivity ratio and assuming a
Gaussian distribution of the errors. In this case the
uncertainties can be expressed by their double standard deviation
2σ. The retrieval is operated for varying each ratio,
R1, R2, and R3, separately by adding and subtracting
2σ. This procedure results in six solutions for the
trispectral retrieval over snow surfaces and four solutions for
the bispectral retrieval over open water. This set of solutions
is sufficient to represent the full solution space of the Gaussian
distributed measurement uncertainties. The median of these
solutions is used as the retrieval result of τ, reff,C, and reff,S, while the standard deviation of all
solutions quantifies the retrieval uncertainty Δτ,
Δreff,C, and Δreff,S.
Independent of these uncertainties caused by the measurements,
systematic errors due to the assumptions in the forward
simulations are considered. Currently, the retrieval algorithm is
limited to liquid water clouds and, therefore, may suffer if the
cloud contains ice. In the Arctic, mixed-phase clouds that are
dominated by a liquid water layer at cloud top are frequent
. In this case, the retrieval may provide
unrealistic cloud properties as the ice crystals absorb solar
radiation at similar wavelengths to the snow surface. The
absorption by the ice crystals may add to the absorption by the
snow surface and bias the results.
Furthermore, limitations of the snow albedo parameterization by
applied in the forward simulations may introduce
biases in the retrieved reff,S. The parameterization
assumes a fixed snow grain shape quantified by the form factor
A=5.8. Typical values of A range from 5.1 for fractals to 6.5
for spheres. For a retrieval of snow grain size in cloud-free
conditions, the snow grain shape implies uncertainties in the
retrieved effective snow grain size up to 25 %. However, as
discussed by , the reflection characteristics of
the snow, in particular the spectral albedo, are not affected at
wavelengths used by the retrieval algorithm. Both properties A
and reff,S change the snow albedo with similar spectral
patterns, which allows the uncertainty of A to be attributed as an
uncertainty of reff,S. Therefore, the retrieved cloud
properties are independent of the assumption of A.
This emphasizes that the retrieved reff,S always has to
be considered as an effective quantity. It represents the snow
grain size that has to be used in the specific albedo
parameterization (fixed form factor A) to provide the snow albedo
which is most representative for the measurements. Spatial or
temporal differences of the retrieved reff,S may result
either from a change of the geometric size of the snow grains or
from changes in the snow grain shape quantified by the
form factor.
Application to airborne measurements
Airborne spectral solar radiation measurements were collected with
the Spectral Modular Airborne Radiation measurement sysTem (SMART)
during the airborne research campaign VERDI. A total of 16 research flights was
conducted in April/May 2012 with the Polar 5 aircraft operated by
Alfred-Wegener Institute for Marine and Polar Research (AWI) over
the Canadian Beaufort Sea, which was partly covered with
snow-covered sea ice.
SMART measured the spectral solar radiance reflected in nadir
direction (2.1∘ field of view) and downward spectral
irradiance with grating spectrometers covering the wavelength
range between 350 and 2200 nm . From both quantities cloud reflectivity γ
was calculated using Eq. (). SMART was calibrated
radiometrically, spectrally, and geometrically in the laboratory.
The uncertainties of the measurements mostly originate from the
radiometric calibration given by the uncertainty of the applied
radiation source (traceable to the standards of the National
Institute of Standards and Technology, NIST) and the signal-to-noise ratio that differs with wavelength due to the sensitivity of
the spectrometers. By calculating the ratios R1, R2, and
R3 used in the retrieval algorithm, calibration
uncertainties are partly cancelled out. For example a bias in the radiometric
calibration will affect cloud reflectivities at different
wavelengths to the same degree, but does not influence the ratios.
Altogether, assuming typical measurements of clouds above snow, a
2σ uncertainty of 6 % was estimated for R1, while for
R2 and R3, 4 and 12 % uncertainties were considered.
For the retrieval of clouds over open water, where
γ(λ2) and the reflectivity ratio R3 are used, the
darker surface (lower signal and lower signal to noise ratio) leads
to uncertainties in the observations of 9 % for
γ(λ2) and 13 % for R3.
Two 30 min sections from observations on 29 April (Case I) and 17
May 2012 (Case II) were selected to test the retrieval algorithm.
In both cases a wide area close to the coastline was covered by
stratiform boundary layer clouds. While for Case I the cloud top,
defined by the boundary layer inversion, reached altitudes of up
to 700 m, persistent subsidence driven by anticyclonic
conditions lead to a low cloud top of 200 m in Case II.
During the remote sensing observations of these clouds, the
Polar 5 aircraft flew at an altitude of about 3000 m. The flight
tracks and the sections of the flight selected for a detailed
analysis are included in Figs. and .
For the application of the retrieval to the measurements, it has to be
considered that a pure snow surface is assumed in the forward
simulations. Although a snow thickness of 2 cm will be sufficient
to neglect the variation of snow albedo with snow thickness
, this constraint might not be valid for
observations over sea ice with leads or melt ponds. The research
flights of VERDI have been performed almost exclusively over the
partly sea-ice-covered Beaufort Sea. For the two cases,
observations have been selected in which the surface conditions are
close to the required pure snow surface. However, potential
effects by leads, melt ponds, or snow-free sea ice are discussed
for the individual cases.
Cloud microphysical in situ measurements on board of Polar 5 were
use to validate the retrieved reff,C. A Cloud Droplet
Probe (CDP) provided size-resolved cloud particle concentrations
in the size range from 2.5 to 46 µm and
corresponding reff,C. Using only
one aircraft, the in situ and remote sensing measurements had been
performed subsequently. For both investigated cases, I and II, the
remote sensing flight legs were flown first. Roughly 1 h
later the in situ measurements were obtained at the same location
following the flight track of the remote sensing sequence. Due to
the stable meteorological conditions, changes in the cloud
properties with time are expected to be small which allows for a
comparison of in situ and remote sensing data. A reference to
validate the retrieved snow grain size is not available because no
ground-based measurements on the sea ice have been conducted
during VERDI.
Time series of cloud optical thickness τ(a), cloud
droplet effective radius reff,C(b) and effective snow
grain size reff,S(c) retrieved from SMART measurements for Case I on 29
April 2012. Uncertainties of the retrieved properties are
indicated by dark shaded areas. When available, results of the
MODIS cloud product are given for τ and reff,C (5×5
pixel average).
Case I – 29 April 2012
The observations in this case were collected exclusively over
snow-covered sea ice and obtained between 16:54 and 17:21 UTC with a
solar zenith angle of 63∘. The retrieved cloud and snow
properties are presented in Fig. . Cloud optical
thickness ranged between τ=6 at the beginning and τ=15 at
the end of the flight leg. reff,C also shows a tendency
of increasing values between 6 and 9 µm,
while reff,S remained almost constant at values around
100 µm. In situ cloud microphysical measurements of
reff,C had been obtained along the same flight track,
about 1 h after the remote sensing measurements. At the cloud
top, two derived vertical profiles show reff,C between
6 and 7.5 µm, which is in the range of
the retrieval results.
Uncertainties Δτ(a) and Δreff,S(b) as a function of retrieved τ and
reff,S. The single measurements are colour coded with
reff,S for (a) and τ for
(b).
The retrieval uncertainties are indicated by the shaded areas in
Fig. . In the first part of the time series, up to
17:07 UTC, the retrieval uncertainties are lowest for τ with
about Δτ±1, and range at Δreff,C±1.5µm for reff,C, and Δreff,S±60µm for reff,S. In the
second part of the time series, the retrieval uncertainty
Δτ significantly increases. This correlates with the
increase of the cloud optical thickness. Similarly, Δreff,S shows a slight negative correlation with uncertainties
being higher for low τ. This is also reflected by the
retrieval grids presented in Fig. . The grid spacing
is narrower for larger τ.
Cloud optical thickness τ(a), cloud droplet
effective radius reff,C(b) and effective snow grain size
reff,S(c) retrieved by MODIS and SMART for Case I on 29
April 2012. The total flight track is indicated by a black line
and overlayed by the retrieval results of SMART. “I” and “T” mark
the locations of Inuvik and Tuktoyaktuk.
For all measurements, the behaviour of Δτ and Δreff,S as functions of the retrieved τ and reff,S is shown in Fig. . For Δτ, values
of reff,S are colour-coded in each data point, while for
Δreff,S colours indicate τ. Positive
correlations are found for both parameters: the larger the
retrieved τ or reff,S, the larger their
uncertainties. For Δτ uncertainties are larger for small
reff,S (colour code in Fig. a). In this
case, small reff,S increase the snow albedo and lower the
contrast between clouds and snow surface at
λ=1650nm, which then becomes less sensitive to
τ. Similarly, Δreff,S depends on the retrieved
τ (colour code in Fig. b), with higher
uncertainties observed for large τ. For high optical
thickness the clouds begin to mask the surface, and information of
the surface is lost in the reflected radiation measured above
cloud top leading to higher uncertainties of reff,S. For
Δreff,C, similar dependencies are obtained but with
less spread; Δreff,C ranges between
±1.2 and ±1.7µm (not shown
here). Δreff,C was found to increase slightly with
decreasing reff,S and increasing reff,C.
The snow-covered sea ice below the clouds did have some open or
only recently frozen leads, which were identified when Polar 5
flew below clouds after the remote sensing flight leg. From
automatic photographs taken on board Polar 5, the proportion of
leads was estimated to be lower than 5 %, which might explain
some of the higher values observed in the retrieved time series of
reff,S.
The retrieved cloud and snow properties were compared to satellite
observations by MODIS . Figure
shows maps of cloud optical thickness τ (a), droplet
effective radius reff,C (b) and effective snow grain size
reff,S (c) retrieved by MODIS. The flight track of
Polar 5 is indicated by a black line and overlayed by the
retrieval results of SMART. τ and reff,C retrieved
by MODIS along the flight track are additionally included in
Fig. . Cloud properties are obtained by the MODIS
cloud product Collection 6 for observations over snow or sea ice
using band 6 at 1640 nm and band 7 at 2130 nm. The effective snow grain size is provided
by the Snow Grain Size and Pollution amount (SGSP) retrieval
algorithm by . The SGSP is limited to cloud-free
pixels and therefore does not show values below the clouds
observed in the same image. For Case I, the Aqua overpass of
20:00 UTC was analysed. Although the MODIS scene was taken about
3 h after the airborne measurements, the stable cloud
conditions allow a comparison. Snow grain sizes typically change
over longer timescales, except when precipitation occurs. The weather
station in Tuktoyaktuk close to the coastline did report light
precipitation of snow grains but is not necessarily representative
for the clouds over the Beaufort Sea. However, a direct comparison
of reff,S is not possible due to the missing data
in cloudy pixels.
The MODIS cloud product in Fig. shows τ and
reff,C in the same range as that retrieved from the airborne
measurements. Note that here a longer time series is shown than
presented in Fig. . This includes areas with snow-covered land surfaces, for which the retrieval can be applied
assuming that the snow layer is sufficiently thick and the snow
albedo is not affected by the underlying surface
. At the southern edges of the cloud field,
lower values of τ are observed by both MODIS and SMART. For
reff,C the values retrieved by SMART show the same
tendency of lower reff,C at the southern cloud edge and
increasing reff,C towards the western end of the flight
track. For large areas of this cloud field the MODIS cloud product
did not provide valid solutions, which illustrates the limits of
the current cloud retrieval in Arctic regions.
For reff,S no direct comparison is possible. However,the
retrieval using measurements by SMART fills the gap of the cloudy
areas not considered in the SGSP retrieval for MODIS. The
retrieved reff,S are in the same range as those observed
by MODIS south and north of the cloud field and therefore are
considered to be consistent with the SGSP product. Lower reff,S were detected by MODIS at higher latitudes with some bias
due to large leads in the sea ice that are imprinted in the
retrieval results. At lower latitudes, the snow is strongly
influenced by accelerated metamorphism processes due to higher
temperatures and therefore exhibits larger reff,S.
Case II – 17 May 2012
For the second case, observations collected on a flight leg
crossing a sea ice edge between 16:45 and 17:12 UTC were analysed.
This transition allows for testing the consistency of the proposed
retrieval algorithm for observations over snow and open water.
Compared to Case I, the cloud altitude was lower with
200 m cloud top altitude indicating a thinner cloud layer.
The time of day and solar zenith angle were almost identical to
Case I.
The retrieved cloud and snow properties for Case II are presented
in Fig. . Additionally, the light grey shaded time
sections indicate measurements above the open ocean while during
non-shaded times snow-covered sea ice was present below the
clouds. As suggested by for the given cloud
base and top altitude (0–200 m), measurements within a
distance of 400 m to the sea ice edge were removed from
the analysis in order to minimize the impact of 3-D-radiative
effects at the sea ice edge.
Over sea ice, the retrieved τ is almost constant with values
around 5. Across the sea ice edge, τ decreases to about 2 and
later slightly increases up to τ=10 with increasing distance
to the ice edge. The systematic decrease of τ over the sea
ice edge as retrieved by the airborne measurements extends up to
8 km. As indicated by , the radiative
field across a straight sea ice edge is affected by the surface
albedo transition only up to distances of about 400 m.
Therefore, the coincidence of the decrease of τ with the sea
ice edge observed here is attributed to the retrieval algorithm
but rather has natural causes. It cannot be concluded from this
single cross section whether the change of the surface and
therefore the change of surface latent and sensible heat fluxes
affected the cloud properties across the sea ice edge. As the high-resolution
MODIS observations indicate, the cloud field showed an
oscillating pattern, which might have coincided with the sea ice
edge at the location of the airborne observations.
Same as Fig. but for 17 May 2012. Non-shaded
times indicate measurements above snow while for light-grey-shaded
times the ocean was ice free.
Same as Fig. but for 17 May
2012.
The retrieved reff,C vary slightly more strongly over open
water, while the retrieved values over snow-covered sea ice are
almost constant at about reff,C=8µm with only
short sections of higher cloud droplet sizes. Over open water,
larger cloud droplets are found with an average of about
10 µm. Close to the sea ice edge, until 17:00 UTC,
reff,C is found to slightly increase with increasing
distance to the sea ice edge simultaneously with the increase of
τ. The in situ microphysical measurements cover two cloud
profiles along the same flight track, one observed above open
ocean and one above sea ice. Both profiles showed no difference
with reff,C of about 9 µm at cloud top, which
is higher than Case I and in agreement with the retrieval
results.
The retrieved reff,S shows a slightly higher variability
and partly higher values ranging between 50 and
200 µm compared to Case I. The larger snow grains might
result from the different location, the advanced time, and snow
metamorphism, or the closer location to the open water. For example a
systematic decrease of reff,S with distance to the sea
ice edge is visible (16:52–16:54 UTC, about 6 km distance). In
Case II the observations take place above compact fast ice without
any leads. Photographs from a flight section in the same area below
the clouds showed that the fast ice was partly free of snow, which
may have caused the higher variability and the single peak of
reff,S=300µm.
The comparison of cloud and snow properties retrieved by SMART and
MODIS is shown in Fig. , similarly to Case I. MODIS
results along the flight track are additionally included in
Fig. . For Case II, the MODIS image was observed at
21:25 UTC, more than 4 h after the airborne observations.
Similarly to Case I, the temporal variation of the cloud properties
is expected to be low as stable dynamic conditions in a high-pressure system prevailed during the time of observations and
before. Figure shows that the low values of τ
observed by MODIS were also covered by SMART. The slight increase
of τ in the western end of the flight leg is represented by
the retrieval using SMART data. A similar pattern and agreement
was found for reff,C.
The direct comparison of the time series in Fig.
confirms the general agrement, although differences in the
location of cloud fluctuations are obvious. Above sea ice, MODIS
observed a steady increase of τ and reff,C and a
similar drop at the ice edge as retrieved by SMART. However, a
more quantitative comparison of SMART and MODIS cloud products is
not possible due to the time difference between the observations.
Differences in the retrieved cloud properties may either result
from the assumption of snow albedo or from temporal changes of the
cloud. Over open water, the MODIS cloud product provided lower
τ and reff,C. This is likely caused by the 4 h
difference between both observations. Due to the subsidence in the
anticyclonic conditions the cloud top continued to decline and
reduced the amount of condensed water, τ, and reff,C. The effective snow grain sizes retrieved by SMART are in
the range of reff,S retrieved by MODIS although the SGSP
algorithm could provide results only in small cloud-free areas.
The single measurements at the western end of the flight leg
indicate that individual ice floes encountered on the ocean and
show slightly lower reff,S. This is an indication of
fresh snow precipitation in this area where the cloud optical
thickness increased.
Conclusions
The retrieval of cloud properties using spectral reflected solar
radiation may be biased significantly if the clouds are located
over a snow surface or sea ice. In this case the snow/sea ice
properties have to be considered in the retrieval. An
inappropriate assumption of the effective snow grain size results
in an incorrect surface albedo at near-infrared wavelengths, which
imprints in the retrieved cloud optical thickness and droplet
effective radius. This snow grain size effect is similar to the
retrieval uncertainties reported by and
for observations over a variable land surface
albedo; only that variability of the snow surface albedo is
largest at wavelengths above roughly 1000 nm, while the land
surface albedo typically varies at wavelengths less than
1000 nm.
For a retrieval of cloud properties using similar wavelengths
bands, 1600 and 2100 nm, to those the MODIS cloud
product Collection 6 applies for observations over snow or sea
ice, the snow grain size effect has been quantified on the basis
of radiative transfer simulations. For a typical low-level liquid
water cloud (τ=4, reff,C=10µm,
θ0=63∘) the retrieved cloud properties would differ
by up to 50 % if reff,S is assumed to be 200 µm instead of the original effective snow grain size of
50 µm, or vice versa. The following is concluded:
The snow grain size effect is largest for small snow
grains because the snow albedo changes more strongly in the range
of small reff,S, while for larger reff,S a
saturation of the absorption of radiation is reached.
The snow grain size effect on retrieved τ is almost
independent of cloud optical thickness. At short wavelengths
used to retrieve τ (λ=1600nm), the
snow albedo is still high and always adds to the total
reflected radiation. Clouds can not mask this additional
reflection of the surface.
The snow grain size effect on retrieved reff,C is
strongest for clouds of low optical thickness. At
wavelengths used to retrieve reff,C
(λ=2100nm) the snow albedo is close to
zero. Therefore, in the case of optically thick clouds, the
radiation scattered by the clouds dominates and can mask the
additional weak reflection of the surface.
The snow grain size effect on retrieved τ does not
depend on the solar zenith angle, while the effect on
reff,C is larger for a higher Sun.
To overcome the snow grain size effect, a method is presented that
accounts for changes in the snow grain size in the retrieval
algorithm for liquid water clouds. A sensitivity study showed that
the spectral signatures of cloud and snow properties (τ,
reff,C, reff,S) significantly differ at specific
wavelengths. Three spectral ranges were identified to be most
sensitive to the three cloud and snow parameters. At wavelengths
between 930 and 1350 nm the spectral cloud reflectivity is
dominated by reff,S, at 1500–1800 nm by τ,
and at 2000–2300 nm by reff,C.
Based on these spectral sensitivities, a retrieval algorithm was
designed using reflectivity measurements at
λ1=1040nm mostly related to reff,S,
λ2=1650nm related to τ, and
λ3=2100nm related to reff,C. By
implementing normalizations in terms of the spectral reflectivity
ratios R1, R2, and R3, the impact of measurement
uncertainties was reduced.
The retrieval algorithm was tested in a feasibility study for
airborne observations by SMART during VERDI in 2012. Two flight
legs, one with closed snow-covered sea ice and a second flown
across a snow-covered sea ice edge were analysed. The results and
an uncertainty analysis suggest:
By considering reff,S, retrieved τ and
reff,C are consistent across a sea ice edge where
the surface albedo changes from snow-covered sea ice to open
water.
Retrieval uncertainties depend on τ. The thicker the
clouds are the stronger they will mask the surface. Less
radiation is transmitted into the sub-cloud layer and can
be reflected by the surface. This reduces the sensitivity
and increases the uncertainties for the retrieval of reff,S.
Retrieval uncertainties also depend on reff,S.
Small reff,S increase the snow albedo and reduce
the contrast between clouds and snow surface at
λ>1000nm increasing the uncertainties of
τ and reff,C.
Reasonable agreement between in situ cloud microphysical
measurement and the MODIS cloud and snow products was found.
Differences in the retrieved cloud properties may either
result from a wrong assumption of snow albedo or the time
difference.
For this first application of the new trispectral retrieval
algorithm, a rather simplistic analysis was applied. A more
general understanding of the retrieval sensitivities and
uncertainties can be achieved by optimal estimation techniques,
which is beyond of the scope of this paper. Although the retrieval
was applied to cases with a specific solar zenith angle only,
radiative transfer simulations showed that the spectral
sensitivities used in the retrieval algorithm are similar in the case
of smaller or larger solar zenith angles. Therefore, the proposed
retrieval method has some potential to be implemented for existing
spaceborne imagers such as MODIS or VIIRS. Due to the limited
number of spectral bands, for these two instruments λ2
would have to be exchanged by the 1240 nm wavelength band
where cloud reflectivity is still most sensitive to reff,S.
By retrieving cloud properties continuously along transitions from sea ice to
open water, the retrieval algorithm allows the impact of surface
changes on the cloud microphysical and optical properties to be analysed. However, retrieval
results close to such ice edges or in heterogeneous sea ice conditions are
influenced by 3-D radiative effects . For the two cases
presented here, the cloud-base altitude was low and therefore the 3-D
radiative effects were reduced. Only a limited part of the results had to be
excluded from the analysis, which is what might differ for clouds located at higher
altitudes .
The presented retrieval assumes that the surface albedo can be
described by a pure snow layer of sufficient depth with no
influence of the sub-snow surface. However, polar sea ice is not
always covered by pure snow. Over new sea ice the snow layer might
still be thin and the surface albedo reduced by the sub-snow
surface . In the
melting season, melt ponds change the surface albedo
. Locally, melt ponds almost totally absorb
solar radiation at wavelengths larger 800 nm depending on
the pond depth . However, on larger spatial scales,
the albedo of melt ponds and snow areas mix into an albedo with
spectral features similar to snow of large grains sizes
. For such cases, it has to be tested whether the
proposed retrieval algorithms still improve the estimated cloud
properties. However, the spectral signature of white sea ice and
melt-pond-covered sea is close to the spectral albedo of pure snow
for the wavelengths used in the retrieval. In that case, the
retrieved reff,S is interpreted as an effective snow
grain size representing an arbitrary surface albedo (white sea ice
or melt ponds) with the same spectral characteristics above
1000 nm wavelength as a snow surface with reff,S.
In this study liquid water clouds have been analysed. However, a significant
fraction of Arctic clouds are either mixed-phase or ice clouds
. In that case, the retrieval algorithm presented here may
provide unrealistic cloud properties. The ice crystals absorb solar radiation at similar
wavelengths to the snow surface.
Therefore, the information of cloud and surface contribution to the reflected
radiation might not be sufficiently separated by the wavelengths applied
here. Further sensitivity studies have to be performed to identify a
different set of wavelengths that are more appropriate for the remote sensing
of ice and mixed-phase clouds.
All data are available from the authors upon request. For
the airborne spectral solar radiation measurements during VERDI contact the
University of Leipzig (a.ehrlich@uni-leipzig.de). The MODIS SGSP product of
snow grain size can be made accessible by the University of Bremen, which hosts the
SGSP algorithm (lora@iup.physik.uni-bremen.de).
The authors declare that they have no conflict of
interest.
This article is part of the special issue “VERDI – Vertical
Distribution of Ice in Arctic Clouds (ACP/AMT inter-journal SI)”. It is not
associated with a conference.
Acknowledgements
We gratefully acknowledge the support by the SFB/TR 172 “ArctiC
Amplification: Climate Relevant Atmospheric and SurfaCe Processes,
and Feedback Mechanisms (AC)3” funded by the DFG (Deutsche
Forschungsgesellschaft). We thank the Institute for Atmospheric
Physic of the Johannes Gutenberg-Universität Mainz, in particular
Stephan Borrmann and Marcus Klingebiel, for providing the in situ
cloud microphysical measurements. We are grateful to the Alfred
Wegener Institute Helmholtz Centre for Polar and Marine Research,
Bremerhaven, Germany for supporting the VERDI campaign by
providing the aircraft and manpower. We would like to thank Kenn Borek
Air Ltd., Calgary, Canada for the great pilots who made the
complicated measurements possible. For excellent ground support
of offices and accommodation during the campaign we are
grateful to the Aurora Research Institute, Inuvik, Canada.
Edited by: Patrick Eriksson
Reviewed by: two anonymous referees
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