AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4615-2016Ground-based imaging remote sensing of ice clouds: uncertainties caused by sensor, method and atmosphereZinnerTobiastobias.zinner@lmu.deHausmannPetraEwaldFlorianhttps://orcid.org/0000-0002-5899-0890BugliaroLucahttps://orcid.org/0000-0003-4793-0101EmdeClaudiaMayerBernhardhttps://orcid.org/0000-0002-3358-0190Meteorologisches Institut, Ludwig-Maximilians-Universität, München, GermanyKarlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, GermanyDeutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, GermanyTobias Zinner (tobias.zinner@lmu.de)20September2016994615463211April201628April20168August2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/4615/2016/amt-9-4615-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4615/2016/amt-9-4615-2016.pdf
In this study a method is introduced for the retrieval of optical thickness
and effective particle size of ice clouds over a wide range of optical
thickness from ground-based transmitted radiance measurements. Low optical
thickness of cirrus clouds and their complex microphysics present a challenge
for cloud remote sensing. In transmittance, the relationship between optical
depth and radiance is ambiguous. To resolve this ambiguity the retrieval
utilizes the spectral slope of radiance between 485 and 560 nm in addition to
the commonly employed combination of a visible and a short-wave infrared
wavelength.
An extensive test of retrieval sensitivity was conducted using synthetic test spectra in which all parameters introducing uncertainty into the retrieval
were varied systematically: ice crystal habit and aerosol properties,
instrument noise, calibration uncertainty and the interpolation in the lookup
table required by the retrieval process. The most important source of errors
identified are uncertainties due to habit assumption: Averaged over all test spectra, systematic biases in the effective radius retrieval of several
micrometre can arise. The statistical uncertainties of any individual
retrieval can easily exceed 10 µm. Optical thickness biases are mostly
below 1, while statistical uncertainties are in the range of 1 to 2.5.
For demonstration and comparison to satellite data the retrieval is applied
to observations by the Munich hyperspectral imager specMACS (spectrometer of
the Munich Aerosol and Cloud Scanner) at the Schneefernerhaus observatory
(2650 m a.s.l) during the ACRIDICON-Zugspitze campaign in September and
October 2012. Results are compared to MODIS and SEVIRI satellite-based cirrus
retrievals (ACRIDICON – Aerosol, Cloud, Precipitation, and Radiation
Interactions and Dynamics of Convective Cloud Systems; MODIS – Moderate
Resolution Imaging Spectroradiometer; SEVIRI – Spinning Enhanced Visible and
Infrared Imager). Considering the identified uncertainties for our ground-based approach and for the satellite retrievals, the comparison shows good
agreement within the range of natural variability of the cloud situation in
the direct surrounding.
Introduction
Clouds play an important role in Earth's energy balance as they interact with
solar and terrestrial radiation. Cloud feedbacks on climate change and
aerosol–cloud interactions remain the largest uncertainties in climate
prediction . The particular importance of ice clouds in the
climate system has been long recognized
, but their radiative effect is still poorly
quantified .
Cirrus clouds are usually optically thin clouds in the upper troposphere
composed of ice crystals of various complex shapes. Satellite observations
reveal that cirrus clouds cover an average area of about 20 % of the
midlatitude regions and more than 50 % of the tropics
. The cloud net radiative forcing is determined by the
relative contribution of clouds' solar albedo effect and its infrared
greenhouse effect, both of which depend on the cloud scattering and absorption
characteristics . The net forcing of thin cirrus clouds at
high altitudes is assumed to be positive, implying a net gain of energy in
the atmosphere .
Cloud properties required to quantify the cloud radiative impact are
effective particle size, i.e. the area-weighted mean particle diameter and
optical thickness as measure of extinction in the cloud .
Several definitions of effective size can be found for nonspherical
particles (e.g. ). In this study we have chosen the
one following .
Cirrus clouds present a challenging task in cloud remote sensing due to their
complex microphysics, their high spatial and temporal variability and their
low optical thickness. Compared to optically thick clouds, detection of ice
clouds from space is difficult as only a small amount of solar radiation is
reflected and the influence on thermal radiation can also be weak. The
detectability of optically thin cirrus often relies on information about the
surface albedo and surface emission, especially for satellite-based methods
. Significant progress in spaceborne cirrus cloud
observation has been achieved using active remote sensors (radar and lidar)
on CloudSat and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observations) that provide detailed profiles of cloud optical properties, especially for cirrus .
The combination with thermal-infrared satellite data shows promising results for
the optical thickness range between 0.5 and 5 . Apart from
the specific active method's limitations (e.g. limited sensitivity of lidar
methods to optical thickness larger than 3, limited radar sensitivity to
small particles, limitations for larger and smaller optical thickness for
thermal infrared), these methods are limited in spatial coverage and temporal resolution.
The common retrieval technique to derive cloud effective radius and optical
thickness from passive space-borne and airborne multi-channel measurements uses
reflected solar radiation at two wavelengths . This
approach can be adapted for the retrieval of ice cloud properties as shown by
for the MODIS (Moderate
Resolution Imaging Spectroradiometer) airborne simulator. Since reflected and transmitted
radiance provide little or no information concerning particle shape, each ice
cloud retrieval is based on uncertain assumptions about particle shape
. These assumptions are known to introduce a major
uncertainty in ice cloud property retrievals (e.g. ).
Satellite observations have many advantages, such as their global coverage,
but they introduce additional uncertainties for cloud retrievals due to
limited spatial resolution (hundreds of metres to kilometres). More detailed
ground-based cloud remote sensing methods allow us to verify and improve
space-based techniques and facilitate long-term monitoring of typical
characteristics at certain locations. Several ground-based techniques exist
for the retrieval of cirrus properties using active (e.g. ) as well as passive remote sensing instruments
(e.g. ). An intercomparison of ice cloud retrieval
algorithms is given in .
In recent years hyperspectral instruments have become available to
atmospheric science and only a few approaches exist to exploit these novel
possibilities in cloud remote sensing today. An example for such a sensor is
the Spectral Modular Airborne Radiation measurement sysTem (SMART). This
non-imaging sensor was used by to retrieve properties of
cirrus clouds with an optical thickness range of 0.1–8 from an airborne
perspective, i.e. from reflected radiances. For ground-based measurements,
with the Solar Spectral Flux Radiometer (SSFR, non-imaging;
, ), a method to derive
optical thickness and effective radius of liquid water clouds with optical
thickness ranging from 5 to 100 is presented in . Their
lower optical thickness limit is caused by missing observation sensitivity
for thin clouds. In contrast to reflectance-based retrievals, there is no
unambiguous mapping between transmittance (transmitted radiance) and optical
thickness. Transmittance first increases with cloud optical thickness and
then decreases if optical thickness exceeds a critical value. Unfortunately,
situations with low optical thickness around 5 are most important for ice
clouds. and show that most ice clouds
have optical thickness in this range. Consequently, this ambiguity has to be
solved for observation of cirrus clouds. use an imaging
spectrometer in the visible (VIS) wavelength region for measurements of optical
thickness and solve the ambiguity problem by adding additional observer and
lidar information. Recently as well as
presented similar solutions for unambiguous retrieval of
optical thickness and effective radius for pointing system without providing
imagery. Both suggest the use of spectral slopes in the VIS to separate
between the two optical thickness regimes.
We will present a combination of both a solution for the transmittance
ambiguity using a similar spectral slope following ideas of and results for image measurements which provide context
information on the distribution of optical thickness and effective radius
over a large area.
To this end, a new ground-based remote sensing system at the Meteorological
Institute Munich is used: specMACS (spectrometer of the Munich Aerosol and
Cloud Scanner) is an imaging spectrometer that provides continuous spectral
radiance measurements in the wavelength range 400–2500 nm. During the
ACRIDICON-Zugspitze campaign (ACRIDICON – Aerosol, Cloud, Precipitation, and
Radiation Interactions and Dynamics of Convective Cloud Systems) in Germany in September/October 2012, a test bed
for the later ACRIDICON/CHUVA airborne campaign ,
measurements of ice clouds above the high-altitude environmental research
station UFS (German: Umweltforschungsstation) Schneefernerhaus at Mount
Zugspitze were collected. In addition
to the introduction of the new retrieval, the general sensitivity of
transmittance-based ice cloud retrievals is evaluated in detail with respect
to the unknown true cloud microphysical situation (particle habit),
uncertainties in necessary additional information (aerosol, albedo),
instrument accuracy (noise, calibration) and accuracy of the applied
retrieval technique itself (interpolation in lookup tables).
The performance of the new retrieval is tested systematically in several
sensitivity studies using a large set of synthetic observations. Selected ice
cloud observations with specMACS at UFS are analysed with the developed
cirrus cloud retrieval method and compared to results of simultaneous
satellite observations from Meteosat SEVIRI (Spinning Enhanced Visible and
Infrared Imager) and MODIS.
MethodsHyperspectral imaging spectrometer
The specMACS instrument is part of the Munich
Aerosol Cloud Scanner (MACS) instrumentation which is used to investigate
cloud–aerosol interactions in the atmosphere. Equipped with high spectral and
spatial resolution the instrument is designed to measure solar radiation that
is reflected from, or transmitted through, clouds. It consists of two
hyperspectral line cameras: the VNIR camera covers the VIS and
near-infrared (NIR) wavelength spectrum between 400 and 1000 nm, while the
short-wave infrared (SWIR)
camera measures solar radiation from 1000 nm onwards to 2500 nm. Both systems
were manufactured by Specim Ltd., Finland. At a given time the system
measures the spectral distribution of solar radiation for a single spatial
line. Over about 35∘ field-of-view 1310 spatial pixels are collected for
VNIR and 320 for SWIR. Spectral resolution is 2.5 to 4 nm in the visible
region and about 7.5 to 12 nm in the NIR region (see also Fig. ). Characterization of all details and
instrument calibration can be found in . For the
measurements at the UFS site specMACS was mounted pointing upwards, with the
line of sight oriented either perpendicular or parallel to the scattering
principal plane, i.e. the solar azimuth angle. The central pixel of the
spatial sensor dimension is directed toward zenith, corresponding to a
viewing zenith angle of θv=0∘. An image is obtained through
cloud motion.
Radiative transfer simulationRadiative transfer model
Radiative transfer was simulated using the radiative transfer code DISORT (discrete ordinate
technique)
provided within libRadtran
(library of radiative transfer; , ;
, ). DISORT is applicable for one-dimensional plane-parallel radiative
transfer simulations. Within this study, this is chosen as approximation
valid for horizontally homogeneous, thin cirrus clouds. We used the C-code
version of DISORT which is included in libRadtran. This
version includes an intensity correction method which is especially useful
for the simulation of highly peaked phase functions which are typical for ice
clouds. The method can handle phase functions stored on an arbitrary
scattering angle grid (for our simulations this grid contained 498 angles).
The number of streams for DISORT calculations was set to 16.
Absorption by atmospheric gas molecules is parameterized using the
representative wavelength parameterization REPTRAN
which is part of libRadtran. Spectra were calculated at high wavelength
resolution and convolved with the sensor's spectral sensitivity
.
Ground-based measurements of transmission through ice clouds are simulated in
terms of spectral transmittance T, defined as
Tλ=πLλE0,λcosθ0,
where Lλ is transmitted spectral radiance, θ0 solar zenith
angle and E0,λcosθ0 is the incident extraterrestrial
irradiance at top of atmosphere.
Optical properties of ice clouds
To perform realistic radiative transfer calculations in a cloudy atmosphere,
models of cloud bulk microphysical and optical properties are essential.
For the simulations we have used the HEY parameterization which is available
in libRadtran . This parameterization is based on single
scattering properties of ice crystals . To generate bulk
optical properties the parameterization assumes gamma size distributions with
parameters typical for ice clouds. The parameterization is available for six
individual habits and for a general habit mixture modelled after the one
defined by . To quantify the retrieval sensitivity to habit
mixture assumption (Sect. ), radiative transfer is
simulated for ice clouds consisting of particles of an individual of the six
different habits and of the “Baum-like” mixture. Phase function examples for
six habits and the resulting habit mixture are illustrated in Fig. .
Phase functions of individual habits (coloured lines), original
and “Baum-like” reproduced habit mixtures (black lines) for
reff= 50 µm and λ= 550 nm. Crystal habits
shown are droxtals (dro), plates (pla), bullet rosettes (ros), aggregates
(agg), solid (sol) and hollow (hol) hexagonal columns.
Interpolation of MODIS white-sky albedo (black points) with ASTER
spectral albedo of grass (green line) and limestone (red line). Spectral
surface albedo for the area around UFS (black line) resulting from the fit A=0.39⋅Agrass+0.05⋅Alimestone. In
light grey the spectral surface albedo for Munich is shown which is used in
the sensitivity tests.
Surface albedo
Due to multiple scattering between ground and cirrus clouds, a spectrally
resolved surface albedo is necessary to simulate hyperspectral transmittance.
The discrete channels of the MODIS surface albedo product
are fitted using a linear combination of albedo data
from the ASTER spectral library . For the measurement
site at UFS, the smallest residuals in this least-square fit are obtained by
combining the spectral albedo of grass and limestone as shown in Fig. . MODIS white-sky albedo acquired between 5 and 21
September 2012 is averaged over an area of 20 × 20 km2 surrounding
the site. This area corresponds to more than 50 % of the radiance reaching
cloud bottom from below at 9 km height, directly above the ground site after
Lambertian reflection at the ground.
Ice cloud transmittance simulations at specMACS spectral sensitivity
for θ0= 36∘, θv= 0∘,
ϕrel= 180∘ and “Baum-like” habit mixture:
variation of (a) optical thickness (reff= 40 µm) and (b)
effective radius (τ= 3.0). Thin grey vertical lines indicate 485 and
560 nm; thick grey lines indicate 550 and 1600 nm used in the retrieval
Example of a lookup table of transmittance at 550 and 1600 nm
simulated for θ0= 36∘, θv= 0∘,
ϕrel= 180∘ and habit mixture.
Retrieval of optical thickness and effective radiusIdea
According to the fundamental work of
, water cloud properties can be derived from
satellite-based measurement of cloud reflectance at two wavelength bands. We
apply this approach to ground-based measurements of ice cloud transmittance.
Remote sensing of radiative properties of ice clouds is complicated due to
their low optical thickness (mostly smaller than 5), nonspherical particle
shape and possible particle orientation. In the VIS spectral range,
transmittance spectra are dominated by scattering of clouds, aerosols and
gas molecules and by surface reflection. At longer wavelengths, in the
NIR, liquid water absorption increases strongly. Consequently,
transmittance is especially sensitive to cloud optical thickness in the
VIS spectrum (Fig. a) and to effective
particle size in the NIR spectral range (Fig. b). This correlation is exploited to retrieve
ice cloud properties from spectral measurements.
Analogous to the approach of , radiances at 550 and
1600 nm are stored in a lookup table as function of optical thickness and
effective particle radius (Fig. ) which can be compared
to measurements. In contrast to reflectance, a wide range of this diagram
shows an ambiguous relationship with optical thickness as transmittance first
increases and then decreases with increasing optical thickness. The presence
of an optically thin cloud increases transmittance by cloud particle
scattering compared to the dark diffuse clear sky (Fig. a, solid lines). Successive increase of ice
cloud optical thickness leads to increased scattering as well as to a growing
degree of cloud scattering and absorption. If optical thickness exceeds a
critical value, cloud reflection back to space and absorption become dominant
and transmittance decreases (Fig. a, dashed
lines).
A method has to be found to resolve the ambiguity in transmittance lookup
tables and to separate overlapping regimes of optical thickness in Fig. . An important factor in distinguishing cloud optical
thickness is sky colour, which is often used for cloud detection. As optically
thin clouds are partially transparent, the short-wave “blue” contribution
from atmospheric Rayleigh scattering is visible. With increasing optical
thickness the cloud scattering becomes dominant, leading to a “grey”
spectrally invariant appearance. A measure for this colour is the slope of
transmittance spectra Tλ in the VIS spectral range,
defined as
SVIS=100T550⋅dTλdλλ1λ2.
The derivative of transmittance is computed by linear regression in the range
from λ1= 485 nm to λ2= 560 nm normalized by
transmittance at 550 nm and scaled with 100 to obtain values in a range
comparable to transmittance. Small optical thickness is associated to
negative SVIS (high fraction of blue), while SVIS
becomes neutral or positive (low fraction of blue) for larger optical
thickness. The wavelength range to calculate SVIS is chosen
because (1) it is part of the VIS spectrum, which is sensitive to optical
thickness; (2) it exhibits a smooth spectral trend, which allows for
determination of a slope (cf. Fig. a); and
(3) it is not too close to the lower end of the spectral range of specMACS
where sensor sensitivity and calibration accuracy quickly deteriorate.
SVIS allows us to separate overlapping parts in Fig. , resulting in two widely unambiguous parts of the
lookup table.
In order to exploit the information of the VIS spectral slope
SVIS, the lookup table is expanded with the slope as a third
dimension. This three-dimensional (3-D) approach is illustrated in Fig. . It resolves the ambiguity in conventional
two-dimensional (2-D)
lookup tables for transmittance (Fig. ). Transmittance
at 550 and 1600 nm represent the two “classical” dimensions of the
lookup table. Additionally, spectral slope SVIS is used as a
third. In this 3-D diagram the simulated radiation values span a
nonintersecting surface: points associated to larger optical thickness
values are shifted to larger spectral slope values along the z axis.
Consequently, it is possible to unambiguously match a pair of optical
thickness and effective radius to a measured transmittance spectrum in the 3-D
lookup table. This approach is similar to the solutions introduced by
and .
Lookup table generation
The basis of the introduced retrieval method is a large set of simulated ice
cloud transmittance for all required specMACS bands which are arranged in
lookup tables. In this section we describe the set-up for these simulations. A
standard summer midlatitude atmosphere by with an ozone
column scaled to 300 DU is used. The extraterrestrial solar spectrum is taken
from . A horizontally homogeneous ice cloud layer is
located at a base height of 9 km with a geometrical thickness of 1 km. Ice
particles are assumed to be randomly orientated. The properties of this model
ice cloud are varied as follows.
45 effective radius values reff∈ [5–90 µm]: increments of Δreff= 1 µm between 5 and 30 µm, Δreff= 2.5 µm between 30 and 65 µm and Δreff= 5 µm between 65 and
90 µm;
60 optical thickness values τ∈ [0–20]: increments of Δτ=0.1 between 0 and 1.5, Δτ= 0.25 between 1.5 and 10 and Δτ=1 between 10 and
20;
6 habits (solid column, hollow column, six-branch rosette, plate, droxtal, roughened aggregate) and the described habit
mixture.
Three-dimensional lookup table using transmittances at 550, 1600 nm and the
spectral slope SVIS in the range 485–560 nm
(simulation for θ0= 36∘, θv= 0∘,
ϕrel= 180∘ and habit mixture).
Solar zenith angles θ0 are simulated for the range of 24 to
75∘ in increments of 1∘. Since the sensor was aligned with
respect to the sun before each measurement, a very limited set of viewing
zenith and azimuth angles has to be provided in the lookup table. Viewing
zenith angles θv from 0 to 10∘ in increments of
1∘ are computed according to the sensor's field of view during the
measurements. As radiative transfer is simulated for one-dimensional
horizontal homogeneous clouds, specification of relative azimuth angle
ϕrel=ϕv-ϕ0 is sufficient. Three relative azimuth angles
are taken into account, because the sensor's spatial line was either aligned
parallel or perpendicular to the principal plane: ϕrel=0∘ and
ϕrel=180∘ (line parallel) or ϕrel=90∘
(perpendicular).
Aerosol plays a minor role for the UFS observations evaluated here due to the
elevated height above the boundary layer. Nevertheless, the effect of a
moderate aerosol layer on the retrieved values was studied. Two settings are
considered: one without aerosol and one assuming OPAC continental-average
aerosol as provided in libRadtran . This aerosol mixture is
based on microphysical properties of various aerosol types included in the
OPAC database . The aerosol optical thickness was set to 0.2
at 550 nm, which is a typical value for the Meteorological Institute site in
Munich. See Fig. for results on specMACS
spectral resolution.
Cloud phase detection
As a first step in any ice cloud retrieval, the observed cloud thermodynamic
phase has to be determined. Ice crystals and liquid droplets exhibit
different absorption coefficients and consequently different cloud radiative
impact. Particle absorption is described by the imaginary part of the complex
refractive index which depends on wavelength and phase. For cloud-side remote
sensing, and proposed to use the ratio
of reflectance in narrow spectral regions at 2.1 and 2.25 µm to
separate between ice and liquid water. A similar approach is applied here for
measurements of cloud transmittance. Ice cloud transmittance rises strongly
in the wavelength range from 2.1 to 2.25 µm as absorption
decreases in this spectral region. In contrast, liquid water transmittance
changes only slightly in the same range, corresponding to a nearly constant
absorption coefficient. A NIR ratio INIR can be
defined as INIR=T2.1µm/T2.25µm, with transmittance T2.1µm at 2.1 µm and
T2.25µm at 2.25 µm. As the NIR ratio is generally
smaller for ice clouds than for water clouds, cloud phase may be determined
by means of a threshold in INIR. We assume that for
INIR<0.92 the observed cloud consists of ice particles and for
INIR≥0.92 the observed cloud might contain liquid water
droplets. Tests using simulated data show a good performance of this
NIR-ratio method (see Sect. ), but the adequate threshold in
INIR slightly depends on assumed ice particle habit and viewing
geometry.
Retrieval procedure
The algorithm to retrieve cloud optical thickness and effective radius from a
measurement is performed in five steps:
Detection of cloud phase using the NIR ratio.
Interpolation of an applicable lookup table from the nearest tabulated sun geometries' values.
Determination of distance of measurement to all lookup table elements.
Selection of subset of close elements in lookup table.
Calculation of weighted average over this subset.
After detection of ice phase an applicable lookup table is obtained for the
sun and sensor geometry (θv, θ0 and ϕrel)
given for the measurement situation. To this end, lookup table elements are
interpolated from the nearest simulated observation geometries' values. For
four nearest solar zenith and four nearest viewing zenith angles, the three
retrieval parameters (transmittance at 550, 1600 and slope 485–560 nm) are
interpolated with respect to scattering angle.
In the third retrieval step, the 3-D distance δTLUT,i between one measurement and all points i in the interpolated
lookup table is calculated as
δTLUT,i=[(T550-T550,i)2+(T1600-T1600,i)2+(SVIS-SVIS,i)2]1/2,
with transmittance T550 at 550 nm, transmittance T1600 at 1600 nm
and VIS spectral slope SVIS, each for the measured data as
well as for points i=1,…,N in the lookup table. The distance δTLUT,i is directly obtained from the given values of transmittance and
transmittance slope because the overall range of values for all three
measured parameters is comparable (compare Fig. ).
A maximum distance accepted is set to δTmax=0.1. That
means if no points in the lookup table are found within δTmax,
the retrieval fails. In cases for which more than three values are found,
the threshold is lowered stepwise to δTmax=0.05, 0.025, 0.0125. Especially for low optical thickness, where many possible
solutions lie within a small transmittance range, this improves retrieval
accuracy. The distance to the closest point in the lookup table δT is
stored as a measure of retrieval significance. Significance 1-δT/δTmax=0 is related to the maximum search radius, while
larger values are related to better matches and a perfect match would be
significance 1. The term significance is chosen because the distance to the
tabulated values, strictly speaking, is not a measure of retrieval accuracy
or quality but rather a technical quantity. Given the fact that we expect
matching measured and tabulated values, if we have considered the influence
factors correctly, δT is an important parameter which indicates the
applicability of the chosen lookup table to the real measurement situation
and consequently the reliability of the results. Still this parameter could
be small for the wrong reasons, e.g. if the impact of wrong albedo and wrong ice
particle habit compensate each other.
Finally, the ice cloud properties x, optical thickness and effective
radius are retrieved by taking a distance weighted average over the
remaining elements in the chosen lookup table subset:
xret=∑i=0Nwixi∑i=0NwiifδTLUT,i<δTmaxxiifδTLUT,i=0,
with
wi=1/δTLUT,i4.
The weighting factor w is found along the following line of thought.
Considering a 3-D space filled with uniformly distributed points, the number
of points contributing to an averaged result increases proportional to the
surface area dA=4πδT2 with increasing distance δT. To account for this factor of δT2, the weight for each point has
to be at least 1/δT2. For more emphasis on close points a smaller
weight should be chosen. We use w=1/δT4.
Sensitivity tests
A large set of simulated cirrus transmittance spectra is used in eight
retrieval test cases in order to determine typical uncertainties inherent to
the presented retrieval method (and to other similar ice cloud retrievals
based on transmittance or reflectance). The cloud properties retrieved from
these synthetic cases may directly be compared to the corresponding simulated
parameters similar to the approach in . Individual
influences on retrieval accuracy can be isolated this way: test case 1 is a
consistency check of whether simulated transmittance from the lookup table is
related to the correct tabulated retrieved properties by the retrieval
procedure. Using radiative transfer simulations not included in the lookup
table, test cases 2 and 3 examine retrieval sensitivity with respect to the
necessary interpolation of values of the effective radius and optical
thickness (case 2) and of the observation angle (case 3) in the solution space.
The assumption of a fixed crystal mixture is tested against several single
habit situations (case 4). Aerosol concentration is varied between “no
aerosol” (representative for the high-altitude measurements at UFS) and
“typical aerosol” (for the area around Munich, case 5). The influence of
inaccuracies in the assumed spectral surface albedo is tested (case 6).
The impact of instrument noise (case 7) and calibration accuracy (case 8) is also
evaluated applying typical values . The results are
summarized in Table .
Results of sensitivity studies: retrieval bias (Br,
Bτ), root mean square error (Rr, Rτ), 95 error
percentile (Pr, 95, Pτ, 95), false detection rate F,
habit of simulated test cases and details about chosen test cases. Optical
thickness of tested cases is 0.3–17 and effective radius is 8–70 (µm).
SensitivityBr, Rr, Pr,95 (µm)Bτ, Rτ, Pτ,95 [-]F (%)Input habitDetails1Consistency check0.0, 0.0, 0.00.0, 0.0, 0.00.0habit mixθ0, θv, reff, τ in LUT2Interpolation reff, τ0.4, 3.3, 5.3-0.0, 0.5, 0.35.7habit mixreff, τ not in LUT3Interpolation θ0, θv0.3, 3.8, 6.80.0, 0.5, 0.48.0habit mixθ0, θv, reff, τ not in LUT4aHabit assumption-2.4, 10.2, 19.70.9, 1.4, 3.172.0sol. columnθ0,θv, reff, τ not in LUT4bHabit assumption-1.5, 13.1, 29.90.4, 1.2, 2.558.1hol. columnθ0,θv, reff, τ not in LUT4cHabit assumption-4.0, 16.6, 35.11.4, 2.0, 4.683.7aggregateθ0,θv, reff, τ not in LUT4dHabit assumption2.3, 11.6, 26.80.1, 1.3, 2.351.4rosetteθ0,θv, reff, τ not in LUT4eHabit assumption2.2, 13.0, 28.3.6-0.7, 2.3, 5.168.1plateθ0,θv, reff, τ not in LUT4fHabit assumption-4.5, 10.5, 26.30.4, 1.3, 2.847.7droxtalθ0,θv, reff, τ not in LUT5Aerosol concentration0.6, 9.0, 18.70.5, 0.8, 1.136.0habit mixaerosol, θ0, θv, reff, τ not in LUT6Spectral albedo0.3, 4.1, 7.6-0.0, 0.5, 0.59.2habit mixalbedo, θ0, θv, reff, τ not in LUT7Instrument noise0.3, 3.8, 7.00.0, 0.5, 0.48.1habit mixθ0, θv, reff, τ not in LUT8a+5 % Calibration offset-0.0, 6.0, 12.0-0.1, 0.8, 1.435.6habit mixθ0, θv, reff, τ not in LUT8b-5 % Calibration offset0.0, 6.2, 12.60.3, 0.8, 1.737.1habit mixθ0, θv, reff, τ not in LUT
Three error measures are provided: bias Bx=〈xret-xtrue〉, root mean square error (RMSE),
Rx=〈(xret-xtrue)2〉 and the 95th percentile Px,95 of all
absolute errors. xret and xtrue are retrieved and
“true” quantity (x being either τ or reff);
〈…〉 is the average over all retrieval instances.
In addition, an error rate F is defined. It gives the percentage of
“incorrect” retrieval cases in the total number of performed retrieval
tests. A test case is perceived as incorrect if the deviation of retrieved
and true quantity is larger than the critical values |Δreff|>5µm and |Δτ|>1 for any one of the
two quantities.
More than 30 000 tests are run for each of the following cases. Random
combinations of spectra for 26 values of τ in the range of 0.3–17, 17
values of reff within 8–70 µm, seven different solar
zenith angles 25∘<θ0<58∘, two relative azimuth angles
ϕrel= (0∘, 180∘) and five viewing zenith angles 1∘<θ0<7∘ are used.
Phase detection
The detection of ice phase worked for the vast majority of all considered
test cases. Between 10 and 700 test cases out of 30 000 tests were
incorrectly classified as liquid water throughout each of the 15 tests listed
in Table , on average about 170 or 0.6 % of each case.
These misclassifications are mostly related to small optical thickness around
0.5. For the ice habit tests 4a to 4f as well as 8a and 8b, up to a few 100
of 30 000 test retrievals did not succeed because no lookup table values were
found near the measurement using δTmax<0.1.
Sensitivity to interpolation in lookup table
In a first case, the retrieval is applied to spectra which are part of the
lookup table for the assumed habit mixture. As anticipated, simulated cloud
properties are exactly reproduced and all error measures assume a value of
zero. For test 2 the retrieval is applied to a large set of additional
synthetic observations simulated for values of effective radius and cloud
optical thickness that are not part of the lookup tables. In order to limit
the number of necessary tests while not damaging the validity of results,
values are chosen in the following way: within the range covered by the
variable lookup table sampling grid Δx (x=τ and
x=reff, values see Sect. ), random
values are chosen at a distance of 0.25×Δx from tabulated
lookup table grid points. This is the average distance from grid points for
random measurements. Each of these ice cloud test cases is simulated for 20
geometries included in the lookup tables assuming the given habit mixture.
This test isolates the effect of interpolation of measurements within the
given lookup table grid: the RMSEs are comparable to the
resolution of the lookup table (in case of optical thickness) or even better
(for effective radius). It is apparent that the error frequency distributions
are not Gaussian, but narrow distributions with a few outliers. Thus, the
95th
percentile Px,95 for both quantities is similar to Rx. That means
Rx is strongly influenced by the outliers, while the vast majority of
deviations are smaller. Furthermore, 5.7 % of retrieval errors are larger than
the critical values |Δreff|>5µm or |Δτ|>1.
In test 3, solar and viewing zenith angles used for the test observations are
not part of the lookup tables either. Thus interpolation of tabulated values
has to be used on other illumination geometries as well. Now test cases for
the involved angles are chosen at locations with a distance of 0.25×Δx (with x being solar or viewing zenith angles). Azimuth angle is
not varied, as we fix the orientation using the sun and assume that this is
precise for quick measurements. Cloud parameters are varied as described for
test 2. Rr and Pr, 95 are only slightly larger than in test 2 for reff, and hardly any changes are detected for τ. Error
rate F increases to 8 %.
Sensitivity to ice crystal habit (test 4d of Table ): scatter plot for the effective radius
retrieval,
assuming the habit mixture.
Test spectra are simulated for bullet rosettes with τ, reff,
θ0 and θv not included in the lookup tables.
Dashed lines indicate Pr, 2.5 and Pr, 97.5 (according to 95 % of all absolute
errors);
dash-dotted lines indicate ±Rr; dotted lines are the means for the
test cases shown: θ0= 31.75∘,
θv= 2.25∘, ϕrel= 180∘.
Sensitivity to ice crystal habit
One major uncertainty in all ice cloud property retrievals is introduced by
assumption of a specific ice crystal habit mixture. In test 4, the retrieval
based on habit mixture is applied to synthetic observations simulated using
single habit characteristics. We apply the retrieval to the same test cases
as in test 3, using lookup tables for each of the six habits (see Table ). Compared to test 3, bias, uncertainty and error rate
do strongly increase due to the habit mismatch. The optical thickness
retrieval is characterized by biases in the range of ±1. As an example
for the effective radius retrieval for one of 70 tested geometries, results
for bullet rosettes are shown in Fig. with
coloured points indicating each of the 442 single tests and lines
representing mean, standard deviation and the interval with 95 % of all
individual errors. While small effective radius values tend to be
overestimated, large values are underestimated for this geometry on average.
Effective radius is generally overestimated for small optical thickness below
about 1. For medium optical thickness between 1 and 7, the effective radius
is mostly underestimated while retrievals for optical thickness larger than
that have only a small bias. This behaviour can not be generalized but
strongly varies with geometry and ice particle habit. The error rates
presented in Table ranging from 48 to 84 % are
probably an upper limit for the real errors, as clouds rarely consist of a
single habit but rather of a habit mixture probably mitigating the errors shown.
Sensitivity to aerosol and albedo situation
Besides habit assumption and interpolation techniques, aerosol optical
thickness influences retrieval performance. In test 5 a retrieval assuming no
aerosol is applied to the test cases of test 3, but a continental-average
aerosol with an optical thickness of 0.2 at 550 nm was used
to simulate the observations. The actual increase of 0.5 is larger than the
aerosol mismatch, possibly caused by the fact that an even larger cloud optical
thickness is needed to match larger sky brightness by aerosol due to greater
forward scattering by large ice particles compared to smaller aerosol size.
Not surprisingly, larger values of the RMSE and 95th percentile occur for small
values of τ, when the aerosol influence on transmittance becomes
predominant.
A basic assumption of the method regarding albedo is albedo influence, which is
correctly represented in the lookup table, as an actual spectral albedo
derived using a MODIS product over 16 days around the measurement period.
Uncertainties still arise from the product's uncertainty, our derivation
process of albedo data for the Zugspitze area and vegetation changes during
the period. As in the test case, a second albedo data set for our main measurement
site in the centre of Munich is used. It is shown in light grey
in Fig. . In the urban area, the vegetation peak
between 750 and 1400 nm is much more pronounced (Munich is a “green” city),
while the changes at shorter and longer wavelengths are smaller. Nonetheless,
the Munich albedo data set is also brighter in the regions used for the
retrieval around 550 and 1600 nm by 15 and 20 %. This increase is
comparable to the difference between summer and winter for vegetated surfaces
and is, at the same time, much larger than the estimate of the albedo product
uncertainty see. Errors and uncertainties caused by such
a variation of albedo are, nonetheless, small for reff (bias at
0.3 µm, RMSE at 4 µm) and τ (no bias, RMSE at 0.5).
Sensitivity to calibration accuracy
Another factor that influences the retrieval uncertainty is instrument noise
(cf. Sect. ). In test 7, random noise is added to the
synthetic observations of test 3 with a maximum magnitude of ±0.25 % of
the signal. This corresponds to a signal-to-noise ratio (SNR) of 400 : 1, which
is a typical value found during characterization of specMACS
. As expected for random noise, the retrieval bias
is identical to test 3, but the RMSE and false detection rate are slightly
increased.
More important than the effect of instrument noise is the calibration
accuracy that can be expected. A 5 % offset due to calibration is assumed for
tests 8a and b, which is a conservative estimate for specMACS for most of
the covered spectrum . Biases introduced are small
while uncertainties, especially for effective radius, are much larger. Overall
the effect is similar to the variation in aerosol content. No bias is found
for the effective radius retrieval. Uncertainty values RMSE at 6 µm
and Pr, 95 at 12 µm show that this has to be the effect of a
cancellation of opposing contributions.
Further uncertainty is introduced by solar zenith angle determination,
alignment of the sensor and changes of the sun position during the
measurement. However, we expect those to be much smaller than the
uncertainties discussed above. Besides, transmittance spectra are influenced
by water vapour concentration in the atmosphere. For the wavelengths used in
the retrieval, which are not located close to any strong water vapour
absorption bands, only small differences in transmittance are to be expected.
Water vapour column variations between 5 and 50 kg m-2 (a range as large
as the difference between polar and tropical regions) only cause differences
on the order of 0.1 % in transmittance.
Application and comparison to satellite data
In this section the presented retrieval method is applied to two cases
measured during the ACRIDICON-Zugspitze campaign at the research station UFS
Schneefernerhaus in October 2012. The ice cloud retrieval was applied to
three specMACS data sets collected on 2 October 2012 at 08:08–08:21,
08:57–09:10 and 09:53–10:04 UTC and two data sets collected on 3 October
2012 at 14:47–15:08 and 15:09–15:14 UTC. Retrieval results are compared
to satellite products from Meteosat SEVIRI and MODIS. On both days SEVIRI
Rapid Scan data over the Zugspitze area were evaluated using the DLR APICS
retrieval Algorithm for the Physical Investigation of Clouds with
SEVIRI;. On 2 October a TERRA overpass at 10:20 UTC almost
perfectly matched the time of the third specMACS measurement interval and
comparison to MODIS collection 6 data is possible. While
APICS is used with a habit mixture following , the MODIS
Collection 6 retrieval assumes severely roughened aggregated columns
.
Figure 7 shows the weather situation as seen by
SEVIRI. Stationary orographic low-level cloudiness, recognisable as yellow
area in the false-colour composite, is visible at the Alps (south of UFS
position, red cross). On 2 October it is concentrated at the northern edge of
the mountains close to the Zugspitze and east of it; on 3 October low-level
cloudiness is shifted southwards. Bands of bluish synoptic-scale cirrus
cloudiness not related to the lower clouds move through the area from west to
east on both days. On 2 October, the filaments of the large north–south
oriented band that just passed Zugspitze were observed by specMACS a few
minutes up to 2 h before the MODIS image collection time; on 3 October
measurements were collected with a more homogeneous cirrus deck above
Zugspitze.
3 October
The later, less complex case is discussed first. For two specMACS data sets
collected between 14:47 and 15:13 UTC on 3 October 2012 a comparison to
Meteosat SEVIRI data is possible.
Figure shows a part of the specMACS data collected
on 3 October 2012 14:47–14:57 UTC. The specMACS measurement was set up
pointing vertically with the sensor's spatial line of measurements
perpendicular to the solar azimuth angle of 243∘ at time of
measurement (sun at south-west after local noon). Data collected by specMACS
with angular resolution of about 0.05∘ (18∘ field of view, 320
spatial pixels) and time resolution of 4 frames per second are regridded to
approximately fill a 30 m × 30 m grid using the wind speed of
18.5 m s-1
(Fig. a) for a cloud bottom at 6500 m above UFS (cf.
Fig. ). For the case shown, the given wind direction is
almost aligned with the sun at south-west. That means advection was nearly
perpendicular to the sensor spatial line and an almost undistorted image of
the cirrus is observed by specMACS (Fig. ). Figure b and c show retrievals of optical thickness and
effective radius; Fig. d shows the retrieval
significance. Retrievals have been possible at retrieval significance values
close to 0.75 and above throughout the largest part of the scene. Following
our definition of this value, this means that for all pixel measurements of
spectral transmissivity tabulated values are consistently within
δT= 0.025 and 0.01 of the tabulated lookup value surface (compare Fig. ).
The real situation seems to be realistically represented by the lookup table.
Left panels: central Europe in a Meteosat false colour composite (R:
0.6 µm; G: 0.8 µm; B: inverted 10.8 µm) for 2
October 2012, 10:20 UTC (left top panel), and for 3 October 2012, 15:00 UTC (left bottom panel). Use of thermal-infrared information leads to bluish (cold, high clouds) and yellowish (warm, low
clouds) colours. The red cross marks the position of UFS at Zugspitze, the black box
the approximate location of MODIS data granule in Fig. .
Right panels: related cloud radar cross sections from the vertical pointing
Ka-band METEK MIRA 35 on UFS Schneefernerhaus (height 2650 m a.s.l.) around time
of specMACS measurements. White areas label the time periods with ground
measurements.
specMACS data at 550 nm collected above UFS on 3 October 2012,
14:47–14:57 UTC (a), retrieval of effective radius (b), optical thickness
(c) and quality (d) ranging from 0 (no values in LUT within search radius) to 1
(a perfect match in the LUT). The spatial dimensions of the measurement at
2160 × 10 800 m are derived from cloud height (see Fig. )
and advection wind speed.
Comparison of retrievals for 3 October 2012: red points show 4 s averages of specMACS retrievals for two sequences of data collection
14:47 and 15:13 UTC, and larger green dots show Meteosat SEVIRI retrievals at the
position of UFS. Error bars and green shading for SEVIRI data are related to
the 8-connected neighbours, and red shading for specMACS data is related to
the standard deviation in all retrievals collected in a 4 s period.
MODIS collection 6 effective radius (a), optical thickness (b) and
cloud mask (c) for 2 October 2012, 10:20 UTC. Red lines of symbols are
related to cross sections in time assuming advection (compare
Fig. ). The first symbol at the lower left end
of these lines marks the UFS position.
specMACS data at 550 nm collected above UFS on 2 October 2012,
08:56–09:10 UTC (a), retrieval of effective radius (b), optical thickness
(c) and retrieval significance (d) ranging from 0 (no values in LUT within search
radius) to 1 (a perfect match in the LUT). The spatial dimensions of the
measurement resulting 1200 × 5970 m are derived from cloud height (see
Fig. ) and advection wind speed.
Comparison of retrievals for 2 October 2012: black dots show MODIS
retrievals for ice clouds along the wind direction line as depicted in
Fig. ; red points show 4 s averages specMACS
retrievals for three sequences of data collection (08:08–8:21, 08:57–09:10
and 09:53–10:04 UTC); larger green dots show Meteosat SEVIRI retrievals at
the position of UFS. Error bars for MODIS retrievals are related to the
standard deviation of retrievals in a spatial tolerance region around the
wind direction (also compare to Fig. ). The broken
vertical line labels the actual time of the TERRA-MODIS overpass.
It can be seen in Fig. b and c that optical
thickness and effective radius are not totally independent. Though there
could be physical reasons for this observation, it is also an inevitable
effect of unknown ice crystal habit which leads to slightly varying
interdependence of the used spectral channels and retrieved parameters. This
is inherent to all passive ice remote sensing methods based on
“Nakajima–King style” retrievals.
Figure shows the comparison of data from
specMACS and SEVIRI for the UFS position over time. The parallax corrected
APICS SEVIRI satellite retrievals available every 5 min are marked with
green points. The error bars on SEVIRI data show the standard deviation of
values in the neighbourhood. This includes all ice retrievals in the
eight connected pixels around the UFS pixel (corresponding to 200 km2). From
each 2-D scene of specMACS data, the collected data averaged across the
spatial dimension and over 4 s segments are shown with red symbols. The
standard deviation of all available retrievals in such a 4 s segment
leads to the uncertainty estimate labelled with thin red lines. These values
correspond to an area of only 0.16 km2. A specMACS scene like
Fig. contains data from about 10 to 50 km2 area.
Obviously, specMACS observes more extreme values due to its high spatial and
temporal resolution while these extremes are averaged out over a SEVIRI pixel
of several kilometre size. Comparison of SEVIRI data points to specMACS data
at the very same moment shows very small differences. Averaged over all data,
optical thickness seems to be slightly lower (mean 1.9 compared to 2.6) and
effective radius slightly higher for SEVIRI (mean 23.2 compared to 19.2 µm).
2 October
On one hand, the second case allows an additional comparison to MODIS data;
on the other hand, it is complicated by low-level cloudiness which affects the
satellite products. Figure shows MODIS collection 6
products for 2 October at the time of the SEVIRI measurement (10:20 UTC) as
presented in Fig. 7a: effective radius, optical
thickness and cloud phase. At the southern edge of the displayed domain the
low-level orographic mostly liquid water clouds can be found. Overlaid is the
cirrus band moving through the area. Differences between the two cloud types
are obvious in optical thickness and effective radius. Water clouds are
optically very thick (τ>30), while optical thickness of ice clouds
ranges from 1 to 10. Effective radius retrievals for water droplets show
small values (10–20 µm); ice particle retrievals show values between
25 and 40 µm and even more than 50 µm around cloud edges.
Interestingly, the above mentioned interdependence of τ and rreff is also visible in these data.
The cirrus band is advected from west to east over the low water clouds with
almost no change in shape and appearance (compare Meteosat and cloud radar
data in Fig. ). Thus we
analysed MODIS data over the full 2 h time period assuming a constant
advection of the cloud band with wind speed. This approach is illustrated by
the red lines of symbols in Fig. . The first symbol in
the lower left corner marks the position of the specMACS sensor at UFS at the
time the cloud band has just passed UFS moving east. Each symbol to
west-north-west of this position is related to a time step of 133 s or
about 1 km distance backward in time along wind direction. This is based on
wind direction and wind speed of 8 m s-1 taken from nearby radiosonde data at
cloud height of the cirrus band from cloud radar data at the site: 4 km above
UFS between 08:00 and 10:00 UTC (Fig. ). To the left and right
of the wind direction little cross symbols mark an uncertainty region as
large as ±1 km (MODIS pixel size) at measurement time and growing
towards ±8 km for a time offset of more than 2 h (related to a wind
direction uncertainty of ±7∘). Water cloud retrievals dominate a
large part of the cross section, most likely a consequence of low water
clouds forming due to topography below an optically much thinner ice cloud.
Figure shows specMACS data collected at 2 October
2012 08:56–09:10 UTC. In this case, the specMACS measurement was again set up
pointing vertically with the sensor's spatial line fixed perpendicular to the
principal plane (sun in the south-east before local noon, ϕ=142∘).
As before, specMACS data are regridded to onto a 30 m × 30 m grid, this
time with wind speed of 8 m s-1. The given wind direction south-west means that
advection was nearly parallel to the sensor spatial line. Thus the 2-D display
of the observed cirrus in Fig. appears distorted.
If this parameter is smaller than zero (grey areas), no tabulated values for
transmittance and transmittance slopes were found in the lookup table within
the maximum search radius and no retrieval is performed. Retrievals have been
possible almost anywhere in the scene, mostly at significance values above
0.5. Grey areas label pixels for which no tabulated values of transmittance
were found within the maximum search radius and no retrieval is performed.
All three data sets are compared in Fig. .
The MODIS data (black) are taken from the cross section through the 10:20 UTC
data set actually observed by MODIS. At the time of the MODIS overflight
(10:20 UTC) no ice cloud was found at the position of UFS either in MODIS
or in Meteosat SEVIRI retrievals. In particular, APICS detected no ice
clouds after 10:05 UTC. The error bars on MODIS (black) show the standard
deviation of values for all ice retrievals in the surrounding area defined by
wind direction uncertainty (Fig. a–c).
For optical thickness (Fig. a) both satellite-based retrievals agree roughly as far as the overall value range is
concerned. Both show a decline in optical thickness from large values around
10–13 towards small values 2–4, although they do not agree in timing of this
decline. specMACS optical thickness agrees well with satellite retrievals
around 10:00 UTC. Not surprisingly, the high-resolution transmittance retrieval
picks up the very small values at the rear end of the cirrus band moving
eastward (after 10:00) better than low-resolution reflectance retrievals from
satellite. MODIS misses them completely (likely due to thick water clouds
below), while SEVIRI shows larger values around 2. Given the resolution
differences, specMACS retrievals are mostly within the expected uncertainty
of the satellite data for 08:56–09:10 UTC (both MODIS and SEVIRI) and for
08:08–08:21 UTC (only MODIS).
For such a cirrus cloud band moving unrelated to lower-level cloudiness,
optical thickness values around 10 and above – as retrieved by both satellite
retrievals – are a sign of the underlying cloud layer's influence.
analyse these cases of thin cirrus overlaid over thicker low-level clouds in detail for MODIS data and they find similar differences as
seen for this scene. Water cloud and ice cloud optical thickness can not be
separated from satellite perspective, while the water clouds below our
observation height (UFS) obviously do not disturb the zenith observation much
(compare Fig. d).
Effective radius values are also in good agreement for the time period
09:53–10:04 UTC. The scatter of specMACS retrievals becomes high when optical
thickness becomes very low after 10:00 UTC. While the two satellite retrievals
agree remarkably well over the whole time period, specMACS derived particle
size is larger for the earliest measurement period 08:08–08:21 UTC. Still the
observed deviation seems to be within the range of values explainable by
uncertainty introduced through underlying water clouds with lower effective
radius (compare Fig. b).
An interesting aspect of this complex example is the demonstration of the
potential of a ground-based method to provide accurate cloud properties
compared to satellite methods, especially for thin cirrus. The same
quantities are retrieved by both methods, utilizing similar wavelength bands,
but the ground-based method benefits from its much higher spatial resolution,
which allows us to separate different parts (or layers) of the observed
cloudiness. In the ground-based data there might still be an impact of
increased albedo (low-level cumulus below the instrument). The low levels of
significance of our results at larger sensor zenith angles might be a sign of
it (see Fig. d). Nonetheless the ground-based
method is less affected by this problem and generally most likely much better
at retrieving thin ice cloud properties than the satellite methods.
Summary and discussion
We presented a new method for the retrieval of ice cloud optical thickness
and effective radius from spectral measurements of transmitted radiance.
Using data from a new spectral imager (specMACS;
, ), a phase
detection and retrieval of optical thickness and effective radius was set up.
Phase detection uses a NIR ratio of transmitted radiances
(T2.1µm/T2.25µm). It is combined with a
well known two wavelength approach following . An
important extension of the method is the addition of a third parameter to the
retrieval to resolve the ambiguity of transmittance retrievals with respect
to increasing optical thickness. If optical thickness increases,
diffusely transmitted downward radiance increases first. For increase above optical
thickness of 4 to 6 radiance values decrease again. Similar to
and , this problem is overcome by an
interpretation of the spectral slope observed at 485–560 nm. This slope
steadily decreases from positive values (blue sky colour) to neutral values
(grey sky colour) for increasing optical thickness in this region. This slope
is combined with radiances at 550 nm (mainly sensitive to scattering and
optical thickness) and 1.6 µm to create a retrieval using a lookup
table based on one-dimensional radiative transfer simulations.
A second important part of the presented work is the rigorous test of
sensitivities of the established retrieval to the method's internal accuracy
as well as its sensitivity to unknowns and uncertainties of real world
observations. In general, for this type of ice cloud remote sensing
instrument accuracy (absolute calibration and noise), interpolation in the
given lookup table's forward solutions, additional scattering by unknown
aerosol and unknown ice crystal habit mixture in the observed cloud have to
be considered as sources of bias and uncertainty. Some of these are
methodical issues. The issue of unknown habit is of course a core part of the
physics of our objects of study. It remains an unsolved issue in the remote
sensing community. The pre-calculated forward solutions of the retrieval have
to assume a certain ice particle habit or mixture situation which most likely
is not the one present for a given observation.
Using more than 300 000 synthetic measurements over the expected range of
optical thickness, effective radius and observation geometries, it was
possible to isolate effects of these uncertainties systematically. They show
that uncertainties due to lookup table interpolation stay minimal: systematic
deviations of optical thickness from true values are negligible and below 0.5 µm for effective radius. Still the variation of a specific retrieval
can be in the range of 0.5 for τ and a few micrometre for effective
radius. Instrument noise and uncertainty in spectral albedo create a
comparably small effect. Also, unknown aerosol for moderate aerosol optical
conditions cause systematic biases around 0.5 in τ and 0.5 µm in
reff. Uncertainties of effective radius can grow to about 10 µm. The impact of limited calibration accuracy is likely to be in a
similar range. Errors can be positive or negative by more than 5 µm
for reff and around 1 for τ, strongly depending on the
optical thickness regime. The biggest problem for retrieval accuracy is
obviously unknown habit. Especially for effective radius it causes systematic
errors much larger than all other influencing factors. Biases are in the
order of 5 µm for reff and for τ in the order of 1.
Uncertainty of a specific retrieval can easily be wrong by more than 10 µm and 1 to 2 for τ.
Our application to two cirrus cases observed during the ACRIDICON-Zugspitze
campaign in fall 2012 agreement within the expected uncertainty ranges. Given
that (1) crystal habit assumptions are not the same for the three compared
methods, (2) reflectance-based methods suffer from additional uncertainty due
to strong surface and cloud influence from below the cirrus clouds and (3) resolution
of the measurements is very different, the deviation of average
optical thickness values and effective radius is mostly within the observed
variability in the surrounding area.
Obviously there are limitations to passive remote sensing of thin ice clouds.
show that thin cirrus ice clouds have a long-term and large-scale average optical thickness in the range of 1 to 2 with standard
deviation in the same range. In this respect the presented results seem to
showcase a rather typical situation. Given these average values, the possible
optical thickness errors of mean values from the synthetic tests up to values
of 1 and the observed differences of mean values up to 4 (though mostly much
less) demonstrate the full challenge of ice cloud remote sensing.
The uncertainty values found here for the ground-based perspective are in
good agreement with existing results on the satellite perspective.
anticipated differences of more than 50 % in optical
thickness retrievals for various habit assumptions based on simple radiative
transfer considerations in the literature. In a case study for an airborne
reflectance-based retrieval, found relative differences of
around 50 % in optical thickness and up to 20 % in effective radius depending
on habit assumptions. also assessed the sensitivity of
their ground-based cirrus optical thickness retrieval to variation of certain
parameters. The values can not be directly compared to our results, as they
only refer to a small number of specific situations regarding observation
geometry and cirrus situation and not a large range of combinations as in our
sensitivity test. For variation of crystal habit and for small optical
thickness up to 1 they showed large relative differences up to 80 % with
average absolute differences at 0.1. Though such cases are contained in the
sensitivity test shown here, average impact over many different situations is
smaller. also present large uncertainties for an albedo
variation. This is caused by their choice of a test albedo which is extremely
different from the measurement situation, while here it was assumed that the
general albedo situation can be characterized well and remaining uncertainty
has only small impact.
In order to improve the situation some of the mentioned uncertainties should
be reduced. Ground-based high-spatial-resolution remote sensing of ice clouds
in itself already reduces some uncertainties of satellite retrievals.
Ground-based methods are much less affected by surface albedo. Low-level
clouds are easily distinguished from cirrus and, in case of elevated
observations, can be excluded almost entirely. Due to
high-spatial-resolution, the
separation of clear and cloudy areas is more precise, while sub-pixel
inhomogeneity and clear sky “contamination” affect satellite data. The
higher resolution and reduced albedo sensitivity improve the sensitivity to
small optical thickness values. Admittedly, however, 3-D effects,
i.e. effects of horizontal radiation transport, are more likely to affect
retrievals at higher spatial resolution.
One important technical approach is the reduction of calibration
inaccuracies. For example, demonstrate retrieval
approaches based on normalized radiance instead of absolute measurements. For
their SSFR, operating at a similar
wavelength range as specMACS (though without imaging capability), they
normalize all spectral radiance values by the value of one spectral channel.
Dependence on calibration accuracy is much reduced this way because only the
more stable channel-to-channel accuracy, has to be considered instead of
absolute radiometric accuracy which is much harder to achieve. Of course the
most important step forward would consist in a reduction of the crystal type
uncertainty. The halo regions around 22 and 46∘ scattering angles
were avoided here for our spectral approach. Uncertainties can be expected to
be higher in these regions with strong angular gradients of transmittance
under single scattering conditions, if no additional information on crystal
habits is available. However, the imaging capabilities of the specMACS sensor
(especially if combined with a scanning platform; see
, ) do not only
allow us to successfully avoid these regions for the spectral evaluation but
would allow for the utilization of the spatial distribution of transmittance
in these regions to provide the missing information. Use of this spatial
distribution could provide important constraints regarding the present
average phase function as demonstrated. Especially the
presence of optical scattering phenomena like type and intensity of halo
displays could be used to identify specific particle shapes and orientation
and information on the mixture with less perfect rough ice particles. A
combination of the presented method with additional information of this kind
will be the next step in our effort to provide better ice cloud property
observations.
Data availability
MODIS cloud products are available via Platnick et al. (2015a). Meteosat
SEVIRI rapid scan level 1.5 radiance data can be retrieved from EUMETSAT data
centre . specMACS data used in this publication and
retrieval results can be found in the Supplement.
The Supplement related to this article is available online at doi:10.5194/amt-9-4615-2016-supplement.
Acknowledgements
We thank Hong Gang for providing the single ice crystal properties from
and three anonymous reviewers for their constructive efforts
in improving this manuscript. This work was partly funded by DFG (German
Research Foundation) projects MA 2548/6-1, MA 2548/9-1 and INST 86/1256-1.
Edited by: A. Kokhanovsky
Reviewed by: three anonymous referees
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