ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-9049-2017Vertical distribution of the particle phase in tropical deep convective clouds as derived from cloud-side reflected solar radiation measurementsJäkelEvelyne.jaekel@uni-leipzig.deWendischManfredhttps://orcid.org/0000-0002-4652-5561KrisnaTrismono C.https://orcid.org/0000-0002-1501-7468EwaldFlorianhttps://orcid.org/0000-0002-5899-0890KöllingTobiasJurkatTinaVoigtChristianehttps://orcid.org/0000-0001-8925-7731CecchiniMicael A.https://orcid.org/0000-0002-0219-2857MachadoLuiz A. T.https://orcid.org/0000-0002-8243-1706AfchineArminhttps://orcid.org/0000-0002-7669-8295CostaAnjahttps://orcid.org/0000-0003-3097-6269KrämerMartinahttps://orcid.org/0000-0002-2888-1722AndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925PöschlUlrichhttps://orcid.org/0000-0003-1412-3557RosenfeldDanielYuanTianlehttps://orcid.org/0000-0002-2187-3017Leipzig Institute for Meteorology (LIM), University of Leipzig, Leipzig, GermanyMeteorological Institute, Ludwig-Maximilians-University Munich, Munich, GermanyInstitut für Physik der Atmosphäre, Deutsches Zentrum für Luft und Raumfahrt (DLR), Oberpfaffenhofen, GermanyCenter of Weather Forecast and Climates Studies (CPTEC), National Institute for Space Research (INPE), Sao Jose Dos Campos, BrazilInstitute for Energy and Climate Research – Stratosphere (IEK-7), Forschungszentrum Jülich, Jülich, GermanyBiogeochemistry Department, Max Planck Institute for Chemistry (MPIC), Mainz, GermanyScripps institution of Oceanography, University of California San Diego, La Jolla, California, USAInstitute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, IsraelNASA Goddard Space Flight Center, Greenbelt, Maryland, USAEvelyn Jäkel (e.jaekel@uni-leipzig.de)27July201717149049906625January20171February20179June201723June2017This 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://acp.copernicus.org/articles/17/9049/2017/acp-17-9049-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/9049/2017/acp-17-9049-2017.pdf
Vertical profiles of cloud particle phase in tropical
deep convective clouds (DCCs) were investigated using airborne solar spectral
radiation data collected by the German High Altitude and Long Range Research
Aircraft (HALO) during the ACRIDICON-CHUVA campaign, which was conducted over
the Brazilian rainforest in September 2014. A phase discrimination retrieval
based on imaging spectroradiometer measurements of DCC side spectral
reflectivity was applied to clouds formed in different aerosol conditions.
From the retrieval results the height of the mixed-phase layer of the DCCs
was determined. The retrieved profiles were compared with in situ
measurements and satellite observations. It was found that the depth and
vertical position of the mixed-phase layer can vary up to 900 m for one
single cloud scene. This variability is attributed to the different stages of
cloud development in a scene. Clouds of mature or decaying stage are affected
by falling ice particles resulting in lower levels of fully glaciated cloud
layers compared to growing clouds. Comparing polluted and moderate aerosol
conditions revealed a shift of the lower boundary of the mixed-phase layer
from 5.6 ± 0.2 km (269 K; moderate) to 6.2 ± 0.3 km (267 K; polluted),
and of the upper boundary from 6.8 ± 0.2 km (263 K; moderate) to
7.4 ± 0.4 km (259 K; polluted), as would be expected from theory.
Introduction
Deep convective clouds (DCCs) play a crucial role in
redistributing latent heat, influencing the hydrological cycle, and
regulating the radiative energy budget of the Earth's climate system. In
particular, tropical convection is a key component of the global circulation
of the atmosphere, which is the primary pathway for energy transport from the
tropics to the mid-latitudes. DCCs exhibit a high variability of cloud
particle sizes and a complex vertical microphysical structure. This includes
the different phase states of water (liquid and ice) of the cloud particles
and the occurrence of layers where phase transitions between liquid water and
ice particles (further referred to as mixed phase) take place. The optical,
microphysical, and macrophysical properties of DCCs determine their radiative
effects and are controlled by particle growth occurring within the clouds.
Consequently, the understanding of the processes driving the evolution of
DCCs is of major importance. In particular, aerosol particles modify cloud
properties, including their radiative effects , their lifetime, and the formation of precipitation . Many
efforts have been undertaken to quantify these effects, which take place over
a wide range of spatial and temporal scales . Aerosol
particles have an influence on the cloud droplet size distributions (more
aerosol particles lead to more and smaller cloud droplets), on warm rain and
cold rain development, on the cloud-top height evolution, the depth of the
mixed-phase layer, and the occurrence of lightning . While
the formation of warm rain is suppressed by enhanced aerosol particle number
concentration, the cold-rain evolution is intensified due to extra latent
heat, which leads to an invigoration of the DCC development
. The phase transition from liquid water
to ice is especially relevant for the development of precipitation.
Furthermore, the optical properties of ice and liquid water clouds differ
and, thus, cause variable radiative effects. found
that in continental clouds glaciation occurs at much colder temperatures
(-15 to -30∘C) than in maritime clouds (warmer than
-10∘C). Consequently, the vertical transitional mixed-phase zone in
continental clouds is geometrically thicker than in maritime clouds. In
polluted clouds the coalescence zone vanishes (in which droplet growth by
collision and coalescence play a major role), and mostly small liquid water
droplets are observed. The mixed-phase zone is shifted to lower temperatures
(less than -15∘C), and glaciation occurs often above the -30 ∘C
isotherm, with the extreme situation of polluted clouds with strong updrafts
reaching -38 ∘C .
Profile measurements of
microphysical structure and formation of precipitation remain a challenge.
Either in situ measurements e.g., or remote sensing techniques are applied to obtain profiles of
cloud microphysical parameters, such as cloud particle size and phase state.
Active remote sensing observations (e.g., radar) provide profiles along the
line of sight. These sensors may penetrate through a cloud, but the
quantitative retrieval of cloud optical and microphysical properties is
problematic since the signal is dominated by scattering due to large
droplets. introduced a method to derive vertical
profiles of the effective droplet radius as a function of brightness
temperature from satellite reflectance measurements. They analyzed clusters
of convective clouds at different stages of vertical development to retrieve
the temporal evolution of individual cloud elements. This ensemble method
assumes that cloud-top properties derived from clouds at different stages of
their evolution are comparable to the properties of an individual cloud as it
evolves through the various heights . From the ensemble of
retrieved effective droplet sizes, a vertical profile of cloud phase can be
estimated because of the relationship between cloud phase and vertical
profile of the cloud particle size . However, the retrieval of the effective droplet size relies on
1-D radiative transfer simulations, which incorporate
retrieval uncertainties due to plane-parallel cloud assumptions and
neglecting the net horizontal radiative transport between the satellite
pixels . Consequently, a decrease in pixel size causes an
increase in the independent pixel bias, because the smaller the pixel, the
more important is the net horizontal photon transport, particularly for the
wavelengths in the visible spectral range, which are used for the retrieval
of the effective droplet radius.
The retrieval uncertainty due to the 1-D
approximation and the assumptions made with respect to the ensemble method
can be mitigated by using multi-angle spectroradiometer measurements
(ground-based, airborne, or satellite) of cloud-side spectral reflectivity. A
step further is the application of high-resolution imaging
spectroradiometers, which enables profiling of individual clouds with a
temporal resolution of 1 min from both ground or aircraft. For airborne
applications there are no safety-related flight restrictions due to strong
turbulence and icing as would be required in case of cloud penetrations for
in situ probing.
The retrieval approach of the thermodynamic water phase
based on cloud-side observations exploits the differences in the imaginary
part of the refractive index of the cloud particles of both phases in the
near infrared (NIR: 0.7–2.5 µm) wavelength range . While and
applied ground-based measurements of spectral
reflectivity between 1.5 and 1.7 µm wavelength for the phase
discrimination, and utilized
reflected radiation data at 2.10 and 2.25 µm wavelength. A phase
index was defined by using the spectral slope of cloud-side reflected radiances between 1.55 and 1.7 µm.
showed by applying 3-D radiative transfer simulations that
this slope is negative for liquid water and positive for ice particles,
mostly independent of the viewing geometry and cloud particles' sizes. For DCCs
with liquid water, ice particles, and mixed-phase layers, profile
measurements of the phase index provide evidence where and in which stage of
development ice particles start to form. For ground-based observations,
identified the mixed-phase zone by a strong increase in
the phase index from negative to positive values, while the vertical profile
of the phase index for pure liquid water or ice particles is less variable.
To determine the height and temperature of the mixed-phase layer from cloud-side spectral reflectivity observations, additional information is required.
used a thermal infrared sensor at 11 µm wavelength
yielding the brightness temperature, which is an indicator of cloud height.
Collocated scanning active remote sensing techniques by radar or lidar were
applied to estimate geometric information on cloud distance and height
. Another method is based on stereographic
analysis of multi-angle observations e.g.,. Differently
from the scanning-point-sensor measurements presented by
, this paper introduces airborne measurements of an
imaging spectroradiometer called specMACS spectrometer of the Munich
Aerosol Cloud Scanner;. These observations were used to
derive vertical profiles of the phase state of DCCs during the HALO (High
Altitude and Long Range Research Aircraft) campaign ACRIDICON (Aerosol,
Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective
Cloud Systems) – CHUVA (Cloud processes of tHe main precipitation systems in
Brazil: A contribUtion to cloud resolVing modeling and to the GPM; GlobAl
Precipitation Measurement) in 2014 . The measurement
technique of imaging spectroradiometers allows for instantaneous spectral cloud-side observations for a set of viewing angles depending on the number of
spatial pixels of the sensor. The imaging spectroradiometer measurements were
supplemented by video camera observations to estimate the cloud distance and
height from stereographic analysis. In this paper we will address the
following questions: (i) can we observe differences in the vertical
distribution of the thermodynamic phase state in DCCs for different aerosol
conditions by using cloud-side observations? (ii) How do the vertical
profiles of cloud phase derived from cloud-side observations agree with
results from satellite (ensemble method) and in situ measurements?
The
instrumentation and the field campaign are introduced in Sect. 1, followed
by a description of the methodology of the phase retrieval (Sect. 2). In
Sect. 3 the method is applied to data from three flights conducted during
ACRIDICON-CHUVA. The variability of vertical phase distribution is discussed
with respect to aerosol conditions and compared to in situ and satellite
products.
Measurements and toolsField campaign
Airborne remote sensing and in situ data sampled during ACRIDICON-CHUVA are
used to derive vertical profiles of the thermodynamic phase (ice or liquid
water) of cloud particles in DCCs as measured over the Brazilian rainforest.
Local convection is strongly forced by the diurnal cycle. In particular, at
the end of the dry season (September), a large variability of aerosol
particles due to biomass burning is observed . Three out
of fourteen scientific flights (labeled as AC10, AC13, AC18) are selected
for this study (flight tracks shown in Fig. )
covering an area of about 1400 × 1200 km. The temperature profiles
of the three flights show only small day-to-day variations in spite of the
different flight directions. In contrast, the relative humidity is variable
with flight area and altitude as was shown by . They
discussed in particular the relationship between cloud base and humidity below
clouds for several flights performed during the ACRIDICON-CHUVA campaign. For
AC13 they found less relative humidity (75 %) and a higher cloud base (2000 m) due to deforestation than compared to measurements over the rainforest
(80 % relative humidity and 1500 m cloud base). In the overview paper of the
ACRIDICON-CHUVA campaign by the aerosol conditions from
AC13 was classified as polluted. used the aerosol
concentration measured with a condensation particle counter (CPC) at cloud
base for flights AC13 and AC18 as an indicator. They found 4100 particles cm-3 for AC13 suggesting polluted clouds and about 740 particles cm-3 for AC18 indicating clouds under Amazonian background
conditions typical of the dry season. No appropriate measurements at cloud
base are available for AC10. Ground-based measurements on this day at the
Amazonian Tall Tower Observatory (ATTO) located at -2.143∘ S,
-59.001∘ W revealed a particle concentration between 1100 and 1600 cm-3. Since the flight AC10 was in the same general region, these data
are used to describe the aerosol condition of AC10. Furthermore, the aerosol
optical depth in the main measurement areas taken from MODIS
(Moderate-resolution Imaging Spectroradiometer) product MOD04/MYD04
(3 km pixel resolution) are chosen as additional parameter. Quite variable
values between 0.3 and 0.4 for AC18 (28 September 2014), between 0.4 and 0.5 for AC10
(12 September 2014), and between 0.5 and 0.6 for AC13 (19 September 2014) are
found. From these data AC10 and AC18 are classified as moderate aerosol
cases. A summary of the three flights used in this work is given in Table .
Flight tracks of AC10 (black), AC13 (red), and AC18 (green). The city of
Manaus is indicated by the black cross.
Summary of presented flights with cloud-side observations during the
ACRIDICON-CHUVA campaign. The ranges of flight altitude and time refer to the
studied cloud cases.
Flight numberAC10AC13AC18Aerosol conditionsmoderatepollutedmoderateAOD (MODIS)0.4–0.50.5–0.60.3–0.4Number of cloud cases91610Flight altitude range (km)7.4–10.45.2–9.31.4–14.0Time range (UTC)17:25–19:2017:55–19:0015:30–20:30InstrumentationspecMACS and GoPro
The imaging spectroradiometer specMACS consists of two
line cameras (manufactured by SPECIM, Finland), one for the visible and
near-infrared (VNIR) and the other for the shortwave infrared (SWIR) spectral
range. The field of view (FOV) along the spatial lines of both cameras
differs slightly (33 and 35∘) due to different optics. The
incoming solar radiation is distributed over 1312 and 320 spatial pixels. For
each spatial pixel, spectral information can be measured within 0.4–1.0 µm (800 spectral channels) and 1.0–2.5 µm (256 spectral channels),
with a bandwidth between 2.5 and 12.0 nm. SpecMACS was characterized in the
laboratory with respect to nonlinearity, dark current, and polarization
. Spatial calibrations were performed to derive the angular
resolution of both sensors, which is needed for final geometric matching of
both sensors. The spectral characteristics were deduced by using
monochromator output at selected wavelengths. The absolute radiometric
response was determined using an integrating sphere and the absolute RAdiance
STAndard RASTA; traceable to absolute
radiance standards of PTB (Physikalisch-Technische Bundesanstalt). The
wavelength-dependent uncertainties (2σ) of the absolute radiometric
response including sensor noise and dark current drift between 3 and 14 %
(in the outer region of the measured spectra) were given in
.
Schematics of cloud-side observations by the imaging spectrometer specMACS (SWIR camera) and the GoPro camera.
The individual fields of view (FOVs) and corresponding number of spatial pixels are illustrated.
During the ACRIDICON-CHUVA campaign, specMACS was mounted at a side view port
on HALO. The transmission of the optical window with purified quartz glass
panes (type: Herasil 102) was characterized in the laboratory. The line
cameras were orientated in vertical position as illustrated in Fig. . During the aircraft movement 3-D (two spatial and one
spectral dimension) snapshots of cloud scenes were taken.
For estimates of
the cloud distance a 2-D digital action camera (type: Hero
HD3+3660-023 Full-HD manufactured by GoPro, Inc., USA, hereafter
GoPro) was installed at the side window of HALO. Video in full HD at a
resolution of 1920 × 1080 pixels was recorded during the flight. The
original lens of the camera was replaced with distortion-free optics, covering
a horizontal FOVh of about 90∘ and a vertical
FOVv of about 59∘. A schematic of the setup is shown in
Fig. . The geometrical calibration of the camera was
performed using a square chessboard. Images from different perspectives of
the chessboard were taken and evaluated by an open source routine
(http://opencv.org) implemented in computer vision algorithms
. This allows for assigning elevation and azimuthal angle to
each point of the image.
NIXE-CAPS
In situ measurements of the asphericity of particles were performed with the
Novel Ice eXpEriment – Cloud, Aerosol and Precipitation Spectrometer
(NIXE-CAPS). The instrument is a combination of two probes, the NIXE-CAS
(Cloud and Aerosol Spectrometer) and the NIXE-CIP (Cloud Imaging Probe).
While the NIXE-CIP detects the size of particles between 15 and 900 µm by
recording 2-D shadow cast images, the NIXE-CAS measures the size and
asphericity of the particles in the range of 0.6 to 50 µm
. NIXE-CAS discriminates between
spherical and aspherical particles by measuring the change of the polarized
components of the scattered laser light in the backward direction, which is
sensitive to the particle shape. Spherical particles are not supposed to
alter the polarization state of the incident light as discussed by
, while non-spherical ice crystals change the polarization
depending on their size and orientation .
With respect to the phase state discrimination, aspherical particles can be
considered as ice particles. In contrast, spherical particles indicate mainly
liquid droplets. Note that while have shown that ice
particles can also be spherical, the large majority of spherical particles is
associated with the liquid phase. The ACRIDICON-CHUVA data set is classified
with respect to temperature, asphericity, and particle number concentration
as measured by NIXE-CAPS (see Table ).
Cloud flag description of the NIXE-CAPS asphericity product after .
Group I: total concentration of particles 3–50 µm is larger than 3 cm-3. Group II: total
concentration of particles 3–50 µm is smaller than 1 cm-3 and total concentration of particles
with size larger than 50 µm is larger than 0 cm-3.
CloudTemperatureDescriptionflagrange (∘C)1.0> 0no aspherical particles detected; liquid1.1> 0aspherical particles detected –could be ice or ash particles2.00 >T>-38no aspherical particles detected; liquid2.10 >T>-38aspherical particles detected, group I;mixed phase2.20 >T>-38aspherical particles detected, group II; ice3.0<-38below homogeneous freezing threshold:all ice, no asphericity criterion; iceCAS-DPOL and LWC hotwire
The CAS-DPOL (Cloud and Aerosol Spectrometer, with detector for polarization)
instrument measures aerosol and cloud particles in the size range between 0.5
and 50.0 µm by sensing individual particles
passing a focused laser beam. The resulting intensity distribution of forward
and backward scattered light is used to derive the size distribution of the
particles. Only particles with diameters between 3 and 50 µm and with a
total number density larger than 1 cm-3 are classified.
Additionally, CAS-DPOL is used to estimate the phase of the cloud particles
(liquid or ice). The aspherical fraction (AF) from the CAS-DPOL is determined
by measuring the perpendicularly polarized light in the backward direction
and the forward scattering light intensity. The ratio of the forward and the
backward scattered light determines the phase of the particle. Particles with
a polarization ratio larger than the 1σ range of the inferred
sphericity threshold are categorized as aspherical. The method gives a size-dependent aspherical fraction of the first 300 particles measured each
second. The bulk aspherical fraction is derived from the number of aspherical
particles to the number of total particles measured between 3 and 50 µm s-1. Calibration of the backward channel was performed during RICE03
(Rough ICE campaign) at the AIDA (Aerosol Interactions and Dynamics in the
Atmosphere) cloud chamber . Spherical
liquid particles reveal a low AF (< 0.1) while aspherical particles (ice or
aerosols) have a high AF (> 0.1, mean of 0.4). Aspherical ice particles may
have an AF < 1 since the orientation of the particles in the sampling
volume may appear circular.
The liquid water content (LWC) was measured with
a King-type LWC hotwire installed on the CAS-DPOL. The
hotwire sometimes returns a signal in ice or clouds of partly frozen
particles. This signal is on the order of 0.2 g m-3. Thus, a
conservative threshold of 0.3 g m-3 is used to reduce the false
alarm rate.
MODIS
MODIS cloud products (Collection 6) of the Terra (MOD06) and Aqua (MYD06)
satellites are used for a comparison of the phase state and glaciation
temperature. Since MODIS mainly measures cloud-top properties, the
time–space exchangeability of convective clouds as proposed by
is applied and referred to as ensemble method. The
cloud particle phase of the cloud tops is directly taken from the MOD06/MYD06
product “Cloud_Phase_Infrared” with a 1 km pixel resolution
. Compared to Collection 5, where the cloud phase product
was classified as ice, liquid water, mixed phase, and uncertain using
brightness temperatures measured at 8.5 and 11 µm ,
Collection 6 is modified by using additional cloud emissivity ratios (7.3/11,
8.5/11, and 11/12µm) as reported by and
. Empirically derived thresholds of these emissivity ratios
were defined to separate between liquid water and ice clouds. Note
that due to several ambiguities (see ) a separate
classification of mixed-phase cloud pixels is no longer provided in
Collection 6. The “mixed phase” and “uncertain” classes from Collection 5 are
now combined into a single class specified as “undetermined”. Hence, the
description of the cloud phase profile by applying the ensemble method on the
“Cloud_Phase_Infrared” product is limited to the liquid water distribution and the ice-phase distribution. Therefore, the cloud particle size product is used
additionally to estimate the glaciation temperature as proposed by
. The vertical distribution and evolution of cloud particle
size inside a DCC provides useful information on the phase state
. The mixed-phase layer is characterized by a strong
increase in cloud particle size with height , whereas for
fully glaciated cloud layers the largest ice particles can be found directly
at the height where the glaciation temperature is reached. At lower
temperatures, no supercooled droplets are left for particle growth, and only
small ice particles are able to move upward inside weakened updrafts.
Consequently, the height and temperature where the increase in particle size
turns into a decrease is considered as glaciation level and temperature. A
sufficiently large statistic is required for the ensemble method. The cloud
particle sizes from the MOD06/MYD06 product are averaged for a bin of cloud
brightness temperatures (channel 31; 11 µm). In contrast to the original
retrieval , the restrictions concerning cloud optical depth
(COD > 30) and cloud-top temperature (CTT < 260 K) were relaxed to COD > 10 and CTT < 280 K, to enlarge the statistics of the data.
Radiative transfer model
3-D radiative transfer modeling is performed with the forward-propagating Monte Carlo photon-transport model MCARATS
(Monte Carlo Atmospheric Radiative Transfer Simulator; ). The optical properties (single-scattering
albedo, extinction coefficient, and phase function) of atmospheric components are pre-defined for each grid cell of the
model domain as either horizontally inhomogeneous or homogeneous layers. For the model input, the atmospheric profiles
of temperature, atmospheric pressure, and gas densities are taken from
. From a radio sounding from Alta Floresta
(-9.866∘ S, -56.105∘ W) and measurements of temperature,
humidity, and pressure performed by HALO, the temperature and pressure
profiles are adjusted to represent the atmospheric conditions on 19 September
2014 (AC13) in the region of one of the measurement flights (representative
of the three flights considered in this study). The density of water vapor is
re-calculated using the relative humidity, temperature, and pressure
measurements. Since Rayleigh scattering is calculated from the density
profile according to , the LOWTRAN (Low Resolution
Transmission Model) parametrization by , as adapted
from SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer;
) is used for gas absorption. The optical properties of
clouds are derived from profiles of effective radius (reff) and
liquid (ice) water contents (LWC, IWC) using Mie calculations for water
clouds, while for ice clouds the parameterizations by are used. For the polluted case, aerosol properties are described
with the model by and scaled by AERONET (AErosol RObotic
NETwork) measurements (site Alta Floresta) of aerosol optical depth, single-scattering albedo, and asymmetry parameter (used for the Henyey–Greenstein
phase function).
Methodology
The retrieval method of the phase state consists of three main steps: (3.1)
the cloud masking procedure to filter illuminated cloud regions, (3.2) the
cloud-phase discrimination, and (3.3) the geometric allocation of the
classified cloud profiles with respect to height and temperature.
Cloud masking procedure
Compared to illuminated cloud sides, the photon paths in shadowed cloud
regions are longer, which is related to more absorption events. This
absorption due to cloud particles is not locally restricted to the cloud-side
parts where the camera is pointed at. In fact, the spectral radiation coming
from shadowed cloud regions is affected by absorption by cloud particles from
cloud parts outside the FOV of each individual spatial camera pixel. Since
the spectral signature of reflected radiation from shadowed regions of cloud
sides is contaminated by a significant fraction of diffuse radiation
originating from unknown cloud regions, a cloud masking technique was
developed to discriminate illuminated and shadowed cloud regions. In
ground-based observations the reflected radiation measured from shadowed
cloud regions showed spectral signatures influenced by the spectral surface
albedo due to interaction between clouds and the surface .
This interaction is reduced for several reasons for aircraft observation of
DCC. The reflected radiation is observed from higher altitudes than from the
ground. This is related to changes in the range of scattering angles.
Furthermore, the distances between surface and in particular the upper parts
of the clouds are much larger. Therefore, scattered radiation from the
immediately adjacent cloud regions has a greater effect on the spectral
features in the shadowed cloud areas than the surface. Since spectral
indication of the surface could neither be observed nor simulated for
airborne measurements, a different approach is chosen based on the
distribution of color values in the observed cloud scene. Three wavelengths
(λB= 436, λG= 555, and
λR= 700 nm) corresponding to wavelengths of the RGB
(red,
green, blue) color space are selected to calculate a simplified RGB color
value for each measured spectrum, which takes into account the sensitivity of
the human eye on the different colors by differential weighting of the three
wavelengths :
RGB=0.2126⋅R+0.7152⋅G+0.0722⋅B,
where R, G, and B represent the normalized spectral radiances. The histogram
of the RGB color values for each cloud scene is used to identify the
illuminated and shadowed cloud areas.
(a) Field of RGB color values from simulated spectral radiances for
cloud-side viewing geometry with a sensor elevation angle of 10∘ and
a relative azimuth angle of 60∘. (b) Histogram of RGB color values
of the field shown in (a). (c) Histogram of RGB color values for a measured cloud scene shown in the inset.
(d) Identified illuminated cloud sides of the observed cloud scene are highlighted in brighter colors.
Before showing an application, the procedure is illustrated using simulated cloud-side reflectivity observations.
In this manner, we can directly compare the classification of illuminated and shadowed cloud regions (i) derived from
known cloud and viewing geometry and (ii) derived from the histogram of the RGB color values. The cloud field was
generated by the Goddard Cumulus Ensemble model for a model domain of 64 × 64 km with
a horizontal resolution of 250 m and a vertical resolution between 0 and 10 km altitude of 200 m. From 10 to 120 km altitude
the simulations are performed with a vertical resolution ranging between 1 and 5 km. The maximum extension of the liquid water
clouds from bottom to cloud top ranges from 1.0 to 7.4 km altitude. As MCARATS is a forward-propagating radiative transfer model
(RTM) the simulations are performed for each grid point representing an observation altitude of 4 km. The sensor is pointed at
an elevation angle of 10∘ and with a relative azimuth angle to the Sun of 60∘ to also trigger areas of shadowed
clouds. Figure a displays the RGB color values derived from the radiance simulations at each of the
256 × 256 grid points. From information of the viewing geometry of the sensor and Sun (solar zenith angle
θ0= 30∘) and the setup of the clouds in the model domain, each observed cloud pixel is
classified as shadowed or illuminated. The histogram of the simulated RGB color values is shown in Fig. b
as a
black line. Two modes are visible, which coincide with the two sub-classes of illuminated (red) and shadowed (blue) cloud
regions which were calculated from the cloud and viewing geometry. To identify the illuminated cloud areas for an unknown
cloud geometry, as is the case for real measurements, only the brightest pixels that correspond to the right-most mode in
the RGB histogram are selected. Since the left side of this mode may also include data from shadowed regions, data larger
than the maximum of this mode will be classified as illuminated and used for the cloud-phase retrieval.
The procedure is applied for an example cloud scene observed during
ACRIDICON-CHUVA from 19 September 2014. During the roughly 1 min flight
leg the aircraft did not change its flight attitude, resulting in almost
constant relative azimuth angle (angle between the Sun and the viewing
direction of specMACS) of 68∘ and solar zenith angle
(θ0= 39∘). Note that all other selected cloud
cases in this study have similar restrictions concerning the flight attitude
and time period (about 1 min) to guarantee comparable illumination
conditions in one cloud scene. Figure c illustrates the RGB
histogram as calculated for observations of specMACS with an elevation
ranging between -13 and +12∘. The inlay in Fig. c
shows the cloud situation as observed from specMACS. Applying the threshold
criteria to identify the illuminated cloud parts gives a cloud mask as
presented in Fig. d, where the illuminated cloud parts are
highlighted.
Cloud-phase discrimination
Vertical profiles of the relationship between temperature and particle size
to identify the mixed-phase cloud layer have been used by, e.g.,
. For continental conditions (as often observed in the
Amazon Basin) the droplet size may not significantly increase between the
main coalescence and mixed-phase regions. Therefore, for these cases it is
difficult to define the height or temperature where phase transition takes
place through the increase in the droplet size. As presented in
, , and , another
method based on differences of the refractive index of ice and liquid water
between 1550 and 1700 nm wavelength can be applied to determine the
thermodynamic water phase. The phase index IP based on
spectral radiances (I) was introduced as follows:
IP=I1700-I1550I1700.
For ground-based application with corresponding viewing geometry, vertical
profiles of the phase index were simulated by . A
significant gradient in the vertical profile of the phase index was observed
between liquid water and mixed-phase layer, but also between mixed-phase
layer and ice phase. A similar behavior was also found for the reflectance
ratio at 2.10 and 2.25 µm as reported by . They
observed a strong gradient in the profile of the reflectance ratio. This is
due to the fact that the imaginary part of the refractive index, which
determines the spectral absorption, is different between ice and liquid water
particles in the two wavelength ranges used by and
. In the following, results from radiative transfer
simulations using MCARATS are presented. The viewing geometry and the
atmospheric description are adapted to the conditions during ACRIDICON-CHUVA
on 19 September 2014. These simulations are performed to demonstrate that ice
and liquid water phase can be separated from the transition layer under
different conditions similar to the results reported by .
Note that due to the different viewing geometry, another angular range of
the scattering phase function was observed than for ground-based
measurements. This might have an effect on the characteristics of phase index
profile in particular with respect to separation of the mixed-phase layer.
The model domain used for the simulations had 140 × 40 × 99
grid cells at a horizontal resolution of 250 m and a vertical resolution of
200 m below 14 km altitude and variable resolution above. For each grid cell
in a flight altitude of 8 km, the spectral radiance at 1550 and 1700 nm
wavelength is simulated for sensor viewing elevation angles between -20 and
+20∘ corresponding to the FOV of specMACS. Two simplified cloud
scenarios with different profiles of cloud effective radius and water content
are assumed. In both scenarios the clouds ranges from 4.0 to 11.0 km altitude
with a mixed-phase layer between 6.4 and 7.0 km. While the first scenario
uses constant values of cloud effective radius (reff= 20 µm
for liquid water and ice) and water content (0.7 gm-3), the
second scenario assumes variable profiles of the microphysical parameters.
These two scenarios are chosen to identify effects on the
IP profile caused by changes in (i) the phase state itself
(scenario 1) and (ii) the cloud particle size and water content (scenario
2). From the 3-D simulations of the spectral radiance at 1550 and 1700 nm, the
phase index is calculated following Eq. (2). For each modeled grid cell in
the model domain with a horizontal distance between 3 and 8 km from the cloud,
a combined IP profile is derived from the different viewing
elevation angles. Such IP profiles are plotted in Fig. a in black dots. Due to the variation of cloud distance
and viewing elevation angle, the IP profile comprises reflected
radiances originating from various scattering angles. For the first scenario
with constant microphysical parameters, three distinct clusters corresponding
to the phase state of water and the zone of phase transition, with negative
values for pure liquid water, can be found. In the mixed-phase layer the
phase index shows a steep increase to values larger than 0.15. The absolute
difference of the phase indices between mixed-phase layer and pure ice-phase
layer is less pronounced than between liquid and mixed-phase layer. This
might be caused by the fact that the contribution of ice particles within the
mixed-phase layer leads to an increased absorption of radiation resulting in
an increase in the phase index. The variability of the phase index for
constant microphysical conditions in each of the phases is caused by the
effect of the different viewing geometries. The vertical cloud structure is
observed from different sensor elevation angles and distances. As the
scattering phase function depends on the scattering angle, the wavelength, and
the particle shape, the viewing geometry of the sensor relative to the
position of the Sun (here θ0= 30∘) also modulates
the phase index. The second cloud scenario assumes variable cloud
microphysical properties. In general, in convective clouds, the size of ice
particles is higher than the size of liquid water particles. Therefore, the
second scenario represents a more realistic vertical distribution of the
particle effective radius and water content than the first scenario. The
corresponding vertical profiles of the effective radius and the water content
of the cloud are plotted in Fig. b. The mixed-phase layer
is characterized by the maximum particle sizes of liquid and ice particles
over the entire profile, but lower water content compared to regions above
and below. As concluded by , the phase index becomes less
variable for a water content of more than 0.4 g m-3 (variation
lower than 7 %). This holds true for most of the DCCs when cloud edges are
excluded, which are optically thinner than the inner regions of the cloud.
Consequently, it is primarily the particle size and the phase state which drive the changes
in the phase index with height. Less impact is attributed to the change of
the sensor elevation angle since the variability of the phase index with
respect to the viewing geometry for each phase state in the first cloud
scenario with fixed cloud microphysics is lower than the variability of
IP due to the changed cloud properties in the second cloud
scenario. The mixed-phase layer for the second scenario is characterized by a
significant increase in the phase index with height. Once the pure ice phase
is reached, the slope of IP decreases. In the following, the
magnitude of vertical change of the phase index will serve as an indicator of
the position of the mixed-phase layer.
(a) Phase index derived for simulated clouds with variable LWC/IWC and effective radius and fixed values
of microphysical properties. (b) Profile of corresponding cloud with variable LWC/IWC and reff.
Cloud geometry retrieval
Due to the spatial dimension of the specMACS-SWIR instrument, reflected
radiances are measured for 320 different angles with an average
pixel-to-pixel spacing of about 0.11∘. To quantify the vertical
position of the mixed-phase layer in terms of height or temperature,
information on the cloud distance is required. For that purpose, collocated
images of the GoPro camera are combined with flight attitude data to apply
stereophotogrammetric methods. The theoretical background on photogrammetry
is given in , while applied these
techniques for cloud geometrical reconstruction. The mathematics for the
geometry retrieval, as it is used in this study, is based mainly on the
method described by . They deployed a side-looking camera
onboard of an aircraft to detect the position of cloud features, similar to
the setup presented in this work.
(a) Schematics of stereophotogrammetric observations of cloud point C from aircraft position P1 and P2
with projected image points C1 and C2. (b) Illustration of aircraft and camera coordinate systems.
To estimate the distance to the observed
cloud element (C) two images from different positions (P1 and P2) with a
projection of the observed point in both images need to be taken (C1 and C2,
called tie points) as illustrated in Fig. a. The geometric
problem comprises three coordinate systems: for the camera, the aircraft, and
the geographic coordinate system (longitude, latitude, and altitude) for the
observed point C . Coordinate transformations are required
to relate the different coordinate systems. Figure b
illustrates the aircraft and camera coordinate system, which differ because
the GoPro camera faces perpendicular to the flight direction. For example, a
positive pitch angle of the aircraft (associated with rotation around the
aircraft ya axis) rotates the camera (image) around the camera's
xi axis as can be deduced from Fig. b. The x and
y axis of the world coordinate system (not shown) are pointed to the east
and to the north, respectively, while the z axis is perpendicular to the
x–y plane (pointing upward). Each selected image in the camera system
(xi, yi, zi) is transformed into the aircraft coordinate
system (xa, ya, za), and finally into the world system
(xw, yw, zw). This transformation requires the rotation of the
coordinate systems with respect to the three Euler angles of pitch, roll, and
yaw using the 3 × 3 rotation matrices for the aircraft to world
[Rwa], and camera-to-aircraft
[Rai] system:
xwywzw=RwaRaixiyizi.
The general form of the two rotation matrices for system 1 to system 2
(either “a” to “w” or i to a) are as follows:
[R21]=cosψcosθcosψsinθsinϕ+sinψcosϕ-cosψsinθcosϕ+sinψsinϕ-sinψcosθ-sinψsinθsinϕ+cosψcosϕsinψsinθcosϕ+cosψsinϕsinθcosθsinϕcosθcosϕ,
with ϕ=-(ϕa-180∘), θ=θa, and
ψ=(ψa-90∘) for aircraft to world coordinates and
ϕ=-ϕi, θ=-θi, and
ψ=-ψi for camera-to-aircraft coordinates.
After coordinate transformation, trigonometric methods are applied to calculate the distance between
the camera positions P1 and P2 to the observed point C. Repeating this procedure for a number of points yields a
relationship
between elevation angle and cloud height. Note that the elevation angle represents the elevation angle of the selected tie
point of the camera image after correction based on the aircraft attitude data. It gives the elevation angle above or below
the flight altitude. For better selection of the tie points, which is done manually, the contrast of the images is increased
for better identification of recognizable structures of the cloud image. Figure illustrates the cloud
geometry retrieval for a cloud scene from 19 September 2014. The selected cloud scene shows a strong convective cloud
embedded in a stratiform cloud layer. After increasing the image contrast (Fig. a) several tie points
with distinctive cloud features of individual clouds were selected. The same tie points are chosen in a second image
taken about 10 s later. Choosing a short time interval helps to reduce the uncertainty of the method induced by
cloud movement. From stereographic analysis of these tie points the distances to the cloud points (in kilometers) are
determined (Fig. b). From cloud distance and viewing elevation angle the height is calculated.
Cloud-top heights for this case are in the range of 12 km, while the top of the stratiform layer is at about 6 km
altitude. The corresponding isolines in Fig. c show quite a homogeneous horizontal distribution
with negligible dependence on the azimuth angle for this particular cloud case. Therefore, the correlation between
elevation angle and height is approximated by a polynomial fit of the third order as plotted in Fig. d.
This fit is used to relate the elevation angles of the specMACS instrument to a cloud height. For all studied cloud cases
of the flights AC10, AC13, and AC18, such simplified correlations between elevation angle and height are determined under
the condition that the azimuthal dependence could be neglected which is fulfilled predominantly for sufficiently small
cloud sections in the horizontal direction.
(a) Cloud image from GoPro camera with enhanced edges and selected tie points from 19 September 2014.
(b) Calculated distances in km to the individual cloud points for the cloud scene displayed as isolines.
(c) Corresponding isolines of calculated heights. (d) Relationship of height and elevation angle derived for
the cloud case including a polynomial fit with a correlation coefficient of R2= 0.987.
The accuracy of the cloud geometry retrieval depends on the distance to the
observed cloud and the uncertainty of the angle determination. Uncertainties
related to pixel selection are estimated with ±5 pixels (0.25∘),
which corresponds to an uncertainty of 130 m for a cloud distance of 30 km
(maximum distance of observations). Additionally, the fitting method results
in mean deviations of 200 m. Overall, uncertainties between 200 and 300 m are
calculated for the observing conditions during ACRIDICON-CHUVA.
Application
From the 14 scientific flights, 3 days (AC10, AC13, and AC18) are selected
with the best observation conditions for specMACS, namely (i) no cloud layer
above the observed cloud (no cirrus), which contaminates the spectral
signature, (ii) high proportion of illuminated cloud parts in the vertical
direction of the cloud, (iii) flight altitude that allows for measurement of an
extended vertical region of the cloud considering the limited FOV of
specMACS, and (iv) isolated clouds with recognizable structures for cloud
geometry retrievals.
Phase profiles from AC13 representing polluted aerosol
conditions will be compared to the 2 days with less aerosol pollution.
Effects of aerosol conditions on the height and thickness of the mixed-phase
layer will be investigated. Second, it will be demonstrated how comparable
the different observation strategies (cloud side, cloud top, and in situ) are.
Flight track (white line) and selected time periods of cloud-side observations during AC13
(19 September 2014). Additionally, the 250 m resolution product for channel 1 (620–670 nm) of the
Aqua-MODIS instrument from 17:50 UTC is shown in the background. Figure is similar to that presented in .
Case study for flight AC13 (polluted aerosol conditions)
During flight AC13 on 19 September 2014, several periods of cloud-side observations are found. The flight track and the
corresponding MODIS image are shown in Fig. . The 250 m resolution radiance of channel 1 (620–670 nm)
of the MODIS overpass from 17:50 UTC illustrates the cloud coverage. The five colored lines denote the periods of cloud-side
observations between 17:50 and 19:00 UTC. The white arrows indicate the flight direction with specMACS pointing towards the
clouds on the right-hand side of the aircraft. The flight altitude for this 1 h flight track ranged between 5 and 10 km.
As a result of cloud masking and cloud geometry analysis, the profile of the phase index for a cloud scene
(section #A in Fig. ) is shown in Fig. . The phase index is calculated in bins of 100 m
in the vertical direction. The standard deviation is indicated by the error bars. A distinctive increase in the phase index is
visible at 6.5 km altitude. Below that altitude a negative phase index indicating the liquid water phase is derived. Within the
mixed-phase layer the phase index increases sharply. The upper limit of the mixed-phase layer is determined to be at 7.1 km. Above
that altitude the variation of the phase index caused by changing particle sizes and viewing geometry is less pronounced.
Sixteen cloud cases are investigated for flight AC13. Each cloud scene is classified with respect to the phase state based on the
profile of the phase index. Figure a presents the statistics over all scenes. The background color of the scene
number corresponds to the flight section as presented in Fig. . Obviously not all profiles show each of the
phase states, mainly for two reasons. First, the cloud particles may have the same phase state, or, second, the viewing
geometry with respect to FOV, flight altitude, cloud height, and distance restricts the vertical range of the cloud observation.
Overall, the depth (Δzmix) and vertical position (ztop, zbot) of the mixed-phase layer
is highly variable for all cases: with Δzmix= 1.2 ± 0.4 km (1σ standard deviation),
zbot= 6.2 ± 0.3, and ztop= 7.4 ± 0.4 km. Even for similar flight sections (as in #B and #D, shown in Fig. )
the upper and lower limit of the mixed-phase layer can vary by up to 900 m, which is larger than the uncertainty of the retrieval
method. The corresponding temperature scale is displayed as nonlinear secondary y axis.
Mean phase index profile for cloud scene shown in Fig. . The mixed-phase
layer is indicated by the colored area.
(a) Phase classification of studied clouds based on specMACS observations during flight AC13.
(b) Profile of LWC measured with the hotwire probe between 17:50 and 19:00 UTC. (c) Aspherical fraction
derived from CAS-DPOL in situ data. (d) NIXE-CAPS in situ measurements of liquid, mixed phase, and ice
(see Table for definitions). Note that time is given in decimal hours. (e) Short horizontal
flight section in the upper part of the mixed-phase layer showing the relationship of vertical wind speed and
classified asphericity of cloud particles. Symbols as in (d). (f) Classification of cloud phase (ice or liquid)
from MODIS observations of cloud tops. (g) Mean profile of effective particle radius from ensemble method
based on MODIS retrieval data of cloud-top effective radius. The black horizontal line indicates the level
of largest ice particles.
The variability of the mixed-phase layer in depth and height within a single
cloud cluster shows that the vertical distribution at least at the cloud
edges is variable. In situ data are used to investigate whether such a variability
is also observed in the cloud's interior. In situ
measurements of CAS-DPOL and hotwire data of the 1 h flight sequence
(17:50–19:00 UTC) during AC13 are shown in Fig. b and c. The
light dots are 1 Hz data, while darker lines represent the 10th and 90th
percentiles and the mean LWC and AF (squares), binned into 600 m
altitude bins. Regions of mixed-phase clouds are characterized by a decrease
in LWC (decrease in the 90th percentile with altitude) and/or an increase in
AF. In these in situ measurements of LWC and AF, the mixed-phase region
extends between 6.4 and 8.7 km. However, the profiles shown in Fig. b and c are based on data sampled over the entire cloud
cluster including clouds at different stages of evolution, and profiles of
individual clouds cannot be derived from this data set, which prevents a
direct comparison of the in situ and remote measurements. The asphericity of
cloud particles in the size range of 20–50 µm derived from NIXE-CAPS is
shown in Fig. d for the 1 h time frame of the cloud
observations. The data are classified as listed in Table . The
heterogeneity of cloud particle asphericity between 5 and 8 km altitude is
observed from its variable classification during the ascent around 18:00 UTC
with solely spherical particles (possibly also related to small spherical ice
particles) and during the descent between 18:25 and 18:80 UTC with spherical
and aspherical particles. Mainly aspherical particles of group II are
observed, indicating the existence of large ice particles with sizes larger
than 50 µm. Except for two single cases, a larger number of spherical
particles (open green circles) is observed up to an altitude of 8 km. From
the descent flight track the position of the mixed-phase layer is estimated
between 6 and 8 km. For example, a closer look at the asphericity is taken
for the time range between 18:28 and 18:34 UTC (Fig. e).
At a constant flight level near the upper boundary of the mixed-phase layer
the occurrence of spherical and aspherical particles is somewhat separated.
While mainly spherical particles are observed during this selected flight
section for vertical wind speeds between ±1 m s-1, there are also
segments with higher vertical wind speeds (between -3 and 5 m s-1). For
this section (around 18.315 UTC) large aspherical particles representing ice
particles were also measured. This suggests that the vertical distribution of
ice and liquid particles is affected by updrafts and downdrafts within a
convective cloud, and therefore it is not homogenous inside the same cloud.
After showing these results from in situ and cloud-side measurements, we also
present retrievals of the phase state based on cloud-top MODIS observations.
In Fig. f the frequency of liquid- and ice-phase
observations for altitude bins of about 200 m is presented. Fully developed
deep convective clouds with cloud tops between 10 and 14 km (classified as
ice cloud) and low level cumulus clouds up to 6 km (liquid water clouds) are
detected. Cloud-phase information from the assumed phase transition layers is
not available in Collection 6. Nevertheless, there are some levels with low
frequency classified as ice and liquid phase (8–11 km), corresponding to
temperatures between -20 and -42 ∘C. In particular, at very low
temperatures (lower than -38 ∘C) the presence of liquid particles can
be excluded even for situations of homogeneous freezing. In fact small ice
particles may be misinterpreted as liquid particles by the retrieval
algorithm at this level .
We applied the ensemble
method to derive profiles of the effective particle size and to estimate the
glaciation height and temperature following the retrieval technique of
for the MODIS scene. For better comparison, the brightness
temperature as a vertical coordinate is converted into altitude. Cloud-top
brightness temperatures (at 11 µm, corresponding to MODIS channel 31) are
simulated for variable cloud-top heights and an atmospheric profile of
temperature and humidity as measured by the aircraft. The best agreement of
simulated and measured cloud-top brightness temperature is used as proxy of
the cloud-top altitude. The result is presented in Fig. g.
The particle size increases with altitude up to a height of about 9.0 km
(horizontal black line). This level is assumed as glaciation height, the
upper level of mixed-phase layer. The standard deviation of the binned (2 K
bins in brightness temperature) particle sizes (horizontal error bars) is
significantly larger for altitudes below 11 km, indicating a larger
variability of the cloud particle size and a smaller statistic. Furthermore,
a second but smaller peak of the particle size is found at about 6 km
altitude. From the conceptual model of cloud particle size profiles inside a
DCC e.g., it might indicate the bottom of the
mixed-phase layer, when cloud particle size starts to increase. However, this
increase is less pronounced than presented in .
Comparing the glaciation height from MODIS with NIXE-CAPS in situ data and results from specMACS observations shows a deviation of
about 1.0–1.5 km between the different retrieval techniques and observation strategies. However, the mean profile over the entire
cloud cluster derived from CAS-DPOL measurements exhibited a similar glaciation height (of about 8.7 km) as found from the MODIS data.
This shows that the satellite-based ensemble method may be representative of a large cloud field. However, for individual clouds NIXE-CAPS
and specMACS measurements have shown lower glaciation heights. The most likely reason is related to the fact that the ensemble method
relies on cloud-top observations of growing clouds in different stages of evolution. As shown in
Fig. g, it is primarily
particle sizes between 22 and 27 µm which are derived, indicating that the profile is dominated by measurements of clouds in the mature
stage. At this stage, the particle phase may be altered by updrafts and downdrafts within the clouds as was shown in Fig. e.
This leads to an enhanced horizontal variability of the cloud phase state which cannot be resolved by passive remote sensing from cloud-top
observations. Another but minor reason of the discrepancy between ensemble method and NIXE-CAPS/specMACS measurements is related to
the retrieval uncertainty of the effective cloud particle radius. While scattering properties are well defined for liquid water particles,
they are variable for ice particles due to differing habits and crystal shapes . This gets even more complicated
for cloud tops where phase transition starts. Additional retrieval uncertainties of the particle size directly contribute to the derived
profile of reff.
(a) Phase classification of studied clouds based on specMACS observations during flight AC18.
(b) Same as (a) but for AC10. (c) GoPro image of cloud scene during AC10. (d) Phase index as derived
from specMACS during AC10 for illustrated clouds from (c).
Comparison with less polluted conditions
Profiles of the phase state for two other flights (AC10 and AC18) performed
under moderate aerosol conditions are presented in
Fig. . On both days the number of complete profiles
showing the liquid, ice, and mixed-phase layer is smaller compared to AC13.
Mainly low level clouds or cloud parts with liquid water were observed during
AC18. The lower boundary of the mixed-phase layer is estimated to be about
5.5 km (-4 ∘C). From NIXE-CAPS measurements, large aspherical ice
particles are found down to 5 km (-1 ∘C), whereas spherical particles
assumed to be liquid water were observed up to 8.7 km. In contrast, the
specMACS data exhibit ice phase down to 7.7 km. As in the case of AC13, the
cloud-top MODIS retrievals of the phase state only distinguishes between
liquid and ice phase. Because of the low statistical significance of clouds
with cloud tops higher than 6 km in the MODIS data, no profile of effective
drop radius is derived.
On flight AC10 no in situ data within mixed-phase clouds were obtained. The MODIS phase product shows ice cloud tops between 11 and 15 km
altitude and liquid water clouds up to 4.5 km. However, the profile of the effective particle radius based on the ensemble method retrieval gives
a glaciation temperature of 260 K, which corresponds to an altitude of about 7.2 km. The specMACS profiles as plotted in Fig. b
show highly variable mixed-phase layers. While clouds #1–#3 with cloud tops between 6.0 and 6.8 km are classified as liquid water clouds, the profiles
of the phase index of clouds #4–#6 reveal the existence of ice particles also between 4 and 7 km altitude. As illustrated in the RGB image taken by
the GoPro camera (Fig. c), clouds #3 and #4 are in close vicinity but in different states of evolution. The diffuse cloud areas
with smoother texture in the GoPro image of cloud #4 indicate precipitation, which explains positive phase indices down to 4 km corresponding to
more than 0 ∘C. As Fig. d shows, the phase index can vary significantly for one altitude level depending on the
occurrence of precipitation. Consequently, the individual state of evolution of each cloud determines the distribution of particle sizes and
phase state. Also, local strong downdrafts can transport ice particles into lower levels, which will be interpreted as a mixed-phase layer from
the cloud-side observation perspective. Due to the horizontal variability of cloud phase inside a cloud cluster for example caused by updrafts and
downdrafts, in situ measurements may only reveal liquid-phase particles. A direct comparison between the observation strategies is subject to
restrictions because of temporal and spatial variability of cloud properties in convective systems.
From theory, the mixed-phase layer is expected to be higher for polluted aerosol conditions than for cleaner aerosol conditions, which can
partly be confirmed by comparison of the three cases. From cloud-side observations, we find that the lower boundary altitude of the mixed-phase layer tends to be higher for polluted conditions (AC13: 6.0–6.5 km) than for the moderate case of AC18 (5.6 ± 0.2 km), while the
upper boundary is shifted from 6.8 ± 0.2 km (moderate case AC10) to 7.4 ± 0.4 km (polluted case AC13).
Conclusions
The vertical evolution of deep convective clouds is linked with
the phase transition from liquid water via the mixed phase to ice. Aerosol
particles may alter the radiative effects of cloud particles (also with
respect to their phase state), their lifetime, and the formation of
precipitation. This study documented the vertical distribution of the cloud
phase for different aerosol conditions as measured during the ACRIDICON-CHUVA
campaign over the Brazilian rainforest in September 2014. Our approach
applies a retrieval method to quantify the height range of the mixed-phase
layer. cloud-side observations performed by an imaging spectroradiometer were
used to determine a phase index based on differential absorption by ice and
liquid water in the spectral range between 1550 and 1700 nm. Negative values
of the phase index indicate liquid particles, whereas ice particles are
characterized by a positive phase index. It was shown by 3-D radiative
transfer simulations that the mixed-phase zone is characterized by a
significant gradient in the profile of the phase index.
A cloud mask method to discriminate between shadowed and illuminated cloud regions was presented to exclude the shadowed areas in the
cloud scene. 3-D radiative transfer simulations were performed to validate the approach. Since the imaging spectroradiometer delivers
spectral radiation data as a function of viewing zenith angle, the derived mean vertical profiles of the phase index needed to be
referenced to altitude ranges. For this purpose, stereographic methods were applied to collocated GoPro camera observations to
estimate the cloud geometry in terms of cloud height profiles and distance to the aircraft.
The profiles of several individual clouds were classified with respect to their zones of phase states. Depending on the viewing geometry
and cloud distance, the pure liquid, ice-phase, and transition-phase layers were identified. It was found that the height
and thickness of the layers of phase transition were variable (900 m in upper and lower limit) even for one compact cloud cluster measured
during flight AC13 with polluted aerosol conditions. Here, the first ice particles were found at temperatures between -3 and -9 ∘C, while
full glaciation was observed between -10 and -20 ∘C. For moderate aerosol conditions, only few cases exhibited liquid water, mixed
phase, and ice phase, which limited the statistical significance of the comparison with AC13. However, comparing the glaciation heights
of AC10 (6.8 ± 0.2 km) and AC13 (7.4 ± 0.4 km) we found an indication of an increase in glaciation height and a decrease
in glaciation temperature for polluted aerosol conditions. With respect to the occurrence of first ice particles, the lower boundary
of the mixed-phase layer was derived with 6.0–6.5 km for polluted conditions, whereas for AC18 the altitude was shifted down to
5.5–6.0 km, which agrees with theory.
Also, in situ measurements of the cloud particle size distribution together
with the asphericity of particles between 20 and 50 µm, measured by the
cloud spectrometer NIXE-CAPS, were used to estimate the cloud's phase
. Aspherical particles can be considered as ice, whereas
spherical shapes are related to liquid droplets or spherical ice. In contrast
to cloud-side remote sensing, in situ observations represent point
measurements within the cloud. Therefore, in situ profile information of an
individual cloud is a combination of data from different states of evolution.
Consistent results of mixed-phase zone levels were found from specMACS and
NIXE-CAPS measurements, for the flight AC13 with most individual cloud cases
showing pure liquid, mixed-phase layer, and pure ice phase.
In addition to in
situ and cloud-side measurements, the glaciation temperature was derived
applying an ensemble method based on MODIS data, which assumes
time–space exchangeability for a cluster of clouds with different states of
evolution. For the polluted and moderate flights, retrieval results of the
effective particle size at cloud top were combined into one single profile.
For flight AC13 the glaciation height of 9.0 km (-26 ∘C), defined by
the level of maximum particle size, deviates from the in situ (8 km) and
specMACS results (6.8–8.2 km). However, for the moderate aerosol case the
glaciation height was much lower at about 7.2 km (-13 ∘C), similar to
the height derived from specMACS observations (7 km). The presented study has
shown that the occurrence of ice particles and the level of the mixed-phase
layer vary by several hundred meters even for similar atmospheric
conditions. Two cloud cases in close vicinity clearly show different cloud
phases at the same altitude. It is assumed that downdrafts and falling
precipitation in well-developed clouds alter the retrieval results of the
phases' vertical distribution. It is concluded that the assumed
time–space exchangeability used in the ensemble method can give a
simplified picture of the vertical distribution of the phase within a field
of convective clouds of different stages of evolution. Cloud
tops where phase transition (from liquid to ice) starts and ends particularly need to be
observed by satellite to profile the thermodynamic phase. The number of
these observations has to be significant, since the particle sizes are
averaged over a larger domain. Thus, in general the ensemble method can give an
indication when phase transition arises for the first time. However, for
estimation of the cloud phase profile at a later stage of the DCC evolution,
in situ and also cloud-side remote sensing might be the better observation
strategy when phase distribution is altered, for example by updrafts and
downdrafts.
Planned future studies include observations of individual convective clouds
to document their evolution from growing to mature and finally to dissipating
stages of development. We intend to deploy our sensor on ATTO
, which is 325 m high and is used to perform continuous
monitoring of chemical, meteorological, and aerosol parameters. The ATTO tower
is located near the Equator (a region with daily occurrence of DCCs in a
highly variable environment with respect to concentrations and types of
aerosol particles) and will serve as an ideal platform for upcoming studies.
The data from the ACRIDICON campaign are stored in the permanent HALO database:
https://halo-db.pa.op.dlr.de/.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The ACRIDICON-CHUVA campaign to study deep convective clouds and precipitation over
Amazonia using the new German HALO research aircraft (ACP/AMT inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
The ACRIDICON-CHUVA campaign was supported by the Max Planck Society (MPG),
the German Science Foundation (DFG Priority Program SPP 1294), the German
Aerospace Center (DLR), the FAPESP (Sao Paulo Research Foundation) grants
2009/15235-8 and 2013/05014-0, and a wide range of other institutional
partners. It was carried out in collaboration with the USA–Brazilian
atmosphere research project GoAmazon2014/5, including numerous institutional
partners. We would like to thank Instituto Nacional de Pesquisas da Amazonia
(INPA) for the local logistic help prior to, during, and after the campaign.
Thanks also to the Brazilian Space Agency (AEB: Agencia Espacial Brasileira)
responsible for the program of cooperation (CNPq license 00254/2013-9 of the
Brazilian National Council for Scientific and Technological Development). The
entire ACRIDICON-CHUVA project team is gratefully acknowledged for
collaboration and support. Evelyn Jäkel gratefully acknowledges funding
of parts of this work by the German Research Foundation (DFG) under grant
JA2023/2-2.
Edited by: Ulrich Schumann
Reviewed by: three anonymous referees
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