In this study, which is the third part of the HaloCam series after
Cirrus clouds cover about one-third of the globe on average
Over the past few decades the natural distribution of ice crystal shapes has been investigated by laboratory studies
Therefore, satellite-based methods have been investigated in recent years to retrieve information about ice crystal shape with large spatial and temporal coverage.
Retrievals of ice crystal habit from multi-angle satellite observations were pioneered by
Investigation of ice crystal shapes in thin cirrus clouds using spaceborne or airborne passive remote sensing is more challenging due to the unknown surface reflectance, especially over land.
Most in situ observations report ice crystals with more rough surfaces and complex rather than pristine shapes:
These findings of predominantly rough and complex crystals with featureless scattering phase functions seem to be in disagreement with sightings of halo displays, which form by refraction and reflection of light by smooth hexagonal ice crystals.
In this study, we investigate a new method to retrieve ice crystal shape and the degree of surface roughness from calibrated camera observations of halo displays using HaloCam
Halo displays are produced by hexagonal ice crystals with smooth faces via refraction and reflection of light as described by
Examples of halo displays observed at the Meteorological Institute of LMU in Munich. The Sun is blocked by a black circular shade to avoid stray light and saturation of the camera sensor. Top left: 22
Figure
In this study we present a novel method to retrieve ice crystal shape and surface roughness in cirrus clouds using ground-based imaging observations of the 22 and 46
The paper is structured as follows. Section
Cirrus clouds featuring a halo display contain at least a certain fraction of smooth hexagonal ice crystals. The frequency of these cirrus clouds, which will be referred to as “halo-producing” cirrus in the following, provides therefore a first estimate of the minimum fraction of smooth hexagonal ice crystals in cirrus clouds.
More detailed information about ice crystal properties can be obtained by analyzing the brightness contrast of the 22
Optical properties based on
Note that the retrieved ice crystal effective radius, shape, and SCF will depend on assumptions about the underlying particle distribution, since the bulk optical properties, e.g., the extinction coefficient
Ice crystals in cirrus clouds are known to follow multimodal rather than monomodal size, shape, and surface roughness distributions. Therefore, matching ice crystal properties could be retrieved for mixtures of arbitrary complexity.
However, this study aims at finding the simplest ice crystal model with the minimum degrees of freedom that matches the observations within the measurement uncertainty. Inspired by
Figure
Sensitivity of the halo features, represented here by the 22
To obtain representative results for ice crystal properties of halo-producing cirrus clouds, long-term observations are required.
These are provided by the weather-proof Sun-tracking camera HaloCam
The COT introduces an ambiguity in the brightness contrast of the 22
HaloCam
Using additional observations of AOT requires clear-sky periods before and/or after the 22
The HaloCam
Lookup table parameters: minimum value, maximum value, and resolution for smooth crystal fraction (SCF), effective radius (
The retrieval was performed as illustrated in Fig. For each ice crystal habit, the respective LUT was selected. The LUT was further constrained to the wavelength representative of the image channel, here 618 nm. In a next step, the AOT dimension of the LUT was constrained using AERONET Sun photometer observations, interpolated to 618 nm.
Since AERONET's AOT can only be measured during clear skies, the values during the observation of the halo display were estimated to range around the daily mean AOT within a Then, for each HaloCam For each HaloCam For the retrieval, each of the five averaged radiance distributions measured with HaloCam The LUT element with the minimum RMSE, averaged over the scattering angle range, represents the best match for the cirrus optical and microphysical properties. In case the average RMSE between LUT and measurements exceeds the 2 The resulting SCF, effective radius A second iteration of the retrieval is performed starting from step 5 to further constrain the COT dimension of the LUT.
Using the retrieved effective radii for the five image segments, the minimum and maximum
Flowchart visualizing the individual steps of retrieving representative ice crystal properties by finding the best match between HaloCam observations (yellow) and a LUT of radiative transfer simulations (blue).
Retrieval results evaluated for all 8 d. Mean value and 1
Observations and LUT are compared in the scattering angle range between
To ensure that only samples with a clearly visible 22
Sundogs appear in the left and right image segments (nos. 1 and 5) only for
Figure
Retrieval results for 21 April 2016 for three selected YG13 ice crystal shapes:
Retrieval results for all 8 d listed in Table
For solid columns (Fig.
Figure
Figure
Table
In the following we assess how stable the retrieved ice crystal habit is considering the necessary assumptions regarding spectral response, aerosol type, and radiometric uncertainty. Using the representative wavelength of HaloCam
Best match habit for the retrieval applied to HaloCam
Table
The presence or absence of the 46
As mentioned above, applying the retrieval to HaloCam For the sake of completeness, we provide here the ice crystal properties used for the DISORT simulations:
Fig.
Ice crystal plates proved to match the observations only for larger effective radii of about 50
In the following, the results of this study will be further discussed and compared with the literature. Previous studies using passive remote sensing have retrieved quantitative information about ice crystal microphysics, primarily from space.
Spaceborne imaging of optically thin clouds over land is challenging since the measured reflectances are very sensitive to the surface albedo. While the BRDF is well known over ocean, it is highly variable over land surfaces.
Thus, over land the majority of ice crystal shape and roughness retrievals based on passive remote sensing techniques focus on optically thicker ice clouds. Moreover, spaceborne observations of ice clouds might also include the ice phase of (deep) convection, e.g., anvils of thunderstorms. Ground-based remote sensing of halo displays focuses on rather thin ice clouds instead with a COT smaller than about 5
Same as Fig.
This study revealed that the overall best-matching ice crystal habits are 8-element and solid columns with an SCF of (
Ice crystal columns and aggregates of columns were also found by
Several other studies found plate-like or compact ice crystals to better represent the observations than columns, for example,
Ice crystal plates of the YG13 database produce a pronounced 46
As shown in
The results of the present study focus on cirrus clouds that produce a visible 22
Our finding that columnar ice crystal shapes best represent the HaloCam observations further implies that the majority of rough ice crystals mixed with a smaller fraction of smooth crystals is sufficient to produce a visible 22
The retrieved effective radii in this study are, to the best of the authors' knowledge, the first observational results for 22 Note that the term “circumscribed halo” in
The retrieved ice crystal size, shape, and surface roughness depend on assumptions about the underlying particle distribution. Although ice crystals in cirrus clouds are more likely described by multimodal size and shape distributions with different degrees of surface roughness and matching ice crystal properties could be found for mixtures of arbitrary complexity, this study aims at finding the simplest ice crystal model with the minimum degrees of freedom that matches the observations within the measurement uncertainty.
We present a novel imaging remote sensing method to retrieve ice crystal optical and microphysical properties, with a special focus on ice crystal roughness and shape.
Using calibrated RGB images of the automated Sun-tracking camera system HaloCam, we exploit the scattering features of the 22 and 46
Long-term observations of ice crystal optical and microphysical properties were performed using HaloCam
It was found that several ice crystal habits and SCFs match the observations within the averaged measurement error in the scattering angle region around the 22
The variation of the retrieved effective radii between the ice crystal habits is much smaller compared to the variation of the SCF and yields an overall mean of about 20
This study highlights the potential and feasibility of a completely automated method to collect and evaluate halo observations.
Long-term calibrated radiance observations of the 22 and 46
These observations contribute to an improved understanding of ice crystal optical and microphysical properties. Implemented on different sites, HaloCam in combination with HaloForest can provide a consistent dataset for climatological studies of ice crystal properties representing optically thin ice clouds, for example, anvil cirrus of deep convection in the tropics or cirrus clouds and diamond dust in high-latitude regions. Representative ice crystal optical properties are required for remote sensing of cirrus clouds as well as climate modeling. To the best of the authors' knowledge, this study presents the first quantitative retrieval for ice crystal shape and surface roughness using ground-based imaging observations of halo displays. Since ground-based observations provide information about the forward portion of the light scattered by ice crystals, the results of this work ideally complement the results of satellite-based studies.
An important choice for creating the lookup tables used in this study is the radiative transfer model. Since cirrus clouds producing visible 22
Comparison between MYSTIC (lines with error bars) and DISORT (solid lines) for the region of the 22
In the following the sensitivity of the retrieval to the retrieved smooth crystal fraction (SCF) is tested for different scenarios using the YG13 model for the ice crystal optical properties. LUTs assuming slightly different atmospheric or ice cloud parameters are matched against synthetic measurements simulated with DISORT.
The tests are performed for the ice crystal habit, AOT, aerosol type, surface albedo, and atmospheric profile. The synthetic measurements were simulated for a wavelength of 500 nm and a solar zenith angle of 45
Sensitivity of the retrieval regarding five different LUT parameters:
First, the retrieval error is estimated by applying the retrieval to simulated test cases using LUTs with slight deviations in the assumed atmospheric condition, e.g., surface albedo, AOT, or aerosol type. In order to investigate the stability of the retrieval for different ice clouds, simulations were performed for a range of COTs and SCFs for one ice crystal habit population.
The retrieval error is evaluated for the difference between the true and retrieved SCFs defined by
Figure
Sensitivity studies as in Fig.
In Fig.
A similar but much less pronounced effect occurs for errors in the assumed aerosol type, demonstrated in Fig.
Radiative transfer simulations performed with
AERONET (version 2, level 1.5) AOT at 500 nm wavelength for the period between September 2015 and December 2016 with an average AOT of 0.19
The last sensitivity study shown in Fig.
Figure
MODIS MCD43A3 white-sky albedo from 19 September 2015 at a wavelength of 555 nm displayed for the geographic region which is covered by the projected 22
Another test was performed for calibrated measurements with an error of the radiometric response of 15 %, which corresponds to the error of HaloCam
These sensitivity studies demonstrate that the largest retrieval errors occur for wrong assumptions about the ice crystal habit and the AOT. Thus, for the compiled LUTs, all available ice crystal habits for the YG13 optical properties are considered. Under the assumption that the optical properties represent the variability of ice crystals in natural cirrus clouds, the retrieval error for the ice crystal habit is negligible. The AOT is varied in the LUT assuming typical values for Munich. For the remaining LUT parameters, i.e., aerosol type, surface albedo, cloud height, and atmospheric profile, “best-guess” fixed values or parameters are chosen. The procedure for how the LUT parameters are selected will be presented in the following sections.
Spectral albedo data from the ASTER library provided with a resolution of 2 nm for grass (blue), shingle (red), conifer (dark green), and deciduous trees (green) as well as concrete (purple). A linear combination for the different ASTER albedo types is determined which represents best the averaged MODIS data from Fig.
Surface albedo between October 2015 and March 2017 for the HaloCam
Depending on the temperature regime of the cirrus and its evolutionary stage, the cloud can contain supercooled water droplets alongside the ice crystals.
Since water droplets cannot form halo displays due to their spherical shape, they have in principle a similar smoothing effect on halo displays to rough ice crystals. Water droplets may therefore not be distinguishable from rough ice crystals by passive ground-based observations in the visible spectral range.
To investigate the effect of supercooled water droplets on the retrieved smooth crystal fraction, synthetic measurements were simulated with DISORT for different mixtures of smooth ice crystal columns and water droplets.
Similarly to the two-habit LUTs, the fraction of water droplets was increased from 0 for a cloud consisting entirely of smooth solid ice crystal columns to 1 for a pure water cloud. The water cloud optical properties were calculated with the Mie tool described in
The sensitivity of the cloud height and thickness as well as the atmospheric profile to the 22
Furthermore, we tested whether it is sufficient to perform radiative transfer simulations for a representative wavelength rather than integrating over the full spectral sensitivity curves of HaloCam
Figure
The sensitivity studies in Appendix
According to the study of
Three percent of the values range in
To constrain the cirrus optical thickness (COT) in the retrieval, we make use of the SSARA Sun photometer, which is located on the institute's rooftop and provides a high temporal resolution of 2 s. After deriving the total optical thickness from SSARA's direct Sun measurements at a wavelength of 500 nm, we subtract the AERONET AOT from the previous clear-sky scene, interpolated to the cirrus time stamp, to obtain the apparent COT.
Due to the enhanced forward scattering in the case of ice crystals, the Sun photometer detects a higher signal within its field of view (FOV) for the same concentration of scattering ice compared to aerosol particles, hence the term “apparent” COT.
This additional forward scattering contribution can be corrected for by using radiative transfer simulations to compute and tabulate the correction factor
Similarly to the procedure presented in
As discussed in
The surface albedo is another parameter which affects the transmission measured at the ground, but its impact on the retrieval is significantly smaller compared to the aerosol type and optical thickness (cf. Fig.
Figure
Averaging over the whole period yields mean albedo values for the red, green, and blue channels of 0.065, 0.063, and 0.050, respectively. The red and green channels show higher values than the blue channel since the surface south of Munich is dominated by green grass and trees. Comparing the red and green channels, a slight difference between winter and summer is noticeable, which is very likely due to the vegetation period. During summer the deciduous trees increase the albedo in the part of the spectrum covered mostly by the green channel, whereas in winter the albedo measured by the green channel is slightly lower than the red channel.
AERONET data for the station “Munich University” are available via
LF prepared the manuscript, developed the retrieval method and measurement strategy, pre-processed and calibrated the HaloCam dataset, compiled the DISORT lookup tables, and analyzed the retrieval results as part of her doctoral thesis. BM secured the funding for the HaloCam project and supervised the doctoral thesis. He provided valuable feedback on the retrieval method and data analysis as well as on the manuscript.
The contact author has declared that neither of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank Markus Rapp (DLR Oberpfaffenhofen) for co-funding the PhD thesis.
We also thank Matthias Wiegner for providing the AERONET Sun photometer measurements and Meinhard Seefeldner, Markus Garhammer, and Anton Lex for their help with maintaining the Sun photometers and HaloCam's Sun-tracking mount. Florian Ewald and Tobias Zinner kindly provided the MIRA-35 cloud radar measurements.
We thank Claudia Emde for implementing
This paper was edited by Odran Sourdeval and reviewed by Ping Yang and one anonymous referee.