Cloud Phase Identification of Low-level Arctic Clouds Cloud Phase Identification of Low-level Arctic Clouds from Airborne Spectral Radiation Measurements: Test of Three Approaches Acpd Cloud Phase Identification of Low-level Arctic Clouds Acpd Cloud Phase Identification of Low-level Arctic Clouds

Atmospheric Chemistry and Physics Discussions This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Abstract Boundary layer clouds were investigated with a complementary set of remote sensing and in situ instruments during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign in March and April 2007. The clouds that formed in a cold air outbreak over the open Greenland sea showed a variety in their thermodynamic 5 state. Beside the predominant mixed-phase clouds pure liquid and ice clouds were observed. Utilizing the measurements of solar radiation reflected by the clouds three methods to retrieve the thermodynamic phase of the cloud were defined and compared. Two ice indices I S and I P were obtained by analyzing the spectral pattern of the cloud top reflectance in the near infrared (1500–1800 nm wavelength) characterized by ice 10 and water absorption. A third ice index I A is based on the different side scattering of spherical liquid water particles and nonspherical ice crystals which was recorded in simultaneous measurements of cloud albedo and reflectance. Radiative transfer simulations showed that I S , I P and I A range between 5 to 80, 0 to 20 and 1 to 1.25, respectively, with lowest values indicating pure liquid water clouds 15 and highest values pure ice clouds. I S and I P were found to be strongly sensitive to the effective diameter of the ice crystals present in the cloud. Therefore the identification of mixed-phase clouds requires a priori knowledge of the ice crystal dimension. I A has the disadvantage that this index is mainly dominated by the uppermost cloud layer (τ<1.5). Typical boundary layer mixed-phase clouds with a liquid cloud top layer will be identified 20 as pure liquid water clouds. All three methods were applied to measurements above a cloud field observed during ASTAR 2007. The comparison with independent in situ microphysical measurements showed a good agreement in identifying the dominant mixed-phase clouds and a pure ice cloud at the edge of the cloud field.

and highest values pure ice clouds.I S and I P were found to be strongly sensitive to the effective diameter of the ice crystals present in the cloud.Therefore the identification of mixed-phase clouds requires a priori knowledge of the ice crystal dimension.I A has the disadvantage that this index is mainly dominated by the uppermost cloud layer (τ<1.5).Typical boundary layer mixed-phase clouds with a liquid cloud top layer will be identified as pure liquid water clouds.All three methods were applied to measurements above a cloud field observed during ASTAR 2007.The comparison with independent in situ microphysical measurements showed a good agreement in identifying the dominant mixed-phase clouds and a pure ice cloud at the edge of the cloud field.

Introduction
The impact of clouds on the radiation budget of Arctic regions constitutes a crucial uncertainty in predicting Arctic climate change as reported in the Arctic Climate Impact Assessment (Corell, 2004).Mostly Arctic clouds are warming the underlying atmosphere (Intrieri et al., 2002).Due to the high albedo of the snow or ice-covered surfaces the longwave radiative heating dominates over the solar cooling and thus determines the cloud radiative forcing in the Arctic.Shupe and Intrieri (2004) showed that low-level clouds are the most important contributors to the Arctic surface radiation budget.Their radiative impact is highly variable and depends on surface albedo, aerosol particles, cloud water content, cloud particle size and cloud thermodynamic phase (Curry et al., 1996;Shupe and Intrieri, 2004).
For instance, a low surface albedo in summer leads to a seasonal cooling effect due to Arctic clouds (Dong and Mace, 2003).Freese and Kottmeier (1998) showed for marine clouds that the low surface albedo of the ice free ocean reduces the upwelling radiation above the overlaying clouds and thus the cloud albedo by up to 30% compared to clouds over highly reflecting sea ice.Cloud radiative properties and cloud life cycle are also influenced by the cloud thermodynamic phase (Sun and Shine, 1994;Harrington et al., 1999;Yoshida and Asano, 2005;Ehrlich et al., 2008b).The simulations by Harrington et al. (1999) showed that the cloud top temperature and the amount of ice nuclei control the conversion of liquid cloud water to solid ice.Cold temperatures and high ice nuclei concentrations lead to higher ice fractions and shorten the life time of the mixed-phase cloud.Furthermore, Yoshida and Asano (2005) showed that an increasing ice fraction results in a significant increase in the absorptance of mixed-phase clouds for the near infrared wavelength range (>700 nm).
Therefore, in situ measurements and/or remote sensing of the thermodynamic cloud phase are of importance.Parameterizations of the dependence of ice volume fraction (ratio of ice to total water content) and cloud temperature were obtained from in situ measurements by Boudala et al. (2004); Korolev et al. (2003).However, due to the Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion limitation in time and space in situ measurements can give a snapshot of the complexity of Arctic clouds only (Lawson et al., 2001;Cober et al., 2001;McFarquhar et al., 2007).Several cloud retrieval algorithms based on satellite data provide the cloud phase.Commonly, before retrieving cloud properties a preselection algorithm distinguishes between ice, mixed-phase and liquid water clouds (Key and Intrieri, 2000;King et al., 2004;Kokhanovsky et al., 2006).This phase discrimination is often based on two methods using the brightness temperatures of thermal infrared (IR; 5-50 µm) channels and the cloud reflectance at channels for solar radiation in the visible (VIS; 300-700 nm) and near infrared wavelength range (NIR; >700 nm).Further methods are based on radar data (CloudSat, Sassen and Wang, 2008) and polarization measurements, for example using data of the POLarization and Directionality of the Earth's Reflectances instrument (POLDER, Buriez et al., 1997).
The contrast of brightness temperatures measured at two wavelengths is related to the ice volume fraction due to the different emissivity of ice and liquid water at wavelengths larger than 10 µm.In the same way the cloud reflectance at NIR wavelengths is affected by the different refractive indices (in particular the imaginary part, i.e. absorption index) of ice and liquid water as demonstrated by Pilewskie and Twomey (1987).Therefore, the ratio of cloud reflectance at two wavelengths was used to determine the cloud thermodynamic phase (band ratio method).Both methods were compared by Chylek et al. (2006) for the Moderate Resolution Imaging Spectroradiometer (MODIS) showing significant discrepancies between the results of the two methods with a tendency of an overprediction of ice phase by the band ratio method.The authors suggest to use the ratio of NIR high resolved spectral bands around 1.5 and 1.4 µm.This was applied successfully by Knap et al. (2002) and Acarreta et al. (2004) for the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY).
In this study we present similar methods of cloud phase identification using airborne spectral solar cloud reflectance measurements combined with radiative transfer simulations.Three approaches to discriminate the cloud phase are applied and discussed.Introduction

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Interactive Discussion
First results for cloud phase identification with the SMART-Albedometer were published in Ehrlich et al. (2008a).The measurements presented here were performed during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) 2007 campaign.Additional information on cloud phase was obtained from in situ cloud microphysical and airborne lidar measurements.
The instrumentation of the aircraft is described in Sect. 2. The measurements of spectral cloud reflectance and three methods to obtain information on the cloud phase are discussed in Sects.3 and 4. Subsequently, the three methods are analyzed by sensitivity studies in Sect. 5. Finally in Sect.6 the application of the methods is examined by a case study of observations from 7 April 2007.

Instrumentation
During ASTAR 2007 two aircraft were employed.We report on data from the Polar 2 aircraft, owned by the Alfred Wegener Institute for Polar and Marine Research (AWI).The airborne instrumentation included the Spectral Modular Airborne Radiation measurement sysTem (SMART-Albedometer), in situ instruments such as the Polar Nephelometer, Cloud Particle Imager (CPI), and Particle Measuring System (PMS) Forward Scattering Spectrometer Probe (FSSP-100), and the Airborne Mobile Aerosol Lidar (AMALi).
The SMART-Albedometer was developed at the Leibniz-Institute for Tropospheric Research as a modular system to measure solar spectral radiation (radiance, irradiance, actinic radiation) from airborne platforms as described e.g. by (Wendisch and  Mayer, 2003; Wendisch et al., 2004; J äkel et al., 2005, Bierwirth et al., 2008  1 ).↑ λ covering the visible (350-950 nm) and near infrared wavelength range (950-2100 nm) with a spectral resolution (full width at half maximum) of 2-3 nm and 9-16 nm.F ↑ λ was measured in the visible part of the spectrum only (350-950 nm).The optical inlets for the irradiance measurements constructed by the Bay Area Environmental Research Institute, CA, USA were designed as integrating spheres made of Spectralon reflectance material (Crowther, 1997).Sealed with a quartz dome the Spectralon integrating sphere provides an almost wavelength independent photon collection efficiency.To measure I ↑ λ a set of new optical inlets for radiance measurements was constructed.The entrance optics of the radiance optical inlet is based on a Zeiss collimator lens (BK 7 glass) with a focal length of 31.6 mm.
The collimator is mounted within a cylindrical housing reducing stray light.Two opening apertures at both ends of the housing define the angle of view.Laboratory measurements and ray tracing simulations found an angle of view of 1.5 • .The outer aperture is covered by BK 7 glass providing vacuum conditions inside the tube and protection against condensation during changes of the external temperature conditions.
The in situ measurements of cloud microphysical properties include particle number size distribution, extinction coefficient, ice and liquid water content, effective diameter, scattering phase function and the asymmetry parameter, a measure for the anisotropy of the scattering phase function.The instruments, data retrieval and measurement uncertainties are described by Gayet et al. (2007).
Additional independent information on the cloud phase was provided by the depolarization measurements of AMALi which is a two-wavelength (532 nm and 355 nm) backscatter lidar with depolarization measurements at 532 nm wavelength.AMALi was installed in nadir looking configuration.The vertical resolution amounts to 7.5 m.The Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion minimum horizontal resolution was around 900 m.Further details of AMALi are described in Stachlewska et al. (2004).

Measurements
During ASTAR 2007 (7-9 April) a cold air outbreak with northerly winds initiated extended boundary layer cloud fields over the open Greenland Sea as shown by the MODIS satellite image in Fig. 1.The convection above the relatively warm open sea maintained the coexistence of ice and liquid water in these clouds.Detailed investigations on the self maintaining dynamics of the mixed-phase clouds are described by Harrington et al. (1999);Fridlind et al. (2007); Morrison et al. (2008).In addition to the predominating mixed-phase clouds, pure ice and pure liquid water clouds were observed during ASTAR 2007 providing the possibility to test cloud phase identification methods.

In situ measurements
In situ observations of the prevailing mixed-phase clouds showed a cloud top layer typically consisting of liquid water with precipitating ice below (Fig. 2).The FSSP indicated particle concentrations up to N tot =50 cm −3 between 1000-1700 m altitude.In the same layers the asymmetry parameter obtained by the Polar Nephelometer, was about 0.85 which is a typical value for spherical liquid water droplets (e.g.Gerber et al., 2000).A narrow ice layer was found between 800 m and 1100 m indicated by lower asymmetry parameters and particle concentrations measured by the CPI up to N tot =1.5 cm −3 .
Below this, precipitating large ice particles were observed down to 500 m.For ice crystals and liquid water particles mean effective diameters of (85±37) µm and (15±5) µm were measured.The cloud optical thickness estimated from the measured extinction coefficients was about 15-20.Introduction

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Airborne lidar measurements
The laser of the AMALi lidar did not penetrate the optically thick clouds completely.However, AMALi did identify a liquid water layer at cloud top from the depolarization signal.Although multiple scattering in the liquid water layer generated high depolarization values comparable to the depolarization signal of ice crystals the detailed analysis of the lidar profiles averaged over 15 s reveals differences in the vertical pattern of the depolarization.The depolarization related to multiple scattering of liquid water particles increases slowly with cloud depth whereas nonspherical ice crystals result in an instantaneous increase of the depolarization (Hu et al., 2007).From this analysis the precipitating ice below the clouds could be identified in several cloud gaps.

Cloud top reflectance
were calculated from the SMART-Albedometer radiance and irradiance measurements.Beside the typical mixedphase clouds also pure ice and pure liquid water clouds could be observed during ASTAR 2007.Cloud top reflectances for all three measured cloud types shown in Fig. 3a reveal differences in the spectral pattern of R λ in the wavelength range 1450-1750 nm.These differences are caused by the contrast in the imaginary part n i of the refractive index (absorption index) of ice and liquid water shown in Fig. 3b.Of all clouds, pure liquid water clouds show the highest R λ values at 1500 nm where the difference of ice and liquid water absorption is maximum.The slope of the reflectance between 1500 nm and 1750 nm is small for liquid water clouds and larger for pure ice clouds.These differences in the spectral pattern of R λ can be used to remotely discriminate the cloud phase.Introduction

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Definition of ice indices: three approaches
To define and evaluate the sensitivity of the ice indices we performed radiative transfer simulations for pre-defined pure ice and pure liquid water boundary layer clouds of various optical thickness (τ=2-20) and effective diameter D eff .For liquid water clouds the effective diameter was chosen between 8 µm and 26 µm corresponding to the range reported by Miles et al. (2000) for marine stratocumulus clouds.The ice clouds were modeled for the range of effective diameter observed during ASTAR 2007 (30-150 µm).
The spectral solar radiative transfer simulations were performed with the libRadtran (Library for Radiative transfer) code by Mayer and Kylling (2005) for the wavelength range 300 nm to 2200 nm adapted to the spectral resolution of the SMART-Albedometer measurements.The discrete ordinate solver DISORT version 2.0 by Stamnes et al. (1988) was applied.The meteorological input (profiles of static air temperature, relative humidity and static air pressure) was composed of a radio sounding at Ny Ålesund/Svalbard (7 April 2004, 11:00 UTC).Corresponding to the observed marine clouds the surface albedo was represented by measurements above sea water obtained during ASTAR 2007.
The stratiform cloud layer was situated between 750 m and 1750 m altitude above the sea surface.The cloud optical properties (extinction coefficient, single scattering albedo and scattering phase function) were calculated from optical properties of the individual cloud particles.Mie-theory was applied for liquid water droplets.For the ice particles column shaped ice crystals were selected.The optical properties of columns were provided by Yang and Liou (1996) based on a combination of methods including an Improved Geometric Optics Method (IGOM) for nonspherical ice crystals.
Results of the radiative transfer simulations for clouds comparable to the observed ice, liquid water and mixed-phase clouds (τ=12) are given in Fig. 4. The simulations of R λ show a similar spectral pattern in the wavelength range 1450-1750 nm compared to the measurements of the three cloud types (Fig. 3a) with the steepest slope observed for the ice cloud.For wavelengths shorter than 1300 nm, R λ differs in the simulations Introduction

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Full because of the different scattering phase functions of ice (nonspherical shape) and liquid water particles (spherical shape).The characteristics of the reflectance spectra were used in the following three approaches to retrieve the cloud phase from the measurements.A common twowavelengths approach and a principle component analysis was applied.A third approach used the combined albedo and reflectance measurements to obtain information on the cloud phase.

Two-wavelengths approach
The spectral slope of the cloud reflectance between 1640 nm and 1700 nm was used to identify the cloud phase with AVIRIS by Knap et al. (2002).The dimensionless ice index defined as I Knap S =(R 1700 nm −R 1640 nm )/R 1640 nm •100 vanishes for pure liquid water clouds and reaches values of up to 30 for pure ice clouds.For SCIAMACHY Acarreta et al. (2004) increased the wavelength range of interest to 1550-1670 nm.The spectral slope was calculated by linear regression excluding the absorption bands of CO 2 .Ice indices calculated this way range between 10 for liquid water clouds and 50 for ice clouds.
The wavelength range used by Acarreta et al. (2004) was limited to 1670 nm due to technical characteristics of SCIAMACHY with changing spectral resolution at 1670 nm.For our measurements with the SMART-Albedometer the definition of ice index I S was extended to the wavelength range 1550-1700 nm, (1) This is the maximum wavelength range where water vapor absorption does not contribute significantly to the measured signal.To reduce the impact of noise from the individual wavelength channels the slope of R λ was calculated by linear regression.Wavelengths affected by CO 2 absorption were excluded from the regression (1560- The ice index I S calculated from the three measured clouds presented in Fig. 3a are given in Table 1.The values range from 8.8 for the liquid water cloud to 57 for the ice cloud with the mixed-phase cloud in-between.These significant differences in I S confirm that the three observed clouds (pure ice, pure liquid water and mixed-phase) can be distinguished with this method.I S calculated from the simulated ice and liquid water clouds is shown in Fig. 5. Typical values for liquid water clouds range between I S =5 and I S =15.Ice clouds show a higher variability of I S with values of up to 80.

Principle component analysis
Principle component analysis (PCA) provides a powerful tool to understand the variations in a multivariate data set (Pearson, 1901).The transformation of the original data into a set of principle components reduces the information given by the multivariate data to a few principle components.Analyzing spectral atmospheric radiation measurements the obtained principle components are correlated with physical processes like molecular scattering, trace gas absorption or aerosol interaction (Rabbette and Pilewskie, 2001).We utilized the PCA to extract the ice and liquid water absorption signature in the spectral cloud top reflectance.
PCA was applied separately on the simulated pure ice and pure liquid water boundary layer clouds introduced above (τ=2/4/6/8/10/12/14/16/18/20).For the ice clouds simulations for D eff =30/60/90/120/150 µm were considered in the PCA providing a set of 50 different clouds.In the PCA of the water cloud simulations D eff =8/10/14/20/26 µm were included.The simulated cloud top reflectance was normalized with R 860 nm to eliminate the impact of cloud optical depth.To focus on the ice and liquid water absorption signature the wavelength range between 1500 nm and 1800 nm was considered for the calculations only.Finally the principle components P C i were calculated by applying the component weightings γ i ,λ obtained form the PCA Introduction

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Full (2) Due to the normalization with R 860 nm the remaining variance of the data will mainly result from the absorption of the particles due to the variation of their effective diameter.
Consequently the calculations showed that the first principle component derived from the pure liquid water cloud simulations is related to liquid water absorption.The contribution of R λ at individual wavelengths to P C W is given by the component weightings γ W shown in Fig. 6.The minimum weight occurs in the wavelengths between 1600 nm and 1700 nm where liquid water absorption is weak as indicated by the imaginary part n i of the refractive index (dashed line).In the same way the first principle component from the pure ice cloud simulations P C I is correlated with ice absorption and has the maximum component weighting γ I at wavelengths around 1550 nm.To utilize P C W and P C I for cloud phase identification we defined a respective ice index I P as, The subtraction of 0.57 was chosen arbitrarily to obtain values close to zero for liquid water clouds.For the observed liquid water cloud presented in Fig. 3a I  cloud particles.Representative scattering phase functions for spherical liquid water particles and ice crystals of column, plate and aggregate shape are illustrated in Fig. 8. Chepfer et al. (2002) used this information to retrieve the ice crystal shape from dual satellite measurements at the wavelength of 650 nm.From the differences in the radiation scattered close to the backscatter angle of 180 • and those scattered into viewing angles between 60 • and 150 • particle phase and shape can be distinguished.A similar retrieval of particle phase and shape was applied by McFarlane et al. (2005) to measurements of the Multiangle Imaging Spectroradiometer (MISR) using the nine different viewing angles of the instrument.By minimizing the differences between measured and simulated reflectances they were able to calculate an ice index.It was shown that the highest differences between droplets and crystals occur between 70 • and 130 • scattering angle.Both studies emphasize that the retrieved properties are representative only for particles near cloud top.
The configuration of the SMART-Albedometer operating under conditions of low Sun allows for a similar retrieval of the cloud phase using simultaneous albedo and nadir reflectance measurements.With the high solar zenith angles (70 • to 85 • ) present during ASTAR 2007, the nadir reflectance measurements correspond to side scattering by the cloud particles with scattering angles of 95 • to 110 • assuming single scattering as being predominant.As indicated by the grey area in Fig. 8 this scattering angles provide substantially enhanced scattering by nonspherical particles compared to spherical particles.This increases the measured upwelling radiance and cloud reflectance which is confirmed by the simulations shown in Fig. 4. The pure ice cloud shows higher R λ at wavelengths up to 1300 nm than the simulations for the pure liquid water cloud.On the other hand, the upwelling irradiance and consequently the albedo α λ =F From this assumptions we suggest the ratio of cloud top reflectance to albedo at 645 nm wavelength β I =R 645 nm /α 645 nm as an indicator of the anisotropy of the radiation field reflected at cloud top.For solar zenith angles larger than 60 • nonspherical particles give a higher β I than spherical particles.Increasing cloud optical depth also increases β I due to multiple scattering processes.
From the simulation of pure liquid water clouds and pure ice clouds presented above β I was calculated and plotted as a function of the corresponding R 645 nm (Fig. 10).Both liquid water and ice clouds show a distinct relation between β I and R 645 nm , with the isotropy of the reflected radiation being significantly higher above ice clouds compared to the liquid water clouds of the same R 645 nm .These differences can be utilized to identify the cloud phase.
Calculating the deviation of measured β I to the theoretical β I values of pure liquid water clouds of the same R 645 nm gives an indicator for the cloud phase.The ice index I A was defined as the ratio The results for the observed pure ice cloud and mixed-phase cloud shown in Fig. 3a are given in Table 1.Both values differ significantly from one.For the pure liquid water cloud it was not possible to calculate I A .This cloud had a small horizontal extension.

Sensitivity studies
The cloud top reflection (especially in the wavelength range used to calculate the ice indices) is affected not only by the cloud thermodynamic phase but also by other cloud optical properties (cloud optical depth and cloud particle effective diameter).In order to reduce their impact on the cloud phase retrieval we applied different normalizations of R λ before calculating the ice indices as shown above.Nevertheless it is impossible to overcome those related uncertainties completely.Acarreta et al. (2004) showed for their ice index similar to I S that the ice index of ice clouds may vary by a factor of up to 3 between clouds of small effective diameter/low cloud optical thickness and clouds of large effective diameter/high cloud optical thickness.Changes in the solar zenith angle were found to be less important for the simulated ice indices.Especially for optically thin clouds the surface properties will have an impact on the ice indices.The surface albedo is crucial for the visible wavelength used to calculate I A while absorption by liquid water, snow or sea ice may affect I S and I P .In order to reduce the complexity in this study we concentrate on the conditions found during ASTAR 2007 with open sea as surface.
In the following we discuss the impact of cloud optical depth and particle effective diameter for the ice indices defined in this paper (Sect.5.1).Section 5.2 presents investigations of the sensitivity of the ice indices on the vertical structure of mixedphase clouds.Most robust with regard to the cloud optical properties of ice crystals is the ice index I A . Figure 11 shows that values for ice and liquid water clouds differ for all simulations.Therefore I A is most suitable for discriminating ice and liquid water clouds in the setting of the present sensitivity study.

Vertical distribution
A second sensitivity study focuses on the ability to identify mixed-phase clouds typically consisting of two layers with liquid water droplets at cloud top and precipitating ice below.Radiative transfer simulations were performed based on the microphysical measurements on 7 April 2007 presented in Sect.3. The cloud optical properties were fixed at τ=15, D eff =15 µm for liquid water particles and D eff =85 µm for ice particles.
The cloud was divided into 10 sublayers with a homogeneous liquid water mode of τ W =1.5 for each layer.One ice layer (τ I =1.5) was added and shifted from cloud top to cloud bottom.For each simulation the ice indices I S , I P and I A were calculated.The results are given in Table 2.
The suggests that these approaches are able to distinguish typical boundary layer mixedphase clouds with a liquid cloud top layer from pure liquid water clouds.The ice index I A deviates from values of pure liquid water clouds only if the ice layer is at cloud top.This suggests that I A is suitable only for pure ice clouds.Typical boundary layer mixed-phase clouds with liquid cloud top will be identified as pure liquid water clouds.This is consistent with the findings of Chepfer et al. (2002) who found that particle shape retrieved from two scattering angles at 650 nm wavelength was insensitive to multilayered clouds when τ of the cloud top layer is larger than 2.

Case study on 7 April 2007
On 7 April 2007 concurrent radiation and microphysical measurements were conducted along the path of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) over the Greenland sea as shown in Fig. 1.A stratus cloud field with cloud top up to 1500 m extended from 77.3 • N) to northern areas at the time of the CALIPSO overpass (10:18 UTC).The profile of total attenuated backscatter signal measured by CALIPSO is shown in Fig. 12a.The lidar could not completely penetrate the optically thick clouds with exception of the cloud edge (<77.4 • N).For the investigated cloud the depolarization measurements (not shown here) were not suitable for a cloud phase analysis.Multiple scattering in the optically thick clouds increased the depolarization regardless of particle shape.Nevertheless the lidar profiles reveal that in the southern part of the cloud deck (see Fig. 1 and left-side of Fig. 12a) ice particles are precipitating down to the surface.These precipitation particles, which are also observed from CloudSat (reflectivity), can be detected by the Lidar because they are not capped by a liquid water layer in this area.This part of the cloud was sampled with in situ microphysical instruments about 1 h before the CALIPSO overpass.Considering the advection of the cloud field with the northerly winds the measurements showed that the cloud edge in the southern part consisted of ice particles only (Fig. 12b, <77.4 • N).The particle concentration mea-Introduction

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Full sured by the CPI increases up to N tot =3 cm −3 .First liquid water particles were observed with the FSSP 3 km further north.After the descent below cloud base (77.5 • N to 77.6 • N) the partly high ice crystal concentrations with simultaneous absence of liquid water particles is related again to precipitating ice below the cloud.Higher cloud layers are probably of mixed-phase as measured during the ascent through the cloud (77.6 • N to 77.7 • N).
Shortly after the CALIPSO overpass the cloud was investigated again by radiation measurements flying above the cloud top.From the measured cloud top reflectance the cloud phase was remotely identified using the ice indices defined above.Figure 12c shows the measured ice indices I S and I P along the flight track of 7 April 2007.Both ice indices show high values around 77.4 • N correlated with the high ice particle concentration measured from the in situ instrumentation 1 h before.The maximum values I S =60 and I P =13 indicate a pure ice cloud.Lower values (I S =20−40 and I P =5−10) corresponding to mixed-phase clouds were measured afterwards when the FSSP measured significant liquid water particle concentrations.With respect to the sensitivity studies of Sect. 5 I S and I P measured above the mixed-phase clouds are higher than expected and close to values of pure ice clouds with small effective diameter.This reveals that either the fraction of ice crystals is much higher than measured by the in situ measurements or the vertical distribution of the ice differs from the assumption of a liquid cloud top layer with high ice concentrations below.
The analysis of the reflectance-albedo ratio β I also reveals the presence of ice at the cloud edge.3-D radiative effects.Nevertheless the measurements above the cloud edge (labeled by red crosses) tend to range in higher values of β I .This shows that at the cloud edge nonspherical ice crystals were present at cloud top.

Conclusions
Three different methods to derive the cloud thermodynamic phase from airborne spectral solar radiation measurements were presented.The ice index I S analyzing the slope of the spectral reflectance and the ice index I P obtained from PCA are capable to identify the cloud phase of low level boundary layer clouds observed during ASTAR 2007.
Within a case study a pure ice cloud at the edge of a mixed-phase cloud field also probed by in situ microphysical probes and observed by CALIPSO showed significant higher values of I S and I P related to ice particles.The mixed-phase clouds inside the cloud field showed lower ice indices compared to the ice cloud but higher values than expected for pure liquid water clouds.The third ice index I A based on the anisotropy of the reflected radiation and defined by the ratio between cloud reflectance and albedo is not able to detect mixed-phase clouds.Simulations showed that I A is mainly affected by the uppermost cloud layers.The optical thickness of the relevant cloud layer was found to be less than τ<1.5.Therefore mixed-phase clouds with liquid cloud top will be identified as pure liquid clouds.Nevertheless for the edge of the cloud field the presence of nonspherical ice crystals was confirmed.
At least for the cloud top layer I A is theoretically a more distinct indicator for the cloud phase than I S and I P .Sensitivity studies showed that both indices I S and I P are strongly dependent on the ice particle effective diameter.Pure ice clouds with small ice crystals result in I S and I P close to values of pure liquid water clouds.Therefore, the discrimination of mixed-phase and ice clouds requires a priori knowledge about the ice crystal dimensions.Cloud optical depth has a minor impact on all three ice indices relevant for clouds with τ<5.The case study showed, that I A is more difficult to interpret compared to I S and I P .The combination of three independent measurements and possible 3-D radiative effects result in a higher uncertainty of this method.From a single measuring point the retrieval of ice phase is not reliable.Cluster analysis or averaging is necessary.
Considering the advantages and uncertainties of all three methods we suggest to rely on a combination of the methods in further studies.Together with airborne lidar and in situ microphysical measurements as presented here further investigations will help to verify algorithms for cloud phase identification from satellites (CALIPSO, CloudSat, MODIS).Especially airborne hyperspectral camera systems resolving the near infrared wavelength range will be capable to investigate the detailed horizontal distribution of ice and liquid water particles.
Screen / EscPrinter-friendly Version Interactive Discussion P =0.6 was calculated.Values for all three observed clouds are given in Table1.The results of the analysis of the simulated liquid water clouds shown in Fig.7reveal typical values of I P =0−2.For ice clouds I P ranges from values of 4 up to 20 clearly capable of being distinguished from liquid water clouds.4.3 Albedo-reflectanceIn general clouds act as non-lambertian reflectors.The radiance field reflected from cloud top is essentially affected by the anisotropic scattering phase function of the scattering angles and is less dependent on the scattering phase function.This is illustrated in Fig.9by comparison of R λ and α λ measured above the mixed-phase and pure ice cloud observed on 7 April 2007.Both clouds had a similar optical thickness of 12.The measurements show, that the difference between R λ and α λ are smaller for the ice cloud compared to the mixed-phase cloud, where liquid water the polynomial fit shown as blue solid line in Fig.10.From the definition of I A it follows that we obtain I A =1 for pure liquid water clouds and I A >1 for pure ice clouds.
Fig. 11 and range between I A =0.98 and I A =1.03.Ice clouds give higher values of I A >1.06 separated distinctly from the results for liquid water clouds.15914 The ice indices I S , I P and I A calculated from the simulations of pure ice and liquid water clouds of different τ and D eff are shown in Figs. 5, 7 and 11.The plots reveal that the ice indices are almost insensitive to D eff and τ for pure liquid water clouds.The values vary only slightly with D eff and τ.I P and I A show almost no variation with τ.On the other hand, the ice indices of the pure ice clouds spread over a wide range.Most significant is the decrease of I S and I P with decreasing D eff .Especially for optical thin clouds I S can reach values of pure liquid water clouds.A slight improvement isgiven by I P .Here non of the ice clouds give values as low as the simulated liquid water clouds.Nevertheless in order to achieve a reliable identification of ice, liquid water and especially mixed-phase clouds from I S and I P a priori knowledge about the ice crystal effective diameter and the cloud optical depth is needed.
results show that all three indices are most sensitive to the upper cloud layer showing the highest values if the ice layer is located at cloud top (τ top W =0).Here τ top W gives the total optical depth of the liquid water layers located above the single ice layer.The maximum values of I S =42, I P =7.8 and I A =1.08 range above typical values for pure liquid water clouds (cf.Figs. 5, 7 and 11).I S and I P decrease slowly with increasing τ top W to values of I S =12, I P =1.6 which reaches the range simulated for pure water clouds.Nevertheless, for τ top W <10 and considering the effective diameter of the water particles (D eff =15 µm) the ice indices I S and I P are higher than for pure liquid water clouds.This Figure 13 shows all measurements taken on 7 April 2007 above clouds.Generally the measured values of β I deviate from the theoretical curve of pure liquid water clouds (1-D simulations) which is not expected for mixed-phase clouds with a thick liquid layer at cloud top (cp.Sect.5.2).The high values of β I indicate the presence of ice crystals at the top of the mixed-phase clouds.It has to be pointed out here that due to the combination of three single measurements (F ↓ λ , F ↑ λ and I ↑ λ ) the uncertainties of the data points are relatively high as marked at two measurements samples in Fig. 13.Furthermore the 1-D-simulations used to define I A do not account for possible

Table 1 .
Ice indices I S , I P and I A calculated for the observed clouds presented in Fig.3a.

Table 2 .
I S , I P and I A of mixed-phase clouds (τ W =13.5, τ I =1.5) for different positions of the ice layer (not all 10 simulations shown here).The position is given by the optical depth τ