Interactive comment on “ Optical thickness and effective radius of Arctic boundary-layer clouds retrieved from airborne spectral and hyperspectral radiance measurements ”

(SMART-Albedometer) and radiance (SMART-Albedometer and AisaEAGLE hyperspectral imaging camera) above Arctic (Norway) boundary-layer clouds. The measured radiances are used to retrieve the cloud optical thickness and droplet effective radius of clouds. Results from measured irradiances are not discussed in the paper. The authors describe a method used to designate common pixels between the nadir radiance measurements of the SMART-Albedometer and the radiance measurements of the AisaEAGLE imaging camera. The authors compare the radiance measurements in the common pixels of the SMART-Albedometer and the AisaEAGLE imaging camera and find good correlation. The authors then investigate two different cloud retrieval approaches based on reflected radiance measurements; a 2-wavelength approach and a 5-wavelength approach. They conclude that while 2-wl is adequate to retrieve cloud optical thickness, the retrieval of droplet effective radius is more sensitive to the retrieval method applied. They summarize that time delays between in situ measurements and the remotely sensed measurements prevents closure about which retrieval method is the best.


Introduction
The Arctic is strongly affected by global warming (Walsh et al., 2011), and clouds play an integral part in the ongoing changes (Wu and Lee, 2012).However, satellite observations are often obstructed by the low contrast between clouds and the surface covered by snow or sea ice (Krijger et al., 2011), and airborne or ground-based cloud observations are scarce, especially over the Arctic Ocean.
The understanding of Arctic clouds is crucial to predict their role in greenhouse warming (McBean et al., 2005).The Arctic heating trend was reported to continue in 2011 (Overland et al., 2011).The relationships between atmospheric conditions and the microphysical characteristics of mixed-phase clouds are key questions of cloud physics (Korolev and Isaac, 2006;de Boer et al., 2010;Seifert et al., 2010).Moreover, the microphysical characteristics of clouds (particle phase, size, concentration and shape) determine their radiative properties and, thus, their impact on the Earth's radiation budget (Curry et al., 1996;Ehrlich et al., 2008a).This is of particular interest in the Arctic by Shupe and Intrieri (2004) from ground-based remote-sensing observations, and climate change is particularly strong.Verification of space-borne retrievals of cloud properties critically depends on independent airborne or ground-based measurements.Problems in retrievals arise from both instrument uncertainty and the assumptions made in the radiative transfer models used for the retrieval algorithms (Brest et al., 1997;Marshak et al., 2006).Thus, credible verification of remotely inferred cloud properties requires (i) direct comparison to in situ measurements; and (ii) comparison of spectral radiances simulated by radiative transfer models to measured quantities (Formenti and Wendisch, 2008;Barker et al., 2011).
For the purpose of improving the data base of the Arctic climate system, the SoRPIC (Solar Radiation and Phase Discrimination of Arctic Clouds) campaign was conducted in the Norwegian Arctic, including one of the first applications of an AisaEAGLE hyperspectral camera for cloud studies.SoRPIC was a collaboration of the Alfred Wegener Institute for Polar and Marine Research (AWI), the University of Leipzig (Germany), the Blaise Pascal University of Clermont-Ferrand (France), the Free University of Berlin (Germany), and the German Aerospace Center DLR.The measurement platform was the Polar-5 research aircraft (C-GAWI) of AWI, Bremerhaven (Germany).This Basler BT67 aircraft is a former DC-3 modernized by Basler Turbo Conversion Oshkosh (Wisconsin, USA) with modern avionics and navigation systems, and turbo prop engines required for the demands of polar research.It was put in service in 2007 (Herber et al., 2008).With an operational range of 1500 km, a maximum altitude of 7.5 km (24 000 feet), 15 kVA of electrical power, and a weight capacity of 2000 kg, the Polar-5 provides a reliable platform for the study of boundary-layer clouds in the Arctic.It is based out of Bremerhaven, Germany, and is presently operated by Kenn Borek Air Ltd., Calgary (Alberta, Canada).
In this manuscript, we follow the convention of defining hyperspectral data as imaging datasets with three dimensions (space, time, and wavelength) with contiguous spectral coverage, in contrast to multispectral where the spectral coverage is not contiguous.Introduction

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Full The term spectral without prefix refers to non-imaging measurements that yield twodimensional datasets (time and wavelength).

Measurements
During the SoRPIC campaign which was held in Svalbard (Arctic Norway) between 30 April and 20 May 2010, a total of 13 research flights were conducted with the Polar-5 aircraft over the Greenland, Norwegian, and Barents Seas.
The aircraft was equipped with a combination of remote-sensing and cloud-particle in situ instruments, listed in Table 1.Parts of the instrumentation are introduced in a recent book by Wendisch and Brenguier (2013).The remote-sensing equipment for this study included one active (the Airborne Mobile Aerosol Lidar AMALi; Stachlewska et al., 2010) and three passive systems (the Spectral Modular Airborne Radiation Measurement System (SMART-Albedometer); AisaEAGLE; Sun photometer).The hyperspectral imaging camera (AisaEAGLE, manufacturer: Specim Spectral Imaging Ltd., Oulu, Finland) was mounted in a tail-pod to measure upwelling radiances I ↑ λ,E (t, y) as a function of wavelength λ, time t, and cross-track distance y (subscript E for AisaEA-GLE).The AisaEAGLE covers the spectral range from 403 nm to 966 nm in 240 channels, with a resolution (full width at half maximum) of 2-3 nm.The cross-track field of view was 40 • wide, divided into 512 spatial pixels (1024 photo diodes with double binning).A dark measurement for correction of the thermal photo-current in the detector was performed every five minutes.An exposure time of 10 ms was used.
During all flights, the upwelling radiance I ↑ λ,S (t, y n ) from the nadir point y n was measured by the SMART-Albedometer (subscript S), which also measured spectral downwelling and upwelling irradiances, F ↓ λ (t) and F ↑ λ (t).The upwelling radiance reflected by the cloud is detected by an optical inlet with a field of view (FOV) of 1.5 • ; the irradiance by integrating spheres (Crowther, 1997).Full 2009) with 1280 channels for wavelengths from 350 to 2100 nm.An exposure time of 500 ms was used.
The viewing geometry for AisaEAGLE and SMART-Albedometer radiances is visualized in Fig. 1.
Both the AisaEAGLE and the SMART-Albedometer have an own Inertial Navigation System (INS) that records the aircraft attitude (roll, pitch, and heading angles).Using these attitude data, the optical inlets of the SMART-Albedometer are actively horizontally stabilised to correct for changes of the aircraft attitude of up to 6 • with an accuracy of 0.2 Wendisch et al., 2001).The AisaEAGLE is fixed to the fuselage, so the real-time attitude angles have to be taken into account in data analysis.
The AisaEAGLE and the SMART-Albedometer were calibrated in the laboratory with a NIST-traceable standard bulb for irradiances and with a NIST-traceable integrating sphere for radiances.The calibration stability during the campaign was monitored with a portable integrating sphere, and was better than 3 %.The radiometric uncertainty is given as 8 % for the AisaEAGLE and 9 % for the SMART-Albedometer radiance.The radiometric calibration of AisaEAGLE has been verified by comparing the upwelling radiances with that of the well-established SMART-Albedometer (compare Ehrlich et al., 2012).Using the INS attitude records, the AisaEAGLE pixels that are located in the field of view of the SMART-Albedometer's radiance sensor are identified for each time step.In this paper, such pixels are referred to as ES (the overlap is shown in Fig. 1).The mean value of all ES pixels is used for comparing AisaEAGLE and SMART-Albedometer radiance data, as in Fig. 2 for a wavelength of 870 nm.The linear correlation coefficient in Fig. 2 is 0.97; the differences can be attributed not only to the measurement uncertainty but also to the different time resolution (1-2 Hz for the SMART-Albedometer, 35 Hz for the AisaEAGLE).
The cloud-top altitude was determined from the backscatter signal of AMALi with an altitude resolution of 7.5 m.The in situ instrumentation includes the Cloud Particle Imager, CPI (Lawson et al., 1998), the Forward Scattering Spectrometer Probe, FSSP-100 (Dye and Baumgardner, 1984), and a polar nephelometer (Gayet et al., 1997).Introduction

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Full On 17 May 2010, a warm front approached Svalbard from Scandinavia, with unusually high temperatures up to 14 • C (Fig. 3).The warm, dry air (50 % relative humidity) was advected onto a ridge of about 0 • C which remained under an inversion at the ocean surface.As observed by AMALi and the drop sondes (Fig. 4), both the inversion and the cloud top increased in altitude toward north.The maximum temperatures ranged from 14 • C at 800 m (74.5 • N) to 8 • C at 1200 m altitude (75.9 • N).A cloud layer formed in the inversion.On the aircraft, we observed that these clouds had a foggy appearance and reached down to the ocean surface.This is supported by the Bjørnøya sounding (50 km west of the southern end of the flight track) that reports continuous saturation up to 975 hPa (Dietzsch, 2010).The Cloud Particle Imager was not operational on this flight.An additional higher cloud layer at 1500 m was observed to the north of the warm airmass, and is excluded from the following discussions.
The aircraft flew at 3100 m altitude almost parallel to the temperature gradient (Fig. 3).Six drop sondes were launched; their data can be trusted only below 2900 m after their adjustment to ambient conditions.AMALi could detect only the cloud top due to saturation.

Retrieval of cloud properties from spectral radiance
The cloud properties are retrieved from the spectral and hyperspectral radiance measurements aboard the Polar 5 aircraft.The measured data are checked against lookup tables (LUT) of simulated radiances.These look-up tables are produced with the radiative transfer package libRadtran (Mayer and Kylling, 2005) spectral upward radiance in dependence on optical thickness τ and droplet effective radius r eff of a plane-parallel cloud, from which the most likely combination is obtained by interpolation of the measured radiance into the simulated radiance grid.Two approaches have been followed to retrieve the cloud optical properties (optical thickness, effective radius) from spectral nadir radiance measurements by the SMART-Albedometer.First, the two-wavelength approach (2WL) presented by Nakajima and King (1990) is used with the radiance at 515 nm and 1625 nm.The grid of pre-calculated radiances I LUT is interpolated to the actual measured radiance I meas at these wavelengths.Second, a five-wavelength (5WL) residual approach presented by Coddington et al. ( 2010) is followed.The same look-up tables are used, but analysed at five wavelengths of 515, 745, 870, 1015, 1240, and 1625 nm in terms of the residuum ζ 2 : where the index i runs over the five wavelengths in increasing order.The weighting factors in Eq. ( 1) reflect the wavelength dependence of the radiance sensitivity to optical thickness (left term) and effective radius (right term).Each of the five wavelengths represents a different order of magnitude of the bulk absorption coefficient of liquid water and, adding the dependence on droplet size, different ranges of single-scattering albedo (Coddington et al., 2010, Fig. 2).For any given flight geometry, ζ 2 is calculated for all elements of the corresponding look-up table, and the minimum indicates the most likely values of r eff and τ.In order to propagate the reflectance measurement uncertainty into the retrieved quantity, both retrievals are repeated for the upper and the lower end of the radiance uncertainty range.
The retrieval results are shown in Fig. 5 for optical thickness τ S and in Fig. 6 for the effective radius r S eff .In the case of optical thickness, the uncertainty (propagated from the reflectance measurement into τ S space) behaves similarly for both retrieval approaches.As the difference between the two retrieval approaches lies within that Introduction

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Full uncertainty, we conclude that either approach can be used to retrieve the optical thickness, and the inclusion of additional wavelengths does not provide additional information.
The retrieval of r S eff is more differentiated.The uncertainties for both approaches differ significantly, with 5WL yielding lower uncertainties (less than 1 µm) than 2WL (1-2 µm).
With values between 0 and 4 µm, the difference in r S eff between 5WL and 2WL is close to and sometimes exceeds the retrievals' uncertainties.The additional wavelengths increase the retrieval sensitivity of the effective radius.The FSSP data (shown as red line in Fig. 6) are close to the retrieved value in the southern section of the flight, and deviates more strongly from the retrieval starting at and north of 75 • N.There are two reasons for this: The time difference between in situ observations and remote sensing is smaller in the south, where the aircraft descended from the latter to the former; and south of 75 • N, the in situ observations occurred near the cloud top, while at more northern latitudes the cloud top grew higher and the in situ observations came from a location deeper inside the cloud.
The retrieved r S eff for 2WL and 5WL are compiled in Fig. 7 in form of two histograms, one for the flight section north of 75 • N and one for the section south of 75 • N. It shows that 2WL and 5WL yield similar distributions of r eff for the northern section, and both deviate equally from the in situ observations that occurred deeper in the cloud.On the other hand, 2WL and 5WL do not agree for the southern flight section, with the distribution from the FSSP observations near cloud top lie between the both.The 5WL retrieval reports a larger amount of large droplets, while 2WL prefers lower values.Judging by the lower retrieval uncertainty of 5WL, the larger values would seem more realistic; however, the deviation from the in situ distribution is beyond the FSSP uncertainty of 1µm.Therefore it is impossible to validate neither 2WL nor 5WL with independent measurements, with the time delay between remote sensing and in situ observations being the most likely source of uncertainty.

AMTD Introduction Conclusions References
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Full The retrieval of the cloud properties from the hyperspectral radiances uses the same principles as for the spectral radiance.The benefit of the hyperspectral data is that they include off-track pixels, adding another dimension not only to the measured data but also to the look-up tables (viewing angle).However, the AisaEAGLE does not cover wavelengths longer than 1000 nm.Therefore, the effective radius cannot be retrieved for the off-track pixels of the AisaEAGLE because wavelengths shorter than 1000 nm are sensitive to the optical thickness only.In the following, the cloud optical thickness τ E is retrieved from the 870 nm radiances for each pixel in the field of view of the AisaEA-GLE.For this purpose, the retrieval grid by Nakajima and King (1990) was constrained to a fixed value of the effective radius, r fix eff .This value is taken from other measurements on the same flight.As there are several options to choose the constraint of the effective radius, we first tested the sensitivity of the retrieval to the various available values.

Influence of effective radius
As we have no information about the effective radius in the off-track pixels, we make a basic assumption in the choice of the fixed value r fix eff : the cloud-particle statistics in this stratiform cloud layer is the same in x (flight) and y direction.Our first option for the effective radius is a moving average of the retrieval from the SMART-Albedometer, r fix eff (t) = r (1) eff := r S eff (t − ∆t, t + ∆t) .Here, the averaging period ∆t is obtained as follows: First, the width d of the observed strip of cloud is determined from the height h between cloud top and the aircraft (from lidar) and the AisaEAGLE's field of view (40 • ).Then the nadir effective radii r S eff (t) from the SMART-Albedometer are averaged over the time in which the radiance spot on the cloud top covered a distance d that is determined from the aircraft speed as (2) Introduction

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Full with α S being the viewing angle of the radiance inlet and v the aircraft speed (ignoring any cloud motion with the assumption v v cloud ).The last term in Eq. ( 2) adds the radiance field of view behind and in front of the nadir point.Hence, the averaging time interval is The other options for the choice of the effective radius include instantaneous and averaged values from remote sensing (S) or from in situ observations (index i).The complete list is this: 1.The moving average in nadir, r (1) := r S eff (t); 3. the mean effective radius of the entire flight leg, r eff := r S eff (t) ∀ t; 4. the value measured by in situ instruments at the same location x, r , where x i is the aircraft location during the in situ measurements and x S during the remote-sensing leg; 5. the mean value of all in situ measurements, r (5 All five options for r fix eff are used as a constraint to derive the optical thickness for all offtrack AisaEAGLE pixels.This results in a spread of the optical thickness τ ES in the ES pixels of 0.3-0.4 units of optical thickness, which is less than the retrieval uncertainty. The histograms of the entire field of optical thickness retrieved with the five different constraints of effective radius (Fig. 8) shows that the choice matters only when the optical thickness is less than 8.One choice, r those low optical thicknesses.This is because of the large time delay between remote sensing and in situ observations.Thin cloud parts are more likely to be changed by weak turbulence than thick stretches of cloud, rendering the local properties detected in situ less representative for the remote sensing at the same location but one hour earlier.We conclude that r (4) eff is a poor choice, while the other options yield very similar results in the retrieval of the optical thickness.

Influence of atmospheric profile
The variation of the vertical structure of the atmosphere along the flight track has been recorded by a series of drop sondes.The major change occurred in the height of the cloud top, which increased from 200 m at the southern end of the flight track to 700 m at the northern end.In order to illustrate the potential influence of the atmospheric profile, the look-up tables for the 2WL retrieval were computed for four drop sonde profiles.
Between one drop sonde and the next (time interval: 15 min), two sets of τ ES differ by up to 0.2 (0.4 in peaks).The difference is greater when a more distant drop sonde is used; the two retrievals that assume the first and the last drop sonde, respectively, differ by up to one unit of optical thickness.In the following, however, we interpolate between look-up tables created for the different drop sondes in order to mitigate this source of uncertainty.

Retrieval results
The hyperspectral data from the AisaEAGLE produce a map of the retrieved cloud The following statistics of the cloud properties for 17 May are based on the retrieval that uses the current effective radius r (2) eff (t) from the SMART-Albedometer.The look-up tables were calculated for a range of values of solar zenith angle, cloud-top height, and meteorological profiles, and were then interpolated to the current conditions of each measurement point.The resulting look-up table contained radiances only as a function of viewing angle and cloud optical thickness.The viewing angle is fixed for each pixel of the hyperspectral camera, and modified by the aircraft roll angle.So for each pixel, the radiance is a function of τ, and the radiance measured by this pixel yields the retrieved τ value at this point.On the flight on 17 May, a total of 4×10 7 values were retrieved; see the histogram in Fig. 11.The histograms for the entire field of view and for the ES pixels do not differ significantly, which justifies the assumption that the cloud statistics are the same in flight direction and across (on the scale of the field of view).It can roughly be compared to the nadir values retrieved from the SMART-Albedometer radiance: While the AisaEAGLE has about 20 times more data points, the shape of the distribution is the same as for the nadir optical thickness of the SMART-Albedometer, with the exception of the bins for τ = 6-10 that are more pronounced.Only the ES pixels can be directly compared to the simultaneous retrieval by the SMART-Albedometer in nadir (Fig. 12).As these mostly agree within the retrieval uncertainty, the differences in the histogram have to originate in slight off-track deviations in the horizontal distribution of the optical thickness, which nadir observations alone would not observe.

Conclusions
Both a two-wavelength (2WL) and a five-wavelength (5WL) approach have been applied to retrieve the cloud optical thickness and effective radius from the nadir radiances of the SMART-Albedometer.While the two approaches agree within uncertainty for the optical thickness, they differ in respect to effective radius: 5WL seems to be more sensitive to larger droplets (more than 10 µm radius) than 2WL.However, even with comprehensive instrumentation during the SoRPIC campaign that includes state-of-the-art

AMTD Introduction
Full remote sensing and in situ instrumentation, it is impossible to give a definite answer to the question which of the two methods yields better results.The fundamental limitation is the time delay which cannot be avoided when remote sensing and in situ observations are performed on the same platform, but also the vertical position within the cloud.Only with simultaneous collocated measurements above and inside the cloud (with two aircraft) can this limitation be overcome and can closure between the different methods be attempted.
Hyperspectral imaging was used to retrieve the cloud optical thickness in a 40 • field of view across the flight track.This extends the application of the hyperspectral camera AisaEAGLE to airborne cloud research, and shows the potential of this rapidly developing technology to this purpose.We found that the distribution of the cloud optical thickness derived from the hyperspectral data are not entirely equal to those derived from the nadir radiance.Hyperspectral observations are therefore a tool to improve cloud statistics in remote sensing.With our current instrumentation, the hyperspectral retrieval is limited to the cloud optical thickness, because the AisaEAGLE does not cover the wavelengths required for a retrieval of the effective radius.However, compatible hyperspectral imagers for those wavelengths already exist and could be successfully applied to retrieve the effective radius additionally.Introduction

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Full where boundary-layer clouds greatly influence the surface radiation budget, as shown Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . The radiative transfer calculations are initialised with environmental parameters from the SoRPIC campaign, including Sun/aircraft geometry, aerosol optical thickness from the Sun photometer, cloud-top height from the AMALi lidar, and meteorological profiles from drop sondes released from the aircraft.The look-up tables then contain the possible values for the Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | optical thickness.An example is shown in Fig. 10.This map has been drawn such that the aircraft nadir (marked with the red line) is always in the centre.The wavy edge of the map represents the rolling of the aircraft in flight.The ES pixels that are observed by the SMART-Albedometer radiance surround the nadir point and are delineated by the blue lines.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 2 .
Fig. 1.Viewing geometry for radiances from the Polar-5 aircraft with the hyperspectral camera AisaEAGLE (black) and the SMART-Albedometer (blue).Typical dimensions in this study: distance aircraft-cloud h = 2600 m, aircraft speed v = 70 m s −1 , width of one AisaEAGLE pixel y E = 3.5 m, length x E = y E + v • t E = 4.2 m with the exposure time t E = 10 ms, width of instantaneous SMART-Albedometer field of view y S = 68 m, length due to exposure time t S = 0.5 s: x S = y S + v • t S = 103 m.