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  • View in gallery

    Cloud attenuated backscatter coefficient (solid line) and linear depolarization ratio (broken line) for three different cloud types: (a) water drops (2 Jul); (b) hexagonal columns, bullets, and rosettes (typical ice cloud; 4 Jul) [Δ(z) is not calculated where the signal becomes too small]; and (c) oriented hexagonal plate crystals (4.15–4.4 km; 8 Jul). Layers of type (a) and (b) are also present in (c).

  • View in gallery

    (a), (d), (g), (j) Time–height attenuated backscatter coefficient; (b), (e), (h), (k) temperature and humidity profiles (0900 LT, black; 2100 LT, red); and (c), (f), (i), (l) time–height linear depolarization ratio, for observational periods on 20, 25, 27, and 28 Jun, respectively.

  • View in gallery

    Same as Fig. 2 but for 1, 2, 4, and 8 Jul, respectively.

  • View in gallery

    Values of (a) emittance ϵa, (b) integrated attenuated backscatter coefficient γ′(π), (c) linear depolarization ratio Δ, and (d) midcloud temperature, all plotted against time, and (e) γ′(π) plotted against ϵa for 20 Jun.

  • View in gallery

    Same as Fig. 4 but for 27 Jun.

  • View in gallery

    Same as Fig. 4 but for 2 Jul.

  • View in gallery

    Same as Fig. 4 but for 4 Jul.

  • View in gallery

    Histogram plots of the distribution of emittance ϵa for three ranges of midcloud temperature.

  • View in gallery

    Plots of mean values of IR emittance ϵa for the temperature intervals of Fig. 8 vs midcloud temperature as compared with previous cirrus experiments at other latitudes.

  • View in gallery

    Same as Fig. 9 but for IR absorption coefficient σa.

  • View in gallery

    Plots of γ′(π) vs ϵa for four temperature intervals: (a) −25° to −20°C, (b) −20° to −10°C, (c) −10° to 0°C, and (d) 0° to 5°C.

  • View in gallery

    Same as Fig. 9 but for the backscatter-to-extinction ratio k/2η.

  • View in gallery

    (a) Plot of integrated linear depolarization ratio Δ vs IR emittance ϵa for various water clouds; (b) plots of the profile of linear depolarization ratio Δ(z) vs cloud penetration for σa ≈ 13 km−1 and telescope angular apertures of 2 and 5 mrad.

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Optical Properties and Phase of Some Midlatitude, Midlevel Clouds in ECLIPS

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  • 1 CSIRO Atmospheric Research, Aspendale, Victoria, Australia
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Abstract

Several cloud optical quantities were measured for the first time in midlevel, mixed-phase clouds. These included cloud infrared emittance and absorption coefficient (10–12 μm), effective backscatter-to-extinction ratio, and lidar depolarization ratio. Contrary to expectations, the supercooled water clouds were not always optically thick and therefore had measurable infrared absorption coefficients. At times, the water clouds had quite low emittances, whereas ice clouds had emittances that sometimes approached unity. On average, the cloud emittances were greater than those measured previously at lower temperatures in cirrus, but with considerable variability. At higher temperatures, the emittance values were skewed toward unity. The infrared absorption coefficients, for the semitransparent cases, showed a similar trend. The effective isotropic backscatter-to-extinction ratio was also measured. When separated into temperature intervals, the ratio was surprisingly constant, with mean values lying between 0.42 and 0.43, but with considerable variation. These ratios were most variable (0.15–0.8) in the −20° to −10°C temperature range where various ice crystal habits can occur. When multiple scattering effects were allowed for, values of backscatter-to-extinction ratio in the supercooled water clouds agreed well with theory. Multiple scattering factors based on previously obtained theoretical values were used and, thus, validated.

Characteristic and well-known patterns of lidar backscatter coefficient and depolarization ratio were used to separate out the incidence of supercooled water and ice layers and to identify layers of horizontal planar hexagonal crystals. This approach allowed the most detailed examination yet of such incidence by ground-based remote sensing. Water was detected for 92% of the time for the temperature interval of −5° to 0°C. Between −20° and −5°C, percentages varied between 33% and 56%, dropping to 21% between −25° and −20°C and to zero below −25°C. Oriented hexagonal plate crystals were present for 20% of the total time in ice layers between −20° and −10°C, the region of their maximum growth. The depolarization ratio varied significantly among different ice fall streaks, indicating considerable variation in ice crystal habit. Although the dependence of depolarization ratio on optical depth had been predicted theoretically, the first experimental validation in terms of IR emittance was obtained in this study.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado.

* Corresponding author address: Dr. Stuart A. Young, CSIRO Atmospheric Research, PMB 1, Aspendale, VIC 3195, Australia.

stuart.young@dar.csiro.au

Abstract

Several cloud optical quantities were measured for the first time in midlevel, mixed-phase clouds. These included cloud infrared emittance and absorption coefficient (10–12 μm), effective backscatter-to-extinction ratio, and lidar depolarization ratio. Contrary to expectations, the supercooled water clouds were not always optically thick and therefore had measurable infrared absorption coefficients. At times, the water clouds had quite low emittances, whereas ice clouds had emittances that sometimes approached unity. On average, the cloud emittances were greater than those measured previously at lower temperatures in cirrus, but with considerable variability. At higher temperatures, the emittance values were skewed toward unity. The infrared absorption coefficients, for the semitransparent cases, showed a similar trend. The effective isotropic backscatter-to-extinction ratio was also measured. When separated into temperature intervals, the ratio was surprisingly constant, with mean values lying between 0.42 and 0.43, but with considerable variation. These ratios were most variable (0.15–0.8) in the −20° to −10°C temperature range where various ice crystal habits can occur. When multiple scattering effects were allowed for, values of backscatter-to-extinction ratio in the supercooled water clouds agreed well with theory. Multiple scattering factors based on previously obtained theoretical values were used and, thus, validated.

Characteristic and well-known patterns of lidar backscatter coefficient and depolarization ratio were used to separate out the incidence of supercooled water and ice layers and to identify layers of horizontal planar hexagonal crystals. This approach allowed the most detailed examination yet of such incidence by ground-based remote sensing. Water was detected for 92% of the time for the temperature interval of −5° to 0°C. Between −20° and −5°C, percentages varied between 33% and 56%, dropping to 21% between −25° and −20°C and to zero below −25°C. Oriented hexagonal plate crystals were present for 20% of the total time in ice layers between −20° and −10°C, the region of their maximum growth. The depolarization ratio varied significantly among different ice fall streaks, indicating considerable variation in ice crystal habit. Although the dependence of depolarization ratio on optical depth had been predicted theoretically, the first experimental validation in terms of IR emittance was obtained in this study.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado.

* Corresponding author address: Dr. Stuart A. Young, CSIRO Atmospheric Research, PMB 1, Aspendale, VIC 3195, Australia.

stuart.young@dar.csiro.au

Introduction

The effects of clouds on the earth’s radiation budget are still little known and understood only approximately. For example, Le Treut et al. (1994) show that two different cloud parameterizations for midlevel clouds give cloud solar and infrared radiative forcings that both differ by up to 20 W m−2 in some regions. In a survey of GCMs, Browning (1994) listed cloud cover and optical properties as among the highest stated priorities for GCM development. Basically, cloud parameterizations are still too simple to include all the complexities of cloud structure, formation, evolution, and microphysics. One method of better understanding these cloud processes is to have an extended program of cloud observations, both from satellite and surface sensors.

The Experimental Cloud Lidar Pilot Study (ECLIPS) was initiated to determine how effective systematic, long-term, ground-based lidar observations would be in providing useful information on cloud properties. Two 30-day experimental periods were selected in 1989 and 1991 in midsummer and midwinter periods, respectively. A number of laboratories studied clouds using lidar and some auxiliary equipment (Platt et al. 1994). The recommended auxiliary equipment consisted of solar and infrared flux radiometers, narrowbeam spectral infrared radiometers, video cameras, and all-sky cameras. Some laboratories published comprehensive results, illustrating the utility of long-term surface observations. For example, Carswell et al. (1995) and Winker and Vaughan (1994) presented statistics on cloud height and depth. Pal et al. (1995), Sassen and Cho (1992), and Del Guasta et al. (1993) presented comprehensive results on cloud optical depth and extinction.

The lidar–radiometer (LIRAD) method has been shown, from extensive observations on cirrus clouds, to provide detailed information on the IR emittance of clouds (e.g., Platt et al. 1987). Therefore, a passive spectral narrowbeam infrared radiometer was included in the ECLIPS experiment at the Commonwealth Scientific and Industrial Research Organization (CSIRO) Division of Atmospheric Research. The ECLIPS experiment at Aspendale would then concentrate on obtaining the IR emittance of the clouds, as well as other lidar quantities.

The midsummer ECLIPS I observations were made in November 1989, and midwinter ECLIPS II in June–July 1991. Both phases included observations on boundary layer, midlevel, and cirrus clouds. During ECLIPS II, the weather was unusually disturbed, giving a number of opportunities to observe midlevel, mixed-phase clouds. As results on cloud height statistics and visible optical depth had already been presented by other authors as described above, it was decided to concentrate the Aspendale analysis on midlevel clouds observed in ECLIPS II. Other results from ECLIPS will be reported elsewhere.

Very little information was previously available on the optical properties of midlevel clouds. Platt and Gambling (1971) and Platt and Bartusek (1974) measured the infrared emittance and lidar backscatter properties of a number of midlevel layer and cellular clouds. Heymsfield et al. (1991) studied the microphysical and optical properties of highly supercooled altocumulus. Heymsfield and Miloshevich (1993) studied supercooled water and ice nucleation in wave clouds. Platt (1977) also studied a mixed-phase altostratus cloud in detail, noting the layered structure of the cloud. Platt et al. (1978) observed lidar backscatter effects of horizontally oriented plate crystals from midlevel clouds. Carswell et al. (1995) include some information on the optical depths of midlevel clouds.

This paper presents a number of cases of midlevel clouds observed in ECLIPS II. The LIRAD analysis applied to such clouds is described briefly. New results on IR emittance and absorption coefficient of the clouds, lidar integrated attenuated backscatter, and backscatter phase function are presented. They indicate the efficacy of the method to obtain detailed long-term data on midlevel clouds. Further information is obtained on cloud phase from lidar linear depolarization methods. It is now well known that information on cloud phase can be obtained from the lidar linear depolarization ratio and backscatter and their characteristic behaviors with increasing optical depths. The percentages of ice and supercooled water at various temperatures are also presented. Areas of horizontally oriented hexagonal plate ice crystals are also identified by well-established lidar depolarization and backscatter methods.

Definitions and equations—LIRAD technique

The lidar–radiometer technique has been employed for a number of years to retrieve cloud optical properties from simultaneous visible lidar and infrared narrowbeam radiometry (e.g., Platt and Bartusek 1974, hereinafter P1; Platt 1979, hereinafter P2; Platt et al. 1987, hereinafter P3; Platt et al. 1998, hereinafter P4). The technique has been applied mainly to cirrus clouds; here it is adapted to midlevel clouds. The definitions and equations have been covered fully in the above papers, among others.

The equations for all clouds tend to be similar, with a few modifications. The equation relating the integrated attenuated backscatter γ′(π) to the infrared absorption emittance ϵa is given by (P1–P4)
i1520-0450-39-2-135-e1
where k is the isotropic backscatter-to-extinction ratio at the lidar visible wavelength, η is a multiple scattering factor (assumed here to be constant with cloud depth), and α is the ratio of the visible extinction coefficient σc to the IR absorption coefficient σa. Definitions involving the depolarization ratio are given in section 5d. If η is not constant with depth, then the term k/2η in (1) becomes k/2η, where η is an effective multiple scattering factor for the cloud. The value of η within the brackets then has a value related to the cloud level being studied. The value of 2αη is not studied here.
The calculation of the infrared absorption emittance ϵa is described fully in P1–P4. A modification in the current study is necessary for midlevel and low clouds. In such cases, appreciable water vapor can be found above cloud top, whereas in the case of cirrus clouds, a good approximation is to assume that all water vapor resides below the cloud. The total radiance Ls at the ground is then
LsLskyLcτcgLacτcgτc
where Lsky is the radiance from gases (mainly water vapor) between the cloud and the ground, Lc is the cloud radiance, Lac is the water vapor radiance above the cloud, τcg is the transmittance from the cloud to the ground, and τc is the transmittance of the cloud. In previous studies on cirrus the third term in (2) was assumed to be zero. The cloud radiance Lc has three components:
LcLaLscLr
where La is the emitted component, Lsc is due to scattering within the cloud, and Lr is due to reflection of upwelling ground radiation. The experimental application of the LIRAD technique to midlevel clouds is described in the following sections.

Observations

ECLIPS II at Aspendale

Phase II of the Experimental Cloud Lidar Pilot Study was conducted at Aspendale (38.0°S, 145.1°E) during 13 June to 12 July 1991. This period, during the Southern Hemisphere winter, was chosen to allow comparisons to be made with the summer observations of ECLIPS I, held during November and December 1989 at the same location.

In order to assess the horizontal extent of the clouds being studied, observations usually covered a period extending one hour either side of the afternoon overpass of the National Oceanic and Atmospheric Administration satellite’s (NOAA-11) Advanced Very High Resolution Radiometer, which gave images of the cloud extent at Aspendale. Occasionally there were two daytime overpasses of the satellite, and the ground-based observations were extended, whereas observations were curtailed during periods of rain or when the sky became clear of clouds.

A variety of clouds were encountered during the study, although we will concentrate on midlevel clouds as well as low-level clouds with temperatures around 0°C. The dates and times of the clouds analyzed in our study are listed in Table 1.

Equipment

The main equipment used included the CSIRO Mark II infrared beam radiometer and the CSIRO dual-polarization lidar. These instruments were pointed in the vertical direction with their axes separated by less than 2 m. Ancillary equipment comprised an all-sky camera for cloud amount and Eppley diffuse and global pyranometers.

Aerological pressure and temperature data were obtained from radiosonde flights launched at 1100 and 2300 UTC from Laverton, 35 km northwest of the lidar site. Atmospheric density profiles calculated from these data were used in the calibration of the lidar and in the retrieval of cloud backscatter profiles. Temperature and humidity profiles were used to retrieve infrared emittances. The prevailing winds in winter are from the northwest, particularly for the disturbed periods studied. Thus the radiosonde data were considered to be representative.

The CSIRO lidar was a combination of original components and components that were to be used in a dual-polarization, multiwavelength, scanning lidar then under development. The receiving telescope was the same as that used in the original CSIRO lidar (Allen and Platt 1977) and in ECLIPS I. The ruby laser was replaced with a Nd:YAG laser operating in the second harmonic at 532 nm and an improved system for measuring the depolarization ratio. A lidar profile was measured every 30 s as in ECLIPS I.

In order to allow simultaneous measurements of the polarized components of the backscattered radiation aligned parallel and perpendicular to the transmitted laser pulse, two photomultiplier detectors were mounted on perpendicular faces of an interim polarizing beamsplitter. The beamsplitter was constructed using a single plate of glass set at 45° to the optical axis and disks of sheet polarizer with their transmission axes aligned so as to transmit one or the other of the two polarization components. The beamsplitter was calibrated and found to pass (after reflection from the glass plate and transmission through the disk polarizer) 8.6% of the strong parallel component to one detector while rejecting 99.997% of the unwanted perpendicular component. The other detector received 48% of the weaker perpendicular component (which was transmitted through the beamsplitter) while 99.8% of the unwanted component was rejected. Satisfactory separation of these polarization components was thus achieved with this simple beamsplitter. This was a considerable improvement on the original LIRAD system, where the perpendicular polarization component was recorded 1 s after the parallel component using the same detector, but with the receiver polarizer rotated appropriately.

The CSIRO Mark II radiometer measured downwelling radiance continuously. The design of the Mark I radiometer was described originally by Platt (1971) and used by Platt and Gambling (1971) and P1. The incoming signal is chopped against a grooved blackbody maintained at 40°C that fills half of the radiometer aperture. This has been found to give a very stable configuration, where radiance from the chopper blades does not contribute to the alternating signal. The Mark II radiometer, a more compact version of the Mark I, was used in later aircraft and ground-based experiments (e.g., Paltridge and Platt 1981; Platt and Dilley 1981; P3, P4). The details of both the Mark II radiometer and the lidar are summarized in Table 2. The low minimum detectable radiance ensures that good signal-to-noise ratios are obtained on both cloudless water vapor radiances and midlevel clouds.

The uncertainty in IR emittance in terms of a maximum error in a single measurement is estimated to be about 0.03–0.05. Thus emittances in the 0.1–1.0 range can be determined with good confidence. Random and systematic errors in the measured radiance and emittance are discussed in the appendix.

Calibration methods

Lidar

In order to obtain quantitative results on lidar backscatter and depolarization ratio, the lidar was calibrated very carefully. In order to obtain total backscatter, the two orthogonal polarization components were combined to produce a composite signal. As the two components followed different optical paths and were detected using different photomultiplier tubes (PMTs) operating at various gains, the relative calibration of the two signals was critical in producing reliable results. This involved a combination of measurements, and modeling where accurate measurements were not feasible. The resulting, calculated, systematic uncertainty in the depolarization ratio decreases from about 13% for a depolarization ratio of 0.05 to about 4% for a ratio of 0.50.

The calibration methods applied to the lidar data were similar to those described by Young (1995); independent calibration of each profile was not possible, however, because the very strong scattering by low clouds often required measurements using low gain values, causing the digitized molecular signal to be small. A reference signal was calculated from aerological data obtained from the radiosonde flight that was launched closest in time to the lidar observations. Representative lidar profiles from each case (those having sufficient sensitivity to give a measurable molecular signal) were fitted to the reference profile over some height region in which prior inspection of the lidar signal suggested that aerosol scattering was negligible. This region was chosen between the top of any obvious, aerosol-laden, mixed layer and the base of the clouds. The region was also chosen to cover as large a height range as possible in order to provide a good fit with low uncertainties. This process provided values of the lidar system constant for each file, as reduced by attenuation between the lidar and the top of the calibration region, and also the varying signal offset voltage. The standard errors in the offset term and the calibration factor were also calculated and were used to estimate uncertainties in the lidar calibration. During the experiment, the calibration uncertainty from this random source varied between 1% and 4%. (The systematic error involved in incorrectly calibrating in a region containing aerosols is estimated to be less than 5%.) The lidar backscatter coefficient and backscatter-to-extinction ratio can thus be measured with a good accuracy. The raw lidar signal was then corrected for signal offset and scaled to produce a profile of attenuated backscatter B′(π, z):
Bπ, zBmzBczτ2mzbzτ2czbz
where Bm is the molecular (Rayleigh) backscatter coefficient, Bc is the cloud backscatter coefficient, τm is the molecular transmittance, τc is the cloud transmittance, and zb is the cloud-base altitude. The profiles thus produced were then used as inputs to the LIRAD technique described in section 2.

Cloud-base and -top height were determined using threshold methods described by Young (1995). Cloud heights are not listed here, but are apparent in the time–height images of section 6.

Radiometer

The radiometer was calibrated against an external blackbody source at the start and end of every afternoon run. The source consisted of a blackened cone immersed in liquid nitrogen. A separate calibration done prior to ECLIPS II ensured that a null signal was obtained when an external blackbody with a controlled temperature was at the same temperature of 40°C as the reference blackbody. Any imbalance was corrected by adjustment of the position of the focused beam on the radiometer detector. It was found that such a balance also gave an optimum output signal.

Data analysis

Analysis procedures generally followed those of previous studies using the LIRAD technique (P1–P4). As mentioned in section 2, some modifications were necessary because of the lower altitude of the clouds studied here. A brief outline of the analysis is given in this section; differences from the procedures used in P4 are explained in greater detail. Methods of identifying cloud layers of supercooled water drops and planar ice crystals are also described.

Profiles of backscatter

Profiles of attenuated backscatter B′(π, z), defined in (4), were converted to profiles of cloud backscatter coefficient Bc(π, z) by methods described in P4:
i1520-0450-39-2-135-e5
Values of molecular (Rayleigh) backscatter Bm(π, z) and transmittance τm(zb, z) were calculated based upon pressure and temperature data interpolated between earlier and later radiosonde profiles. Values of cloud transmittance τc were calculated iteratively at each level, using a preliminary value of the backscatter-to-extinction ratio k/2η based on cloud temperature with successive estimates of the cloud backscatter coefficient Bc, as described in P4.

Absorption coefficient and IR emittance

Profiles of visible backscatter coefficient Bc(π, z) were converted to profiles of IR absorption coefficient σa using the relation (P4)
i1520-0450-39-2-135-e6
The initial value of ξ was set high to ensure convergence in clouds with high optical thickness. The downward-emitted absorption radiance at cloud base La was calculated using the profile of σa(z) and an equation of radiative transfer (P4). Additional second-order cloud radiance components Lsc and Lr in (3) were added, and the transmittance by water vapor and other gases between the cloud and ground τcg (P4) was used to determine the theoretical cloud radiance at the surface, Lcts:
LctsLaLscLrτcg
The measured cloud radiance at the surface Lcms is obtained by rearranging (2) and writing Lcms = Lcτcg:
LcmsLsLskyLacτcgτc
(We neglect radiance due to water vapor within the cloud as it is small in comparison with the other terms.) As noted previously, the assumption (used in cirrus cases) that all water vapor resides below the cloud does not hold for midlevel clouds. Values of Lsky, τcg and Lac in (8) were calculated as described in P4. The calculation includes absorption due to water vapor dimers, weak water vapor lines, and wings of strong water vapor lines, and estimates for CO2 lines and aerosol attenuation. For cases where no cloud-free radiance could be measured, the value of the dimer absorption coefficient k1 [(13) in P4] was set to 8.1 g−1 cm2 atm−1 to agree with values used by Clough et al. (1989) in their LOWTRAN code. For runs having lidar shots with no cloud detected, k1 for the run was adjusted to make the modeled radiance match the measured radiance during cloud-free periods.

The ratio ξ in (6) was adjusted iteratively until the radiance values Lcts and Lcms in (7) and (8) agreed to within 0.2%, resulting in an estimate of the absorption emittance ϵa and IR optical depth δa that was consistent with the observed radiance for each lidar shot. In a small number of cases, this algorithm was unable to produce a value of Lcts as high as the measured value Lcms, even if ξ was adjusted high enough to make the cloud“black.” This was true for several highly attenuating clouds with midcloud temperatures above −15°C. One explanation for this problem is that the actual temperature and humidity profiles may have been different from those obtained by interpolating between the morning and evening sonde measurements. For lidar shots exhibiting this problem, the emittance was set to unity, and the shot was excluded from statistics on δa and σa.

Backscatter-to-extinction ratio

The attenuated cloud backscatter Bc(π, z)τ2c was estimated from (4) using the approximation
i1520-0450-39-2-135-e9
and the integrated attenuated backscatter γ′(π) was calculated as
i1520-0450-39-2-135-e10
This approximate form was used to make the calculated γ′(π) independent of the backscatter-to-extinction ratio k; in the exact form of (9), the Bm(π, z) term is multiplied by τ2c(zb, z), but calculation of τc requires an assumed value of k. This is a very good approximation, however, with errors of less than 0.4% in γ′(π) for a homogeneous 1-km-thick cloud; the errors are small because B′(π, z) is much greater than Bm(π, z) when τ2c differs significantly from unity.

Values of k/2η were obtained by calculating a least squares fit of (1) to sets of γ′(π) and ϵa. Because k is a function of particle habit, separate fits were performed over different ranges of midcloud temperature. The four values of k/2η were then used in a new iteration of calculations described in section 5a, beginning with B′(π, z). This process was repeated until the derived values of k/2η converged.

Depolarization ratio

Profiles of depolarization ratio Δ(z) were calculated using the attenuated cloud backscatter Bc(π, z)τ2c(zb, z) given in (9). Values of the perpendicular and parallel attenuated cloud backscatter were combined to give the depolarization ratio:
i1520-0450-39-2-135-e11
Values of integrated depolarization ratio
i1520-0450-39-2-135-e12
were also calculated.

Identification of layers of supercooled water

Individual spherical droplets produce zero depolarization in the backscatter direction. However, in a water cloud of moderate to high optical depth, multiple scattering processes cause a gradual increase of depolarization ratio Δ(z) from zero at cloud base as the lidar pulse penetrates into the cloud. At the same time, the attenuated backscatter coefficient will decrease rapidly into the cloud because of strong cloud attenuation. Such cloud patterns have been observed by Sassen (1978, 1984) and simulated by Sassen et al. (1992). The latter authors also showed how the values of Δ(z) depend on telescope aperture. Thus, layers of water cloud can be unequivocally identified from such patterns. An example of those measured in the current study is shown in Fig. 1a and is utilized to identify cloud layers containing supercooled water. As a contrast, Fig. 1b shows returns from an ice layer that are more typical of hexagonal columns. The value of Δ(z) rises immediately and stays fairly constant throughout the cloud [near the cloud boundaries, values of Δ(z) were too noisy to include].

Detection of layers of planar ice crystals

Individual planar hexagonal plates, when illuminated orthogonal to their large surfaces, produce characteristic patterns of very high backscatter and near-zero depolarization. Such crystals, after attaining a certain size, become horizontally oriented when falling through the atmosphere and, thus, also produce similar patterns for a vertically pointing lidar.

Platt et al. (1978) and Sassen (1984) demonstrated the high degree of horizontal orientation of some layers of ice crystals by scanning the lidar away from the vertical. At the same time, the very high measured backscatter rapidly decreased, and the depolarization increased. Platt et al. (1978) found that the lidar signal had decreased by a factor of 10 when pointing 0.6° away from the vertical. Platt (1977) measured high backscatter and low depolarization ratios in a cloud between −20° and −10°C. Platt (1978) and Popov and Shefer (1994) were able to model the characteristic lidar backscatter patterns, showing that the presence of ice crystal plates was a necessary and sufficient condition for the high backscatter measured by lidar in such cases. Sassen (1980) made measurements from the ground on optical light pillars generated by nearby light sources. He demonstrated that horizontally oriented planar ice crystals were necessary to give such patterns, but that the ice crystals must have diameters greater than about 100 μm. Thus, layers of smaller ice crystals would be more randomly oriented and would not give the characteristic lidar patterns. Popov and Shefer (1994) also pointed out that backscatter from planar crystals could mask returns from other types of crystals that might also be present.

Patterns of very high backscatter and low depolarization ratio are found to occur most frequently in the −20° to −10°C region in midlevel clouds, where planar ice crystals show maximum growth rates (Ryan et al. 1976; Miller and Young 1979). Such patterns of lidar backscatter and depolarization are used in this study to identify layers of hexagonal plates. Figure 1c shows backscatter patterns that identify three different cloud layers. From 4.0 to 4.15 km, there is an ice layer with a “normal” value of Δ(z) of about 0.4. From 4.15 to 4.4 km, the very high backscatter coefficient and low value of Δ(z) indicates a layer containing oriented planar crystals. The patterns from 4.6 to 5 km indicate a water layer.

Results and discussion

This paper describes relations between calculated values of ϵa, γ′(π), k/2η, Δ, Δ(z), and midcloud temperature in midlevel clouds. Time–height representations of attenuated backscatter coefficient and depolarization ratio Δ(z) are also presented.

Case studies

All cases of midlevel cloud from the ECLIPS II data were selected for analysis where possible. “Midlevel cloud” was defined, for the purposes of this study, as having temperatures between −25° and 0°C. This corresponded to clouds lying between 2- and 6-km altitude. Thus, some clouds that could be more accurately described as boundary layer clouds were included. A few cloud layers with temperatures up to 5°C were included for some purposes that are specified later. Cases where the midlevel cloud was obscured by low cloud below 2 km were excluded. The characteristics of the layers and their spatial and temporal extent were also determined from visual sightings, from satellite images, and from solar downcoming global and diffuse measurements at the surface (Platt et al. 1994).

The weather conditions pertaining to the whole period indicated that June 1991 was the wettest experienced by Melbourne (and Aspendale) since recordkeeping began in 1885. It was also one of the warmest. A succession of cold fronts from 13 to 28 June, another from 4 to 9 July, and a high pressure ridge in between permitted the study of a variety of clouds. The cloud height and temperature ranges in the various cases are listed in Table 1, along with the corresponding dates and observation times. The lidar telescope angular aperture employed is also shown. Table 3 gives a description of the cloud structure, phase, optical properties, and other distinguishing features for each case.

Time–height profiles of lidar attenuated backscatter and depolarization ratio are shown in Figs. 2a–l and 3a–l for all the eight cases. Radiosonde profiles of temperature and relative humidity measured at 0900 and 2100 LT are also shown. More detailed analyzed results for selected cases are shown in Figs. 4–7, and discussed below.

Cloud features in selected cases

These cases were selected to indicate the large variability in infrared emittance, how it was correlated with integrated attenuated backscatter, and the behavior of Δ in typical clouds. Figure 4a from 20 June indicates that the IR emittance of supercooled water clouds is not always unity. The depolarization ratio Δ is seen to have some correlation with emittance, another feature of water clouds. The cloud temperature is well below zero. Values of γ′(π) in Fig. 4e show a typical relationship with ϵa. Figures 5a–e on 27 June show a more complex structure in comparison with 20 June. Again, ϵa is quite variable, but Δ is now also very variable. There is only one period, between 1410 and 1420 LT, when Δ is characteristic of a supercooled water cloud. The plot of γ′(π) versus ϵa is similarly much more scattered, due in part to the very variable properties of cloud fall streaks. Figures 6a–e on 2 July show properties of what is often regarded as “typical” stratus cloud. For over half the time ϵa is unity, and the cloud is very attenuating. Values of Δ increase somewhat with ϵa, and values of γ′(π) are close to those of k/2η [see (1)]. In contrast to 20 and 27 June, the cloud on 4 July (Figs. 7a–e) has an emittance close to unity for the entire period. Values of Δ are more typical of ice cloud, at least to about 1520 LT, when values drop to 0.2, and patterns in Figs. 3g and 3i then indicate a supercooled water cloud. Values of k/2η from Fig. 7e indicate the large range of backscatter phase function in this cloud.

Cloud infrared emittance and absorption coefficient

Features in section 6b indicate that the infrared emittances of the observed midlevel clouds were quite variable on some days, whereas on other occasions they were close to unity. Because the uncertainty in emittance is shown to be 0.03 to 0.05 in the appendix, the variations were mostly real. Supercooled water clouds at times showed ϵa < 1, whereas some ice clouds showed ϵa ≃ 1.

A summary of the emittance variations in various temperature ranges is shown in Fig. 8 in histogram form. Clouds in the −25° to 0°C temperature range are divided up as shown. Frequencies of cloud with ϵa = 1 are shown to the right of unity emittance. The estimated uncertainty in emittance of 0.03 to 0.05 is within one frequency interval. The most striking feature is the high frequency of emittances of unity at the high temperature end. This frequency varies from 54% in the −10° to 0°C range to zero for temperatures below −20°C. In the latter range, nearly 30% of the clouds have emittances less than 0.1, and the histogram starts to resemble those for cirrus clouds, as discussed in Platt and Dilley (1981) and Platt et al. (1987). These behaviors are not unexpected. The specific humidity of the air, and thus the ice/liquid water content, will increase with temperature. Mean values and standard deviations of measured emittance in various temperature ranges are shown in Fig. 9. The standard deviations are much larger than the estimated errors. As the random error is reduced considerably by averaging, the error is then given by the systematic error of ∼0.02. Therefore, the standard deviation is caused mainly by changes in cloud emittance. As the emittance distributions in Fig. 8 tend to be quite skewed, mean values of emittance in midlevel clouds can be misleading. However, they do appear near the high end of the mean values obtained in cirrus clouds (Platt et al. 1998), which are also plotted in Fig. 9. The collected data, which now extend from −75° to 0°C, are intended for parameterization of cloud optical properties in climate models and for validation of products from cloud-resolving models.

Values of cloud IR absorption coefficient σa were also calculated by dividing the cloud optical depth by the cloud physical depth. This could not be done for clouds with ϵa = 1 when the optical depth was indeterminate, so that mean values of σa err on the low side. Such values are plotted in Fig. 10 as mean values over the same temperature ranges, as in Fig. 8. The standard deviations in σa were large but not shown. The values are still on the high side of those obtained previously for cirrus clouds. The range of values is seen to be from 0.02 to 8 km−1 in a temperature range of from −75° to 0°C. Again this is due to the increase of water supply with temperature that follows the nonlinear Clausius–Clapeyron equation. Such a relation that can be gleaned from Fig. 10 is again intended for cloud parameterization and validation.

Effective cloud backscatter-to-extinction ratio

As indicated in (1) and in earlier papers, the integrated attenuated backscatter γ′(π) tends to the quantity k/2η when ϵa tends to unity. This quantity gives information on cloud phase and ice crystal habit. This is the first set of such data on midlevel clouds. As discussed in section 6b, values of γ′(π) can be scattered for some midlevel clouds because of different phases and ice crystal habits. Plots of γ′(π) versus ϵa for various temperature ranges are shown in Figs. 11a to 11d. The most scatter is apparent in Figs. 11b and 11c, between temperatures of −20° and 0°C. In optically dense clouds, values of k/2η around 0.2 to 0.6 signify hexagonal ice crystals, values from 0.4 to 0.5 are due to supercooled water, and the highest values (up to about 1) are due to oriented planar crystals. In Fig. 11a, values represent hexagonal crystals (Platt et al. 1998) or supercooled water drops, and in Fig. 11d, water clouds, although they are on the low side for water and some ice crystals might be present.

Mean values of k/2η in three temperature intervals are compared with past observations in Fig. 12. The values are comparable with cirrus values in winter where they overlap. The data are suitable for comparison with modeled backscatter phase functions.

Values of k/2η were also estimated for periods when the layers had the definite backscatter characteristics of supercooled water clouds (see section 5e). Results are shown in Table 4 for various cases. Also shown are mean values of k, assuming that η = 0.7 (Platt 1981), together with the values from Deirmendjian (1969) for two models of water cloud. The agreement of the average value of k with the Deirmendjian values is quite good. It is consistent also with the theoretical multiple scattering factor (≈0.7, Platt 1981). However, the standard deviation is a bit larger than anticipated from the small difference in values between the C1 and C2 clouds, together with the estimated calibration errors in lidar backscatter. Nonetheless, the values measured for what are considered to be supercooled water clouds are approximately consistent with theory. This is the first validation of the theoretical values of η from Platt (1981). It should be noted that the telescope aperture on 2 July was 5 mrad. Thus there would be greater multiple scattering effects (as discussed in the next section), the value of η would be smaller, and k/2η would be larger, as observed. Assuming that η = 0.6 for that day, the values in parentheses in Table 4 would be obtained.

Depolarization ratio

Values of Δ also show considerable scatter at all temperatures (see section 6b). Values for supercooled water clouds varied between 0 and 0.8, depending on aperture and optical depth (see below). Values for ice clouds varied from near zero (horizontal plates) to close to unity (fall streaks).

The generation of depolarized radiation in water clouds, being a function of multiple scattering (section 5e), varies with range, receiver angular aperture, cloud drop size, and cloud optical depth. Figure 13a shows that Δ is correlated with cloud optical depth (or equivalently, ϵa) but that the regression values appear to vary from cloud to cloud. The higher values on 2 July were made with the larger telescope aperture of 5 mrad, giving additional multiple scattering. When the aperture was 2 mrad and the emittance was close to unity, variations in Δ must have been due to changes in drop size or cloud range.

Typical values of Δ(z) plotted against cloud depth, for approximately the same values of mean σa, are shown in Fig. 13b. The effects of telescope aperture are again evident. These are the first experimental data to indicate specifically the dependence of depolarization ratio on optical depth and angular aperture as predicted by Sassen et al. (1992).

Cloud phase: Presence of supercooled water, ice, and hexagonal plates

Using the methods described in section 5e, the incidence of layers of supercooled water was determined. Table 5 shows the total times that water and ice were present for given temperature intervals. Columns 4 and 5 show the corresponding percentages. There is, of course, some overlap in the times when both water and ice were detected in the same temperature layer. Also, the figures do not take into account the vertical extent of a given cloud layer, except when it might protrude into a separate temperature interval. Further, some higher layers may be obscured. The ice phase is seen to dominate for temperatures below −20°C. Between −20° and −5°C, water is prevalent for 43% of the time and ice for 57%. The variations in the separate temperature intervals are probably due to the small sample and relative uncertainty, as no trend can be seen. For temperatures greater than or equal to −5°C, the water phase dominates, as expected. Of course, it is possible that some ice existed in some water layers, and vice versa. Lidar could only determine the dominant phase. The high prevalence of water in the −20° to −15°C interval was due to a long-lasting supercooled water layer on 20 June (Figs. 2a–c and 4a–e). Similarly, the high prevalence of ice in the −10° to −5°C range was due to the lowering of the cloud base on 4 July (Figs. 3g–i and 7a–e).

There is not much information on water/ice percentages in mixed-phase clouds with which to compare the present data. Mossop et al. (1970) sampled ice incidence in small cumulus clouds off the coast of Tasmania, Australia. Their percentages of ice for two temperature intervals are also shown in Table 5. Although measured in different types of cloud, the values are comparable. King (1982) and Ryan et al. (1985) sampled frontal clouds over the Western District of Victoria, Australia. The clouds had maximum top at −27°C and lowest base at 8°C. They found mainly the ice phase throughout the clouds. Sassen (1984) investigated a long-lived frontal cloud with ground-based lidar and radar, and found a fairly persistent supercooled water layer. Some layers of supercooled water were found near cloud tops in the present work on 27 June at −23°C and on 4 July at −10°C. Such layers near cloud top have been seen also by King (1982), Sassen (1984), Paltridge et al. (1986), and Rauber and Tokay (1991). The present ground-based method appears to be very promising for the types of clouds sampled.

The method of section 5f was used to identify layers of hexagonal planar plate crystals greater than ∼100 μm in size. Such plate crystals were detected for 20% of the time in ice layers between −20° and −10°C. Outside of that temperature range, no layers with the hexagonal crystal signature were detected. Hexagonal plate crystals were detected on 27 June in areas of virga at temperatures between −18° and −12°C. On 8 July they were detected between −16° and −12°C, and a persistent layer formed between 1612 and 1645 LT with a depth of about 150 m and a temperature of about −15°C. The observed temperature ranges are consistent with the calculations of Ryan et al. (1976) and Miller and Young (1979) who found that the growth rate of planar hexagonal crystals dominated over that of other habits between −20° and −10°C, peaking at −15°C. Fall streaks in some of the glaciating clouds showed some interesting variations in Δ(z). Fall streaks on 27 June (Fig. 2i) had values of Δ(z) that approached unity at times. High values were also observed in some streaks on 8 July. Values were consistently high in such fall streaks. They have been observed previously by Sassen (1976, 1978) and Derr et al. (1976). Schotland et al. (1971) and Pal and Carswell (1977) found values approaching unity in falling snow. Although of interest, the reason for these high values remains unknown.

The lidar depolarization method has allowed a detailed examination of the prevalence of water and ice in midlevel clouds, and of the prevalence of hexagonal plate crystals. This is believed to be the most detailed study made to date from the surface, and the first calculation of ice–water percentages, although cloud samples were rather limited.

Conclusions

The ECLIPS results obtained in this study indicate the power of the methods used to obtain detailed information on cloud optical properties and cloud phase. The only disadvantage is the high attenuation in some of the clouds. The present dataset has been analyzed successfully to give information on the optical properties of midlevel clouds that has not been available heretofore. In terms of the phase, the data show how the fraction of supercooled water in the clouds can be obtained to quite a good approximation.

The mean infrared beam emittance at 10.84 ± 0.5 μm, when binned into various temperature intervals, increases with temperature, as expected. The standard deviation of the variation in any temperature interval is quite large, and this variation is much larger than the estimated systematic error of 0.02. The proportion of the cloud layers with emittance equal to unity is seen to increase with temperature. The emittance frequency histogram shows an increasing percentage of unity emittance at increasing temperatures and is thus quite skewed toward unity, except at the lower temperatures. The IR absorption coefficient shows a similar trend.

The effective backscatter-to-extinction ratio, k/2η, of the clouds is typical of either supercooled water clouds or various ice crystals. When a theoretically derived value of the multiple scattering factor is applied, values of k for the supercooled water clouds agree well with values of the normalized backscatter phase function for water drops given by Deirmendjian (1969). The values of backscatter-to-extinction ratio found in ice layers cover the ranges found previously for hexagonal crystal columns through to oriented planar crystals.

The lidar linear depolarization ratio shows characteristic patterns that are used to identify layers of supercooled water, hexagonal ice crystals, and even planar ice crystals. Similar patterns of backscatter coefficient conform to the various interpretations. Although these patterns have been observed and interpreted previously, they are used in this study to obtain detailed information on cloud phase in the present set of midlevel clouds. They provide information on the percentage of ice in the clouds at various temperatures—information that is very scarce, and that has not been measured previously by ground-based sensing.

The percentage of ice in the cloud layers is found to be 100% for temperatures below −25°C, falling to 0% above 0°C, as expected. The percentages at intermediate temperatures are nearly equal, within the levels of experimental uncertainty, but with some preponderance of ice.

Averages of IR beam emittance and absorption coefficient at 10.84 ± 0.5 μm extend past statistics obtained before in cirrus clouds. These now represent a comprehensive dataset over the temperature range from −75° to 0°C for parameterization of cloud properties in models and for the validation of cloud microphysical models. A caveat in the current study is that the IR absorption coefficients represent cases of semitransparent cloud, and thus tend to be lower than correct values at the higher temperatures. Mean data for the backscatter-to-extinction ratio give information for the validation of modeled cloud backscatter phase functions for various crystals. In the case of the water clouds, the values verify past theoretical calculations of the multiple scattering factor.

The ground-based method of identifying the phase of midlevel clouds gives more detail than can be obtained by other methods. In situ microphysical measurements from aircraft flying at certain specific levels in the cloud can miss detailed vertical structure. Similarly, aircraft spiraling down through the cloud at one location can miss horizontal variations in structure. Balloon-borne measurements of cloud microstructure have the same problem. Of course, detailed images and sizes of cloud particles measured in situ are extremely useful and very necessary for process studies and for understanding cloud evolution. A combination of such methods over short periods, together with long-term ground-based monitoring, would be desirable.

Combined lidar and radiometer long-term observations are now being made at several different sites around the globe under the auspices of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program (Stokes and Schwartz 1994). These observations, following the ECLIPS pilot study, are providing very useful data on clouds. Micropulse lidars and infrared radiometry are giving continuous information on cloud optical properties in the spirit of the ECLIPS. These lidars will eventually be upgraded to give depolarization and better vertical resolution, together with more sensitive and faster IR radiometers with narrower receiver beamwidths. This study has shown the value of such observations. The ARM program also includes millimeter radar, which when put together with IR radiometry or lidar can obtain information on cloud microphysics and effective radius. ARM sites are now installed in northern Oklahoma, Manus Island (in the tropical west Pacific), Nauru (farther east in the Pacific), and the North Slope of Alaska.

Acknowledgments

The authors thank the following: Guntis Grauze assisted in the preparation and operation of the IR radiometer and edited the lidar data. Dr. Bruce Forgan, Bureau of Meteorology, Melbourne, arranged the surface flux data. Dr. C. Wooldridge, then at Macquarie University, took all-sky observations. The Australian Bureau of Meteorology provided the radiosonde data. Some of the later stages of analysis were supported by the U.S. Department of Energy, Office of Health and Environmental Research, Grants DE-FG02-92ER61373 and DE-FG03-94ER61748.

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APPENDIX

Infrared Radiometer Error Analysis

There are both random and systematic errors present in the ECLIPS data that restrict the accuracy in measurement of infrared emittance. Random errors occur in all the terms of (8). They are a function of the random noise in the radiometer detector and amplifiers and appear in the cloud and water vapor radiances. These errors have been treated here in the same manner as in Platt et al. (1998). The radiometer noise is a smaller component of the cloud signal than it is in the tropical cirrus case, because of the larger cloud signals.

Systematic errors occur in the estimation of the water vapor radiance, the radiometer calibration, and in estimation of the scattering and reflecting terms in (7). Errors in ϵa were also estimated for errors in retrieved values of B(π, z), and their variations in z [see (6)]. There were no continuous measurements of water vapor path available in the ECLIPS data. On some days, the water vapor radiance could be estimated from the cloudless periods. On the other five days there was a continuous cloud cover. The radiance then had to be estimated from interpolated values of the water vapor path calculated from the radiosonde data, coupled with the known value of the water vapor absorption coefficient. As the water vapor path changed appreciably between the morning and evening radiosondes on some days, uncertainties from this cause had to be estimated.

Table A1 shows the minimum detectable emittance from both random and systematic sources, together with the total. Random and systematic radiances were 0.057 and 0.049 W m−2 sr−1, respectively. In the former case, the radiometer integration time, as set in the experiment, was 10 s. The total emittance uncertainties are seen to be in the region of 0.03 to 0.05, which is much smaller than the total range of emittances measured. Thus, the instrument was well able to measure differences of cloud emittance in this experiment.

Fig. 1.
Fig. 1.

Cloud attenuated backscatter coefficient (solid line) and linear depolarization ratio (broken line) for three different cloud types: (a) water drops (2 Jul); (b) hexagonal columns, bullets, and rosettes (typical ice cloud; 4 Jul) [Δ(z) is not calculated where the signal becomes too small]; and (c) oriented hexagonal plate crystals (4.15–4.4 km; 8 Jul). Layers of type (a) and (b) are also present in (c).

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 2.
Fig. 2.

(a), (d), (g), (j) Time–height attenuated backscatter coefficient; (b), (e), (h), (k) temperature and humidity profiles (0900 LT, black; 2100 LT, red); and (c), (f), (i), (l) time–height linear depolarization ratio, for observational periods on 20, 25, 27, and 28 Jun, respectively.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 3.
Fig. 3.

Same as Fig. 2 but for 1, 2, 4, and 8 Jul, respectively.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 4.
Fig. 4.

Values of (a) emittance ϵa, (b) integrated attenuated backscatter coefficient γ′(π), (c) linear depolarization ratio Δ, and (d) midcloud temperature, all plotted against time, and (e) γ′(π) plotted against ϵa for 20 Jun.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 5.
Fig. 5.

Same as Fig. 4 but for 27 Jun.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 6.
Fig. 6.

Same as Fig. 4 but for 2 Jul.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 7.
Fig. 7.

Same as Fig. 4 but for 4 Jul.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 8.
Fig. 8.

Histogram plots of the distribution of emittance ϵa for three ranges of midcloud temperature.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 9.
Fig. 9.

Plots of mean values of IR emittance ϵa for the temperature intervals of Fig. 8 vs midcloud temperature as compared with previous cirrus experiments at other latitudes.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 10.
Fig. 10.

Same as Fig. 9 but for IR absorption coefficient σa.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 11.
Fig. 11.

Plots of γ′(π) vs ϵa for four temperature intervals: (a) −25° to −20°C, (b) −20° to −10°C, (c) −10° to 0°C, and (d) 0° to 5°C.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 12.
Fig. 12.

Same as Fig. 9 but for the backscatter-to-extinction ratio k/2η.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Fig. 13.
Fig. 13.

(a) Plot of integrated linear depolarization ratio Δ vs IR emittance ϵa for various water clouds; (b) plots of the profile of linear depolarization ratio Δ(z) vs cloud penetration for σa ≈ 13 km−1 and telescope angular apertures of 2 and 5 mrad.

Citation: Journal of Applied Meteorology 39, 2; 10.1175/1520-0450(2000)039<0135:OPAPOS>2.0.CO;2

Table 1.

A summary of observations at Aspendale during ECLIPS II.

Table 1.
Table 2.

Lidar and infrared radiometer parameters.

Table 2.
Table 3.

A summary of meteorological conditions and cloud observations.

Table 3.
Table 4.

Values of k/2η and k for supercooled water clouds.

Table 4.
Table 5.

Duration and frequency of detection of ice and water in clouds.

Table 5.

Table A1. Random and systematic contributions to minimum detectable emittance.

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