Evaluation of the Lidar–Radar Cloud Ice Water Content Retrievals Using Collocated in Situ Measurements

Sujan Khanal University of Wyoming, Laramie, Wyoming

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Zhien Wang University of Wyoming, Laramie, Wyoming

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Abstract

Remote sensing and in situ measurements made during the Colorado Airborne Multiphase Cloud Study, 2010–2011 (CAMPS) with instruments aboard the University of Wyoming King Air aircraft are used to evaluate lidar–radar-retrieved cloud ice water content (IWC). The collocated remote sensing and in situ measurements provide a unique dataset for evaluation studies. Near-flight-level IWC retrieval is compared with an in situ probe: the Colorado closed-path tunable diode laser hygrometer (CLH). Statistical analysis showed that the mean radar–lidar IWC is within 26% of the mean in situ measurements for pure ice clouds and within 9% for liquid-topped mixed-phase clouds. Considering their different measurement techniques and different sample volumes, the comparison shows a statistically good agreement and is close to the measurement uncertainty of the CLH, which is around 20%. It is shown that ice cloud microphysics including ice crystal shape and orientation has a significant impact on IWC retrievals. These results indicate that the vertical profile of the retrieved lidar–radar IWC can be reliably combined with the flight-level measurements made by the in situ probes to provide a more complete picture of the cloud microphysics.

Corresponding author address: Sujan Khanal, Dept. of Atmospheric Science, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071. E-mail: skhanal@uwyo.edu

Abstract

Remote sensing and in situ measurements made during the Colorado Airborne Multiphase Cloud Study, 2010–2011 (CAMPS) with instruments aboard the University of Wyoming King Air aircraft are used to evaluate lidar–radar-retrieved cloud ice water content (IWC). The collocated remote sensing and in situ measurements provide a unique dataset for evaluation studies. Near-flight-level IWC retrieval is compared with an in situ probe: the Colorado closed-path tunable diode laser hygrometer (CLH). Statistical analysis showed that the mean radar–lidar IWC is within 26% of the mean in situ measurements for pure ice clouds and within 9% for liquid-topped mixed-phase clouds. Considering their different measurement techniques and different sample volumes, the comparison shows a statistically good agreement and is close to the measurement uncertainty of the CLH, which is around 20%. It is shown that ice cloud microphysics including ice crystal shape and orientation has a significant impact on IWC retrievals. These results indicate that the vertical profile of the retrieved lidar–radar IWC can be reliably combined with the flight-level measurements made by the in situ probes to provide a more complete picture of the cloud microphysics.

Corresponding author address: Sujan Khanal, Dept. of Atmospheric Science, University of Wyoming, 1000 E. University Ave., Laramie, WY 82071. E-mail: skhanal@uwyo.edu

1. Introduction

Airborne in situ instruments provide measurements of cloud particles at a very fine temporal and spatial scale and play an important role in understanding cloud microphysical and dynamical processes (Stevens et al. 2003; Baker and Lawson 2006; McFarquhar et al. 2007; Bailey and Hallett 2009). However, these measurements cover only limited time periods over limited geographic regions. Furthermore, a major weakness of airborne in situ instruments used in cloud particle studies is that their sample volumes, defined along a fixed flight path, are relatively small. Small sample volumes of these instruments lead to high statistical measurement uncertainties in clouds where particle concentrations are small. Remote sensing measurements, which are available from the surface, airborne, and space platforms, overcome this weakness of in situ instruments (Platnick et al. 2003; Wang et al. 2009, 2012). Radars provide information about cloud and precipitation particles as well as cloud dynamics (Heymsfield et al. 1996; Stephens et al. 2002) and lidars are capable of measuring aerosols and optically thin clouds (McGill et al. 2002). Since radar is more sensitive to larger particles, it could fail to detect clouds with small particles. On the other hand, lidar signal does not penetrate optically thick clouds because of strong attenuation (Wang and Sassen 2002). Combining lidar and radar measurements provides not only more complete cloud macrophysical properties (Clothiaux et al. 2000; Wang and Sassen 2001) but also more accurate cloud microphysical properties (Heymsfield et al. 2008).

The combined radar and lidar measurements can provide a vertical profile of ice water content (IWC), significantly increasing the sampled volume in comparison with in situ instruments (Donovan and van Lammeren 2001; Okamoto et al. 2003; Deng et al. 2010). However, most of the remote sensing cloud microphysics retrieval algorithms are developed for either satellite or ground-based measurements (Wang and Sassen 2002; Pokharel and Vali 2011). Depending on the assumptions made about the cloud microphysics, such as the ice particle size distribution and particle habit, the retrieved IWC can vary significantly (Heymsfield et al. 2008). Therefore, it is important to evaluate remote sensing retrievals of cloud IWC based on in situ measurements to ensure the accuracy of the remote sensing–retrieved properties. In situ measurements can also provide useful information for the assumptions about ice particle size distribution and particle habit. But, in many cases, the in situ measurements are either unavailable (Okamoto et al. 2003) or the measurements are spatially separated by more than 1 km (Deng et al. 2013). The spatial separation of in situ and remote sensing data and the spatial inhomogeneity of cloud microphysical properties present a significant challenge for algorithm validation (Wang et al. 2005). To overcome this issue, Heymsfield et al. (2008) used in situ microphysical properties to simulate lidar and radar measurements to test different retrieval algorithms. Therefore, the collocated in situ and remote sensing measurements from the University of Wyoming King Air (UWKA) aircraft during the Colorado Airborne Multiphase Cloud Study, 2010–2011 (CAMPS) provide a unique dataset to evaluate and refine remote sensing retrieval of IWC. Remote sensing measurements add the context to understand and interpret in situ measurements, while together airborne in situ probes and remote sensing instruments provide complementary capabilities to study cloud microphysical and dynamical processes in greater detail than before (Wang et al. 2012).

In this study, the retrieved IWC from combined lidar–radar measurements (Wang and Sassen 2002) is evaluated using an in situ total water content (TWC) probe: the Colorado closed-path tunable diode laser hygrometer (CLH; Davis et al. 2007a,b). A brief description of the relevant in situ and remote sensing instruments used during CAMPS and their measurement capabilities is provided in section 2. Section 3 describes the IWC retrieval algorithm and the results. In section 4, the conclusions of this study are given.

2. Measurements

The dataset used for this study was collected by the UWKA during the CAMPS field campaign from December 2010 to February 2011 over the Rocky Mountains in northern Colorado and southern Wyoming (flights.uwyo.edu/projects/camps11). The main objective of CAMPS is to gain a better understanding about the vertical and horizontal structure of wintertime mixed-phase clouds over complex terrain with the aim of better representing these types of clouds in cloud-resolving and climate models. This field project provided combined radar–lidar measurements together with a suite of in situ cloud microphysical measurements including bulk TWC measurements from the CLH and offered a unique opportunity for lidar–radar retrieval algorithm development and validation.

Cloud water content is a fundamental cloud microphysical property (Korolev et al. 1998; Schwarzenboeck et al. 2009). Vertical profiles of cloud water content influence cloud dynamical and radiative properties (Gultepe and Isaac 1997). There are several instruments that are routinely used to measure cloud liquid water content (LWC) on the UWKA, such as the forward scattering spectrometer probe (FSSP; Knollenberg 1981), cloud droplet probe (CDP; Lance et al. 2010), Gerber Particle Volume Monitor 100A (PVM; Gerber et al. 1994), and Droplet Measurement Technologies (DMT) hot-wire probe. LWC can be derived from FSSP and CDP as a third moment of the size distribution function, whereas PVM and DMT hot-wire probe provide direct measurements of the LWC. These instruments are primarily designed to be reliable in liquid clouds but may suffer from large uncertainties in the presence of ice particles, such as ice shattering impacts on FSSP (Gardiner and Hallett 1985; Heymsfield 2007; Febvre et al. 2012). IWC may be calculated indirectly from ice particle size distribution measurements made by 2D-C and/or 2D-P probes using empirical mass–size relationships. Studies indicate that different mass–size relationships can result in uncertainty of a factor of 2 or more in estimated IWC (Heymsfield et al. 1990; Lawson and Baker 2006).

The CLH measures cloud TWC by evaporating hydrometeors and measuring the resulting water vapor via infrared absorption. The CLH can provide improved IWC measurements relative to 2D-C or 2D-P (Davis et al. 2007b) because no assumptions need to be made about the density of particles to convert from measured diameter to mass. The CLH has three major components: a tunable diode laser source (wavelength 1.37 μm), water vapor absorption cell, and a detector. The decrease in the detected laser intensity referenced to the transmitted light source can be related to the concentration of the water vapor using the Beer–Lambert law. CLH has a subisokinetic inlet that enhances the particle concentration, relative to ambient concentration, which increases the detection threshold of the instrument (Davis et al. 2007a). The enhanced condensed water is evaporated in the heated flow path. For CAMPS, a heated mesh was added to the flow path to promote the shattering of large particles into smaller ones that evaporate more quickly, thereby increasing the size range of particles whose IWC can be completely measured. The CLH provides enhanced total water (eTW) mixing ratio measurements, which contains the contribution from both ambient water vapor and water vapor generated by the evaporation of liquid and/or ice particles. The TWC can be calculated as
e1
where w is the ambient water vapor mixing ratio measured from another independent in situ instrument such as the LI-COR hygrometer, and EF is the enhancement factor that accounts for the particle enhancement compared to the ambient value as a result of the subisokinetic inlet. IWC is the same as the TWC when there are no liquid particles present in clouds. EF is estimated as the ratio of the aircraft true airspeed and the instrument inlet airspeed. This estimated EF gives an overestimation of enhancement of small ice particles (~5 μm) but is an accurate representation of the enhancement for ice particles greater than about 25 μm (Davis et al. 2007b). IWC is mainly controlled by large ice particles for precipitating clouds like those sampled during CAMPS. The CLH IWC is only slightly biased dry (~10%) because of the overestimation of EF at small particle sizes. Davis et al. (2007b) report an uncertainty of 20% in CLH IWC measurements, which increases to 50% at smaller IWC values (<5 mg m−3). The large uncertainty at the lower IWC values is due to the small difference between the eTW and ambient water vapor mixing ratio. The measurement accuracy of the CLH eTW is about 10% (Davis et al. 2007b). CLH TWC was evaluated with other UWKA measurements for liquid-only clouds and they agree well.

The Wyoming Cloud Radar (WCR; http://atmos.uwyo.edu/wcr) and Wyoming Cloud Lidar (WCL; Wang et al. 2009) are active remote sensing instruments that are routinely carried on board UWKA for cloud studies. They provide two-dimensional measurements along a horizontal or vertical plane defined by the orientation of the beam. The WCR is a Doppler radar operating at 95 GHz (3-mm wavelength), and it provides measurements of radar reflectivity factor , Doppler velocity, and polarization fields. The signal is transmitted and received by up to four antennas (three single-polarization antennas and one dual-polarization antenna) pointing in different directions (Haimov and Rodi 2013). The WCR on board UWKA is calibrated using corner reflectors, and the measurement uncertainty of the WCR is ±2 dB. The upward-pointing WCL I (operating wavelength 355 nm) and downward-pointing WCL II (operating wavelength 351 nm) are used to obtain vertical profiles of backscatter power and linear lidar depolarization ratio (LDR). LDR is defined as the ratio of backscatter power in a perpendicular (cross polarized) channel to backscatter power in a parallel (copolarized) channel for a linearly polarized transmitted light. LDR can effectively provide information about cloud phase, especially when combined with radar measurements (Sassen 1991; Wang and Sassen 2001). Smaller LDR values indicate spherical liquid droplets, whereas larger LDR values indicate nonspherical ice particles. Lidar backscatter power is stronger in liquid and liquid-dominated mixed-phase clouds than in ice clouds because small liquid droplet number concentration is usually much higher when compared with large ice particle concentration. The WCR is more sensitive to larger particles than the WCL. In mixed-phase clouds, the WCR signal is dominated by ice particles, whereas the WCL signal is dominated by liquid droplets.

Since near-range signals can get saturated, WCL is designed to have an incomplete overlap for the near-range signals. The WCL overlap function during CAMPS is plotted in Fig. 1a. It is determined from the molecular backscattering at a wavelength of 355 nm in clear air above the boundary layer. Above ~300 m from the flight level, there is a full overlap. However, below 300 m the overlap correction is significant and reaches over two orders of magnitude close to the flight level. Original and overlap-corrected lidar backscatter profiles at 1731:30 UTC 20 February 2011 are plotted in Fig. 1b. The strong backscatter power close to the flight level shows that there is a cloud layer that extends to ~600 m above the flight level. Above this altitude, lidar backscatter power is dominated by the background molecular backscatter, which slowly decreases with height. Figures 1c and 1d show more clearly the impact of overlap correction based on 10-min WCL-I observations from the same flight. A narrow band of measurements near the flight level in Fig. 1c is biased low because of the incomplete overlap, which is corrected as seen in Fig. 1d. The lidar backscatter from clear air, for example around 1734 UTC, can be used to calibrate the WCL measurements. After this clear-air period, the aircraft again penetrated through optically thick clouds starting around 1735:30 UTC, which is indicated by very weak and noisy molecular signals above the cloud layer. The average flight-level temperature for this section is −33°C.

Fig. 1.
Fig. 1.

(a) The overlap function of the WCL I during CAMPS project. The dashed vertical line indicates the overlap of 1, i.e., a full overlap. (b) Original (red line) and overlap-corrected (black line) lidar backscatter profiles at 1731:30 UTC 20 Feb 2011 during CAMPS. (c) The 10-min original and (d) overlap-corrected lidar backscatter profiles (plotted in a base-10 log scale) from the same flight.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

For this study, IWC profiles are retrieved by combining WCL and WCR measurements, which has the potential to provide reliable IWC vertical structure with increased sampled volume. However, careful evaluations of retrieved IWC are needed. Here, CLH IWC measurements are used to evaluate WCR–WCL IWC retrievals.

3. Methodology and results

a. Algorithm description

For this study, a radar–lidar retrieval algorithm developed by Wang and Sassen (2002) is used. In the algorithm, IWC is retrieved from lidar extinction coefficient σ (km−1) and radar reflectivity (dBZ). The size distribution and shape of the ice particles are assumed to be a modified gamma size distribution and randomly oriented solid hexagonal ice crystals.

The vertical profile of cloud extinction coefficient is calculated from the WCL-I measurements by inverting the lidar equation (Klett 1981; Fernald 1984). Molecular backscattering coefficient (km−1 sr−1) at 355 nm is calculated using Rayleigh scattering theory based on measured ambient temperature and pressure at flight level assuming the standard atmosphere. Molecular extinction coefficient is related to through molecular lidar ratio , which is a constant, . An important assumption for lidar cloud extinction retrieval is that the lidar ratio for cloud , defined as the cloud extinction to backscatter ratio , remains constant. Yorks et al. (2011) suggests a distribution of from 10 to 40 sr with peak at 25 sr for ice clouds; depends on ice crystal habits and ice particle orientations. When plate ice crystals are horizontally oriented, can be as small as 1 because of their specular reflections increasing the backscattering. As the fraction of horizontally oriented ice crystals decreases, increases steadily and reaches close to the maximum value for a given ice crystal habit. Cloud can be determined with the constraint of cloud-layer transmittance derived from molecular signals above the cloud layer when the lidar signal is not totally attenuated, such as the measurements around 1731 UTC in Fig. 1d. Using this approach, an optimum of 13 sr is obtained for CAMPS clouds, which have temperature ranging from −40° to −10°C. So, the lidar ratio of 13 sr is used for this study. Lidar cloud measurements are also affected by multiple scattering. However, the greatest impact due to multiple scattering occurs in optically thick clouds when either liquid or mixed-phase cloud layers are present because of strong attenuation. The multiple scattering factor has been suggested in previous studies, such as Platt (1981) and Hu et al. (2006). For this study, a moderate multiple scattering correction factor of 0.8 is used because of the short distances of clouds above lidar.

In this study, the inversion of the lidar equation is carried out using a backward solution. In this approach the numerical iteration is calculated backward from a reference range, which is typically set at the far end of the lidar range gates. The backward solution is preferred because it provides a numerically stable solution and is also less sensitive to boundary values than is the forward approach (Klett 1981; Fernald 1984). The cloud-top boundary is selected based on the lidar backscatter power. For all ice clouds, it is the first range gate from the flight level where is smaller than 0. The cloud-top height for liquid-topped mixed-phase clouds is set at five range gates (~20 m) above this level because the lidar signal attenuates more quickly near the cloud top for these clouds. At cloud top, the lidar backscatter is set equal to the molecular backscatter, which is derived from Rayleigh scattering.

The derived extinction and radar reflectivity is then used to retrieve IWC based on the parameterization from Wang and Sassen (2002). IWC retrieved using this approach is quite tolerant with respect to the errors in and is primarily controlled by errors on the extinction coefficient. For example, according to Wang and Sassen (2002), for an uncertainty of 100% in and an uncertainty of 0% in , the uncertainty in retrieved IWC is 17%, whereas for an uncertainty of 0% in and an uncertainty of 100% in , the uncertainty in retrieved IWC is 71%. In Wang and Sassen (2002) the weak dependency on indicates that IWC retrievals have a weak dependence on ice crystal shape assumption because the relationship among , IWC, and general effective radius can be applied to other crystal shapes (Fu 1996). Therefore, uncertainties in retrieved extinction coefficients due to uncertainties in lidar ratio and cloud boundary conditions contribute to major uncertainties in retrieved IWC. For example, if the lidar ratio of 25 sr is used instead of 13 sr, the retrieved IWC increases by about 50% and the extinction increases by 70%. Similarly, an uncertainty of 19 m (five range gates) in the cloud-top height leads to around 10% and 13% error in IWC and extinction, respectively.

b. IWC retrieval and evaluation

1) Case studies

An example of combined lidar–radar IWC retrieval is given in Fig. 2 based on measurements made on 20 February 2011 during CAMPS. The flight-level temperature for the case shown in Fig. 2 is −31.6°C. Figures 2a and 2b show the radar reflectivity (dBZ) from up- and down-pointing WCR beams, respectively. Since, the first usable radar range gate is about 105 m from the flight level, data close to the flight level are obtained through interpolation. Figures 2c and 2d show lidar backscatter power (plotted as 10 log10) from WCL I and WCL II, respectively. The solid white line shown on Fig. 2c denotes the derived cloud boundary for the Fernald backward extinction retrieval. Figures 2e and 2f show the uncalibrated LDR from WCL I and WCL II, respectively. Near flight level, low LDR is caused by perpendicular signal saturation rather than the occurrence of liquid and horizontally oriented ice crystals. The difference in scale for WCL I and WCL II measurements is due to their different technical specifications such as, laser power and system optical and electrical gains. More details about WCL I and WCL II can be found in Wang et al. (2009). High LDR values and a lack of strong attenuation of lidar signal indicate ice-only clouds. Figure 2g shows the retrieved cloud extinction (plotted as 10 log10) profile from the WCL-I measurements. Similarly, Fig. 2h shows the profile of retrieved IWC. IWC generally decreases from flight altitude toward cloud top. Since IWC is a third moment of ice particle size distribution, it indicates that ice particle size also decreases from the flight level to the cloud top, which is also consistent with the profile in Fig. 2a. Figure 2i shows the comparison of CLH IWC measurements at flight level (black line) and retrieved IWC at ~20 m above the flight level (red line). Although there are range gates closer to the flight level, 20 m was chosen to avoid overlap correction errors at closer ranges. The radar reflectivity at this level was calculated as an average of the values from the first usable range gates from up and down WCR beams. WCR- and WCL-retrieved IWC values compare well with CLH IWC in terms of both the magnitude and the spatial variation.

Fig. 2.
Fig. 2.

An observation and retrieval example on 20 Feb 2011. (a),(b) Radar reflectivity from up- and down-pointing WCR beams. (c),(d) WCL-I and WCL-II backscatter power. The white line in (c) represents lidar cloud-top boundary for extinction retrievals. (e),(f) Uncalibrated LDR. (g) Extinction retrieved from WCL-I measurements plotted in log scale. (h) Radar–lidar-retrieved IWC profile. (i) CLH IWC (black line) at the flight level and retrieved IWC (red line) at 20 m above flight level.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

Figure 3 shows another IWC retrieval case from 20 February 2011. The flight-level temperature during this period is −14.0°C. Relative to the case shown in Fig. 2, there are some major differences. In contrast to Figs. 2c and 2e, Figs. 3c and 3e both show a highly spatially inhomogeneous structure. High values of lidar backscatter power and low LDR values around 2026 and 2028 UTC indicate the occurrence of horizontal ice crystals. Horizontal ice crystals cause large backscatter through specular reflection, which reduces their lidar ratio, but have weak attenuation in lidar signals. If normal lidar ratio is used, it significantly overestimates the extinction and hence the IWC retrieval is biased high. This explains the large discrepancy in the comparison with the CLH IWC. During some periods, retrieved IWC can be 4 times as large as the CLH IWC, as seen in Fig. 3i.

Fig. 3.
Fig. 3.

As in Fig. 2, but for another case on 20 Feb 2011.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

Other than challenges from horizontally oriented ice crystals, the lidar–radar IWC retrieval algorithm is not able to retrieve ice in mixed-phase clouds because of lidar signals being dominated by supercooled liquid droplets and radar signals by ice crystals. Around 2027 UTC in Fig. 3, mixed-phase clouds are present near the flight level. Because of the high concentration of liquid droplets compared to ice particles, the lidar signal attenuates quickly, as highlighted in Fig. 3c. Furthermore, the assumptions of particle size distribution for IWC retrieval does not apply for liquid particles. Therefore, applying the lidar–radar retrieval algorithm in mixed-phase clouds overestimated the retrieved IWC.

However, the lidar–radar algorithm can be effectively applied for an ice layer below the liquid-dominated mixed-phase cloud layer, which is a typical vertical structure of stratiform mixed-phase clouds. Such an example is presented in Fig. 4. The flight-level temperature during this period is −19.0°C. The liquid-dominated mixed-phase clouds near the top are indicated by the high lidar backscatter power and low LDR values, while the ice layer below is evidenced by the low lidar backscatter power and high LDR values. The liquid-dominated mixed-phase layer at the cloud top is generally thinner than 250 m. When the boundary condition is set within or above the mixed-phase cloud layer, the backward solution converges quickly because of high extinction coefficients of the liquid phase. This reduces the uncertainties of retrieved ice cloud extinction coefficients due to the uncertainty in cloud boundary conditions. Although there is a large horizontal inhomogeneity as indicated by cellular structure in measurements, CLH IWC and the retrieved IWC shows a good correlation in general.

Fig. 4.
Fig. 4.

As in Fig. 2, but for a case on 17 Feb 2011.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

2) Statistical analyses

There were nine flights with the up-pointing lidar WCL I during CAMPS. Four of those flights either did not have enough ice cloud measurements or had significant amount of liquid or mixed-phase clouds near the flight level. Cloud transits from the remaining five flights were combined for the statistical comparison between in situ CLH IWC and lidar–radar-retrieved IWC. For the comparison, retrieved IWC is taken from 20 m above the flight level. Cloud transits with identifiable occurrence of horizontal ice crystals, as evidenced by their lidar backscatter and LDR values, were removed from the analysis. Additionally, to avoid liquid or mixed-phase clouds at the flight level, data points with CDP LWC above 1 mg m−3 are excluded. The combined dataset contains nearly 12 000 s of in-cloud measurements (equivalent to 1200 km at an airspeed of 100 m s−1). Most clouds sampled during these flights were precipitating, as are the cases shown in Figs. 24. Both CLH and retrieved IWC were averaged for 5 s to increase the statistical significance of in situ measurements as well as to reduce the effect of cloud spatial inhomogeneity. The data are separated into all ice clouds (similar to the case shown in Fig. 2) and liquid-topped mixed-phase clouds (similar to the case shown in Fig. 4). Since ice clouds and mixed-phase clouds can have different ice particle properties such as size distribution and shape, this partition of the dataset can be useful to better evaluate the IWC retrieval algorithm in different microphysical conditions. Figure 5 shows the scatterplot between the lidar–radar-retrieved IWC and the CLH IWC for ice and liquid-topped mixed-phase clouds. Most data points in both types of clouds are smaller than 50 mg m−3. Both groups show a good correlation between the retrieved and the measured IWC with correlation coefficients around 0.85. The slope of the best-fit line through the origin for the two cases is 1.15 and 0.86, respectively, for ice and liquid-topped mixed-phase clouds. Therefore, the retrieved IWC is within the measurement uncertainty of the in situ instrument, which is 20%. The scatter seen in Fig. 5 is partly due to the different sample volumes between the remote sensing instruments and in situ probes. The vertical resolution of the WCL is 3.75 m while that for WCR is 15 m. Considering different pulse repetition frequencies of WCL and WCR and typical airspeed of UWKA (~100 m s−1), the horizontal resolutions are about 30 m for WCL and about 5–6 m for WCR during CAMPS. Depending on flight conditions, the mass flow rate of the CLH is set at 1–2 standard L min−1 (Davis et al. 2007b).

Fig. 5.
Fig. 5.

Scatterplot between lidar–radar-retrieved IWC vs in situ CLH IWC for (a) all ice clouds and (b) liquid-topped mixed-phase clouds. One-to-one lines are also shown along with the number of data points, correlation coefficient, and the best fit through the origin.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

Figures 6a and 6b show the ratio of the lidar–radar IWC to the CLH IWC as a function of the CLH TWC for all ice clouds and liquid-topped mixed-phase clouds, respectively. The ratios are binned at 5 mg m−3 interval based on the CLH IWC. The ratio of the median (mean) lidar–radar IWC and median (mean) CLH IWC in each bin is shown with a cross (a diamond sign). The median (mean) of all data points for ice clouds is 1.22 (1.26) and for mixed-phase clouds is 0.94 (0.91). This is consistent with the median values reported in Heymsfield et al. (2008), which vary from 0.61 to 1.17 for different radar–lidar approaches. The difference between the retrieved and in situ IWC for ice clouds may be due to the dry bias in the CLH measurements, as discussed in section 2. It is clear that the scatter ranges of the ratios decrease with increasing IWC. Other than the first bin, the median (mean) ratios show a weak dependence on the CLH IWC. The overestimation of retrieved IWC at the first bin (IWC < 5 mg m−3) could be due to the higher uncertainty of CLH measurements at that range.

Fig. 6.
Fig. 6.

Ratio of the retrieved IWC and CLH IWC as a function of the CLH IWC for (a) all ice clouds and (b) liquid-topped mixed-phase clouds. The horizontal line shows the ratio of 1. The ratios of the mean and median of all data points at each bin are also shown.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

Figure 7 shows the frequency distribution function (as a percentage) of the difference between the retrieved and the CLH-measured IWC. The cumulative distribution function is also plotted on the right y axis (blue histogram) as a percentage. Figure 7a shows a very narrow distribution with the mode at 0 for ice clouds. Almost 50% of the data points lie in a very narrow range of ±2.5 mg m−3 of the measured IWC. This distribution is slightly skewed to the right, which means a slight overestimation of the retrieved IWC. On the other hand, Fig. 7b shows a slightly broader distribution for liquid-topped mixed-phase clouds. The mode of the distribution is still centered at the difference of 0. The consistency between the in situ IWC and the lidar–radar-retrieved IWC near the flight level gives us confidence on the accuracy of the WCR–WCL-retrieved vertical IWC profile. The systematic differences between ice and liquid-topped mixed-phase clouds are mainly due to different ice cloud microphysics (size and shape). The details can be further explored to refine our retrieval algorithm in the future for different types of clouds, especially the assumptions about ice particle size distribution, habit, and orientation.

Fig. 7.
Fig. 7.

Frequency distribution function of the difference between the retrieved and CLH IWC for (a) ice and (b) liquid-topped mixed-phase clouds.

Citation: Journal of Applied Meteorology and Climatology 54, 10; 10.1175/JAMC-D-15-0040.1

4. Conclusions

In this study, a lidar–radar IWC retrieval algorithm based on Wang and Sassen (2002) is applied to the WCL and the WCR measurements during CAMPS. The collocated remote sensing and in situ measurements aboard UWKA provide a unique dataset for evaluation studies. It is shown that the presence of horizontally oriented ice crystals in ice clouds affects the retrieved IWC accuracy. Since horizontally oriented ice crystals have strong lidar backscatter signal because of specular reflection, their lidar ratio is small. If a normal lidar ratio is used, then the retrieved extinction coefficient and IWC can be significantly overestimated. However, the occurrence of ice clouds with dominant horizontally oriented ice crystals can be easily identified with WCL measurements. After removing clouds with oriented ice crystals, near flight-level IWC retrieval is compared with the in situ CLH IWC. Statistical analysis showed that the mean difference between the retrieved and the measured IWC to be around 26% for all ice clouds and 9% for liquid-topped mixed-phase clouds. Considering their different measurement techniques and different sample volumes and the uncertainty of the in situ measurements, this is a statistically good agreement. These results indicate that IWC retrieved from the WCL and WCR is reasonably accurate when reliable extinction coefficients are derived from the WCL measurements. The systematic differences between ice and mixed-phase clouds indicate that the forward model is sensitive to ice crystal size and habit assumptions. With collocated WCR, WCL, and in situ measurements, a future plan includes integrating in situ microphysical measurements as a part of the forward model to improve the retrieval of IWC vertical structure with WCR and WCL.

Acknowledgments

This research is supported by the NSF under Award AGS-0964184 and also partially supported by the DOE DE-SC0006974 as part of the ASR program. We thank Dr. Linnea Avallone for organizing CAMPS and for providing the CLH data. We also thank UWKA scientists and crew members who were instrumental in collecting and processing the CAMPS dataset. The authors acknowledge the editor’s and referees’ efforts in improving the manuscript.

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  • Davis, S. M., L. M. Avallone, E. M. Weinstock, C. H. Twohy, J. B. Smith, and G. L. Kok, 2007a: Comparisons of in situ measurements of cirrus cloud ice water content. J. Geophys. Res., 112, D10212, doi:10.1029/2006JD008214.

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    • Export Citation
  • Davis, S. M., A. G. Hallar, L. M. Avallone, and W. E. Engblom, 2007b: Measurement of total water content with a tunable diode laser hygrometer: Inlet analysis, calibration procedure, and ice water content determination. J. Atmos. Oceanic Technol., 24, 463475, doi:10.1175/JTECH1975.1.

    • Search Google Scholar
    • Export Citation
  • Deng, M., G. G. Mace, Z. Wang, and H. Okamoto, 2010: Tropical Composition, Cloud and Climate Coupling Experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar. J. Geophys. Res., 115, D00J15, doi:10.1029/2009JD013104.

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  • Deng, M., G. G. Mace, Z. Wang, and R. P. Lawson, 2013: Evaluation of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign. J. Appl. Meteor. Climatol., 52, 10141030, doi:10.1175/JAMC-D-12-054.1.

    • Search Google Scholar
    • Export Citation
  • Donovan, D. P., and A. C. A. P. van Lammeren, 2001: Cloud effective particle size and water content profile retrievals using combined lidar and radar observations. 1. Theory and examples. J. Geophys. Res., 106, 27 42527 448, doi:10.1029/2001JD900243.

    • Search Google Scholar
    • Export Citation
  • Febvre, G., J.-F. Gayet, V. Shcherbakov, C. Gourbeyre, and O. Jourdan, 2012: Some effects of ice crystals on the FSSP measurements in mixed phase clouds. Atmos. Chem. Phys., 12, 89638977, doi:10.5194/acp-12-8963-2012.

    • Search Google Scholar
    • Export Citation
  • Fernald, F. G., 1984: Analysis of atmospheric lidar observations: Some comments. Appl. Opt., 23, 652653, doi:10.1364/AO.23.000652.

  • Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 20582082, doi:10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2.

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    • Export Citation
  • Gardiner, B. A., and J. Hallett, 1985: Degradation of in-cloud forward scattering spectrometer probe measurements in the presence of ice particles. J. Atmos. Oceanic Technol., 2, 171180, doi:10.1175/1520-0426(1985)002<0171:DOICFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gerber, H., B. G. Arends, and A. S. Ackerman, 1994: New microphysics sensor for aircraft use. Atmos. Res., 31, 235252, doi:10.1016/0169-8095(94)90001-9.

    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and G. A. Isaac, 1997: Liquid water content and temperature relationship from aircraft observations and its applicability to GCMs. J. Climate, 10, 446452, doi:10.1175/1520-0442(1997)010<0446:LWCATR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haimov, S., and A. Rodi, 2013: Fixed-antenna pointing-angle calibration of airborne Doppler cloud radar. J. Atmos. Oceanic Technol., 30, 23202335, doi:10.1175/JTECH-D-12-00262.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 2007: On measurements of small ice particles in clouds. Geophys. Res. Lett., 34, L23812, doi:10.1029/2007GL030951.

  • Heymsfield, A. J., K. M. Miller, and J. D. Spinhirne, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Cloud microstructure. Mon. Wea. Rev., 118, 23132328, doi:10.1175/1520-0493(1990)118<2313:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and Coauthors, 2008: Testing IWC retrieval methods using radar and ancillary measurements with in situ data. J. Appl. Meteor. Climatol., 47, 135163, doi:10.1175/2007JAMC1606.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, G. M., and Coauthors, 1996: The EDOP radar system on the high-altitude NASA ER-2 aircraft. J. Atmos. Oceanic Technol., 13, 795809, doi:10.1175/1520-0426(1996)013<0795:TERSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., Z. Liu, D. Winker, M. Vaughan, V. Noel, L. Bissonnette, G. Roy, and M. McGill, 2006: A simple relation between lidar multiple scattering and depolarization for water clouds. Opt. Lett., 31, 18091811, doi:10.1364/OL.31.001809.

    • Search Google Scholar
    • Export Citation
  • Klett, J. D., 1981: Stable analytical inversion solution for processing lidar returns. Appl. Opt., 20, 211220, doi:10.1364/AO.20.000211.

    • Search Google Scholar
    • Export Citation
  • Knollenberg, R. G., 1981: Techniques for probing cloud microstructure. Clouds, Their Formation, Optical Properties, and Effects, P. V. Hobbs, Ed., Academic Press, 15–91.

  • Korolev, A. V., A. N. Nevzorov, J. W. Strapp, and G. A. Isaac, 1998: The Nevzorov airborne hot-wire LWC–TWC probe: Principle of operation and performance characteristics. J. Atmos. Oceanic Technol., 15, 14951510, doi:10.1175/1520-0426(1998)015<1495:TNAHWL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lance, S., C. A. Brock, D. Rogers, and J. A. Gordon, 2010: Water droplet calibration of the cloud droplet probe (CDP) and in-flight performance in liquid, ice and mixed-phase clouds during ARCPAC. Atmos. Meas. Tech., 3, 16831706, doi:10.5194/amt-3-1683-2010.

    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., and B. A. Baker, 2006: Improvement in determination of ice water content from two-dimensional particle imagery. Part II: Applications to collected data. J. Appl. Meteor., 45, 12911303, doi:10.1175/JAM2399.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., G. Zhang, M. R. Poellot, G. L. Kok, R. McCoy, T. Tooman, A. Fridlind, and A. J. Heymsfield, 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment (MPACE): 1. Observations. J. Geophys. Res., 112, D24201, doi:10.1029/2007JD008633.

    • Search Google Scholar
    • Export Citation
  • McGill, M. J., D. Hlavka, W. Hart, V. S. Scott, J. Spinhirne, and B. Schmid, 2002: Cloud physics lidar: Instrument description and initial measurement results. Appl. Opt., 41, 37253734, doi:10.1364/AO.41.003725.

    • Search Google Scholar
    • Export Citation
  • Okamoto, H., S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa, and H. Kumagai, 2003: An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar. J. Geophys. Res., 108, 4226, doi:10.1029/2001JD001225.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459473, doi:10.1109/TGRS.2002.808301.

    • Search Google Scholar
    • Export Citation
  • Platt, C. M. R., 1981: Remote sounding of high clouds. Part III: Monte Carlo calculations of multiple scattered lidar returns. J. Atmos. Sci., 38, 156167, doi:10.1175/1520-0469(1981)038<0156:RSOHCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pokharel, B., and G. Vali, 2011: Evaluation of collocated measurements of radar reflectivity and particle sizes in ice clouds. J. Appl. Meteor. Climatol., 50, 21042119, doi:10.1175/JAMC-D-10-05010.1.

    • Search Google Scholar
    • Export Citation
  • Sassen, K., 1991: The polarization lidar technique for cloud research: A review and current assessment. Bull. Amer. Meteor. Soc., 72, 18481866, doi:10.1175/1520-0477(1991)072<1848:TPLTFC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schwarzenboeck, A., G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet, 2009: Response of the Nevzorov hot wire probe in clouds dominated by droplet conditions in the drizzle size range. Atmos. Meas. Tech., 2, 779788, doi:10.5194/amt-2-779-2009.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790, doi:10.1175/BAMS-83-12-1771.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2003: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bull. Amer. Meteor. Soc., 84, 579593, doi:10.1175/BAMS-84-5-579.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and K. Sassen, 2001: Cloud type and macrophysical property retrieval using multiple remote sensors. J. Appl. Meteor., 40, 16651683, doi:10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and K. Sassen, 2002: Cirrus cloud microphysical property retrieval using lidar and radar measurements. Part I: Algorithm description and comparison with in situ data. J. Appl. Meteor., 41, 218229, doi:10.1175/1520-0450(2002)041<0218:CCMPRU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., G. M. Heymsfield, L. Li, and A. J. Heymsfield, 2005: Retrieving optically thick ice cloud microphysical properties by using airborne dual-wavelength radar measurements. J. Geophys. Res., 110, D19201, doi:10.1029/2005JD005969.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., P. Wechsler, W. Kuestner, J. French, A. Rodi, B. Glover, M. Burkhart, and D. Lukens, 2009: Wyoming Cloud Lidar: Instrument description and applications. Opt. Express, 17, 13 57613 587, doi:10.1364/OE.17.013576.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and Coauthors, 2012: Single aircraft integration of remote sensing and in situ sampling for the study of cloud microphysics and dynamics. Bull. Amer. Meteor. Soc., 93, 653–668, doi:10.1175/BAMS-D-11-00044.1.

  • Yorks, J. E., D. L. Hlavka, W. D. Hart, and M. J. McGill, 2011: Statistics of cloud optical properties from airborne lidar measurements. J. Atmos. Oceanic Technol., 28, 869883, doi:10.1175/2011JTECHA1507.1.

    • Search Google Scholar
    • Export Citation
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  • Bailey, M. P., and J. Hallett, 2009: A comprehensive habit diagram for atmospheric ice crystals: Confirmation from the laboratory, AIRS II, and other field studies. J. Atmos. Sci., 66, 28882899, doi:10.1175/2009JAS2883.1.

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  • Baker, B., and R. P. Lawson, 2006: In situ observations of the microphysical properties of wave, cirrus and anvil clouds. Part I: Wave clouds. J. Atmos. Sci., 63, 31603185, doi:10.1175/JAS3802.1.

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  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39, 645665, doi:10.1175/1520-0450(2000)039<0645:ODOCHA>2.0.CO;2.

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    • Export Citation
  • Davis, S. M., L. M. Avallone, E. M. Weinstock, C. H. Twohy, J. B. Smith, and G. L. Kok, 2007a: Comparisons of in situ measurements of cirrus cloud ice water content. J. Geophys. Res., 112, D10212, doi:10.1029/2006JD008214.

    • Search Google Scholar
    • Export Citation
  • Davis, S. M., A. G. Hallar, L. M. Avallone, and W. E. Engblom, 2007b: Measurement of total water content with a tunable diode laser hygrometer: Inlet analysis, calibration procedure, and ice water content determination. J. Atmos. Oceanic Technol., 24, 463475, doi:10.1175/JTECH1975.1.

    • Search Google Scholar
    • Export Citation
  • Deng, M., G. G. Mace, Z. Wang, and H. Okamoto, 2010: Tropical Composition, Cloud and Climate Coupling Experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar. J. Geophys. Res., 115, D00J15, doi:10.1029/2009JD013104.

    • Search Google Scholar
    • Export Citation
  • Deng, M., G. G. Mace, Z. Wang, and R. P. Lawson, 2013: Evaluation of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign. J. Appl. Meteor. Climatol., 52, 10141030, doi:10.1175/JAMC-D-12-054.1.

    • Search Google Scholar
    • Export Citation
  • Donovan, D. P., and A. C. A. P. van Lammeren, 2001: Cloud effective particle size and water content profile retrievals using combined lidar and radar observations. 1. Theory and examples. J. Geophys. Res., 106, 27 42527 448, doi:10.1029/2001JD900243.

    • Search Google Scholar
    • Export Citation
  • Febvre, G., J.-F. Gayet, V. Shcherbakov, C. Gourbeyre, and O. Jourdan, 2012: Some effects of ice crystals on the FSSP measurements in mixed phase clouds. Atmos. Chem. Phys., 12, 89638977, doi:10.5194/acp-12-8963-2012.

    • Search Google Scholar
    • Export Citation
  • Fernald, F. G., 1984: Analysis of atmospheric lidar observations: Some comments. Appl. Opt., 23, 652653, doi:10.1364/AO.23.000652.

  • Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 20582082, doi:10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gardiner, B. A., and J. Hallett, 1985: Degradation of in-cloud forward scattering spectrometer probe measurements in the presence of ice particles. J. Atmos. Oceanic Technol., 2, 171180, doi:10.1175/1520-0426(1985)002<0171:DOICFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gerber, H., B. G. Arends, and A. S. Ackerman, 1994: New microphysics sensor for aircraft use. Atmos. Res., 31, 235252, doi:10.1016/0169-8095(94)90001-9.

    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and G. A. Isaac, 1997: Liquid water content and temperature relationship from aircraft observations and its applicability to GCMs. J. Climate, 10, 446452, doi:10.1175/1520-0442(1997)010<0446:LWCATR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haimov, S., and A. Rodi, 2013: Fixed-antenna pointing-angle calibration of airborne Doppler cloud radar. J. Atmos. Oceanic Technol., 30, 23202335, doi:10.1175/JTECH-D-12-00262.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 2007: On measurements of small ice particles in clouds. Geophys. Res. Lett., 34, L23812, doi:10.1029/2007GL030951.

  • Heymsfield, A. J., K. M. Miller, and J. D. Spinhirne, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Cloud microstructure. Mon. Wea. Rev., 118, 23132328, doi:10.1175/1520-0493(1990)118<2313:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and Coauthors, 2008: Testing IWC retrieval methods using radar and ancillary measurements with in situ data. J. Appl. Meteor. Climatol., 47, 135163, doi:10.1175/2007JAMC1606.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, G. M., and Coauthors, 1996: The EDOP radar system on the high-altitude NASA ER-2 aircraft. J. Atmos. Oceanic Technol., 13, 795809, doi:10.1175/1520-0426(1996)013<0795:TERSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., Z. Liu, D. Winker, M. Vaughan, V. Noel, L. Bissonnette, G. Roy, and M. McGill, 2006: A simple relation between lidar multiple scattering and depolarization for water clouds. Opt. Lett., 31, 18091811, doi:10.1364/OL.31.001809.

    • Search Google Scholar
    • Export Citation
  • Klett, J. D., 1981: Stable analytical inversion solution for processing lidar returns. Appl. Opt., 20, 211220, doi:10.1364/AO.20.000211.

    • Search Google Scholar
    • Export Citation
  • Knollenberg, R. G., 1981: Techniques for probing cloud microstructure. Clouds, Their Formation, Optical Properties, and Effects, P. V. Hobbs, Ed., Academic Press, 15–91.

  • Korolev, A. V., A. N. Nevzorov, J. W. Strapp, and G. A. Isaac, 1998: The Nevzorov airborne hot-wire LWC–TWC probe: Principle of operation and performance characteristics. J. Atmos. Oceanic Technol., 15, 14951510, doi:10.1175/1520-0426(1998)015<1495:TNAHWL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lance, S., C. A. Brock, D. Rogers, and J. A. Gordon, 2010: Water droplet calibration of the cloud droplet probe (CDP) and in-flight performance in liquid, ice and mixed-phase clouds during ARCPAC. Atmos. Meas. Tech., 3, 16831706, doi:10.5194/amt-3-1683-2010.

    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., and B. A. Baker, 2006: Improvement in determination of ice water content from two-dimensional particle imagery. Part II: Applications to collected data. J. Appl. Meteor., 45, 12911303, doi:10.1175/JAM2399.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., G. Zhang, M. R. Poellot, G. L. Kok, R. McCoy, T. Tooman, A. Fridlind, and A. J. Heymsfield, 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment (MPACE): 1. Observations. J. Geophys. Res., 112, D24201, doi:10.1029/2007JD008633.

    • Search Google Scholar
    • Export Citation
  • McGill, M. J., D. Hlavka, W. Hart, V. S. Scott, J. Spinhirne, and B. Schmid, 2002: Cloud physics lidar: Instrument description and initial measurement results. Appl. Opt., 41, 37253734, doi:10.1364/AO.41.003725.

    • Search Google Scholar
    • Export Citation
  • Okamoto, H., S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa, and H. Kumagai, 2003: An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar. J. Geophys. Res., 108, 4226, doi:10.1029/2001JD001225.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459473, doi:10.1109/TGRS.2002.808301.

    • Search Google Scholar
    • Export Citation
  • Platt, C. M. R., 1981: Remote sounding of high clouds. Part III: Monte Carlo calculations of multiple scattered lidar returns. J. Atmos. Sci., 38, 156167, doi:10.1175/1520-0469(1981)038<0156:RSOHCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pokharel, B., and G. Vali, 2011: Evaluation of collocated measurements of radar reflectivity and particle sizes in ice clouds. J. Appl. Meteor. Climatol., 50, 21042119, doi:10.1175/JAMC-D-10-05010.1.

    • Search Google Scholar
    • Export Citation
  • Sassen, K., 1991: The polarization lidar technique for cloud research: A review and current assessment. Bull. Amer. Meteor. Soc., 72, 18481866, doi:10.1175/1520-0477(1991)072<1848:TPLTFC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schwarzenboeck, A., G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet, 2009: Response of the Nevzorov hot wire probe in clouds dominated by droplet conditions in the drizzle size range. Atmos. Meas. Tech., 2, 779788, doi:10.5194/amt-2-779-2009.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790, doi:10.1175/BAMS-83-12-1771.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2003: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bull. Amer. Meteor. Soc., 84, 579593, doi:10.1175/BAMS-84-5-579.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and K. Sassen, 2001: Cloud type and macrophysical property retrieval using multiple remote sensors. J. Appl. Meteor., 40, 16651683, doi:10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and K. Sassen, 2002: Cirrus cloud microphysical property retrieval using lidar and radar measurements. Part I: Algorithm description and comparison with in situ data. J. Appl. Meteor., 41, 218229, doi:10.1175/1520-0450(2002)041<0218:CCMPRU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., G. M. Heymsfield, L. Li, and A. J. Heymsfield, 2005: Retrieving optically thick ice cloud microphysical properties by using airborne dual-wavelength radar measurements. J. Geophys. Res., 110, D19201, doi:10.1029/2005JD005969.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., P. Wechsler, W. Kuestner, J. French, A. Rodi, B. Glover, M. Burkhart, and D. Lukens, 2009: Wyoming Cloud Lidar: Instrument description and applications. Opt. Express, 17, 13 57613 587, doi:10.1364/OE.17.013576.

    • Search Google Scholar
    • Export Citation
  • Wang, Z., and Coauthors, 2012: Single aircraft integration of remote sensing and in situ sampling for the study of cloud microphysics and dynamics. Bull. Amer. Meteor. Soc., 93, 653–668, doi:10.1175/BAMS-D-11-00044.1.

  • Yorks, J. E., D. L. Hlavka, W. D. Hart, and M. J. McGill, 2011: Statistics of cloud optical properties from airborne lidar measurements. J. Atmos. Oceanic Technol., 28, 869883, doi:10.1175/2011JTECHA1507.1.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) The overlap function of the WCL I during CAMPS project. The dashed vertical line indicates the overlap of 1, i.e., a full overlap. (b) Original (red line) and overlap-corrected (black line) lidar backscatter profiles at 1731:30 UTC 20 Feb 2011 during CAMPS. (c) The 10-min original and (d) overlap-corrected lidar backscatter profiles (plotted in a base-10 log scale) from the same flight.

  • Fig. 2.

    An observation and retrieval example on 20 Feb 2011. (a),(b) Radar reflectivity from up- and down-pointing WCR beams. (c),(d) WCL-I and WCL-II backscatter power. The white line in (c) represents lidar cloud-top boundary for extinction retrievals. (e),(f) Uncalibrated LDR. (g) Extinction retrieved from WCL-I measurements plotted in log scale. (h) Radar–lidar-retrieved IWC profile. (i) CLH IWC (black line) at the flight level and retrieved IWC (red line) at 20 m above flight level.

  • Fig. 3.

    As in Fig. 2, but for another case on 20 Feb 2011.

  • Fig. 4.

    As in Fig. 2, but for a case on 17 Feb 2011.

  • Fig. 5.

    Scatterplot between lidar–radar-retrieved IWC vs in situ CLH IWC for (a) all ice clouds and (b) liquid-topped mixed-phase clouds. One-to-one lines are also shown along with the number of data points, correlation coefficient, and the best fit through the origin.

  • Fig. 6.

    Ratio of the retrieved IWC and CLH IWC as a function of the CLH IWC for (a) all ice clouds and (b) liquid-topped mixed-phase clouds. The horizontal line shows the ratio of 1. The ratios of the mean and median of all data points at each bin are also shown.

  • Fig. 7.

    Frequency distribution function of the difference between the retrieved and CLH IWC for (a) ice and (b) liquid-topped mixed-phase clouds.

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