Cloud feedbacks are one of the largest uncertainties in global climate model simulations of future climates, limited in part by a lack of observations with sufficient and known accuracy to constrain cloud microphysical parameterizations (Stephens, 2005; IPCC, 2013). Cloud hydrometeor phase is a radiatively important property of clouds (Sun and Shine, 1994; Shupe and Intrieri, 2004; Turner, 2005) that is difficult to accurately model and observe (e.g., Komurcu et al., 2014; Shupe et al., 2008; Cesana and Chepfer, 2013), and is also important for understanding cloud life cycle (Shupe et al., 2008). Quantitative microphysical retrievals make assumptions regarding the phase of cloud properties (ice, liquid, or mixed) before choosing appropriate forward models to use in algorithms (e.g., Zhao et al., 2012). Retrievals of cloud phase are a necessary first step towards improved retrievals of water contents and particle sizes.

The focus of this study is the development of an algorithm that identifies cloud phase from vertically pointing radars and lidars at the ARM (Atmospheric Radiation Measurement) Climate Research Facility (www.arm.gov) that also estimates the uncertainty of that identification. A number of methods have been developed previously to identify cloud phase using similar instrumentation. Shupe (2007) presented an algorithm that uses thresholds of lidar backscatter and depolarization ratio, and three moments of the radar Doppler spectrum (reflectivity, mean Doppler velocity, and spectrum width) along with temperature to classify six different hydrometeor types. A target classification algorithm developed for the CloudNet network of observation sites (Illingworth et al., 2007) uses lidar and radar scattering parameters to flag times when instruments indicate detection of small liquid drops, falling hydrometeors, and melting ice along with temperature information to give likely hydrometeor classifications (Hogan and O'Connor, 2006). Both of these decision-tree methods are based on well-established scientific understanding of instrument sensitivities to hydrometeors, but do not quantify the uncertainty of the phase assignment.

Lidar backscatter, especially paired with depolarization ratio, is a sensitive indicator of the presence of super-cooled liquid water (e.g., Sassen, 1991; Shupe, 2007), which exists at temperatures colder than 0 ∘C. A number of algorithms have been developed for ground- or space-based active sensors that use lidar backscatter thresholds or the attenuation of the lidar (Zhang et al., 2010; Choi et al., 2010; Hogan et al., 2003, 2004; Cesana and Chepfer, 2013) or lidar backscatter and depolarization ratio together (Cesana and Chepfer, 2013; Hogan et al., 2003) to identify liquid clouds.

Lidar data alone have two limitations in identifying cloud phase. First, because lidar data are more sensitive to high concentrations of small liquid droplets than low concentrations of large ice crystals, it may fail to detect mixed-phase conditions. A recent study by Bühl et al. (2013) showed that using lidar measurements alone to detect mixed-phase clouds underestimated the fraction of mixed-phase clouds compared to combined lidar and radar methods when the concentration of ice crystals was very low compared to the number of liquid droplets. The radar wavelength on the other hand is much longer, so the strength of the signal is proportional to the particle diameter to the sixth power and thus is much more sensitive to a few large ice particles.

Another considerable limitation of lidar measurements for phase detection is that lidars attenuate quickly in clouds with an optical depth greater than three so can only be used in optically thin clouds. In order to circumvent this limitation, Luke et al. (2010) trained a neural network on wavelet transforms of the full radar Doppler spectrum to emulate lidar backscatter and depolarization ratio measurements. Though noisier, the radar Doppler spectra generally did have sufficient information content to reproduce the phase information in optically thick clouds that would otherwise not have been retrievable from actual lidar observations. Yu et al. (2014) built on this work and used wavelet transforms to deconvolve liquid peaks in the Doppler spectra from other signals. These studies show that a good deal of information is available within the Doppler spectra to identify liquid within a cloud in addition to the high sensitivity to ice.

The goal of this study is to test the value of two potential improvements to previous decision-tree approaches to operational phase identification algorithms. First, the means and covariances of observational variables are used in a simple Bayesian classifier to estimate the probability that a given phase category describes a particular cloud volume. This gives an estimate of confidence in the phase identification, a first step towards quantifying the uncertainty of microphysical retrievals. Second, additional variables describing the radar Doppler spectra are used to test how much this information improves identification of liquid and mixed-phase cases, particularly in the absence of lidar measurements.

This study describes an algorithm proof of concept, using data from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign when aircraft in situ measurements are available along with vertically pointing lidar and radar measurements to help train and evaluate the algorithm. Section 2 documents the observational data used in the study. The ground-truth data set used to train and validate the algorithm is described in Sect. 3, followed by a description of the algorithm methodology in Sect. 4. Section 5 discusses algorithm validation. Finally, conclusions and a description of additional work needed to create an operational algorithm using these techniques are included in Sect. 6.

The time period of the M-PACE field campaign was chosen because of the simultaneous availability of data from the high spectral resolution lidar (HSRL), millimeter cloud radar (MMCR) Doppler spectra, and aircraft in situ measurements. The M-PACE field campaign took place in the fall of 2004 at the ARM Barrow, Alaska, site (Verlinde et al., 2007). Another advantage of using the M-PACE field campaign is that a number of studies have already been done interpreting M-PACE aircraft data (e.g., Klein et al., 2009; McFarquhar et al., 2007; Verlinde et al., 2013; Morrison et al., 2009), facilitating a quicker identification of a truth data set to train and test the algorithm.

In order to develop a phase detection algorithm with uncertainty estimates, some data of known phase must be available for training the algorithm. Four classifications were chosen in this study because sufficient data for these cases could be identified based on expert knowledge and in situ aircraft measurements. The time and height periods defining test data for each of these cases are given in Table 1, and the details of how they were chosen is described below. Though these categories describe a large fraction of the cloud phases observed during M-PACE, they are not exhaustive, which will be discussed in more detail in Sect. 5.

In situ measurements are particularly important for identifying known mixed-phase cases, with both ice and liquid present in a cloud volume, as these are harder to identify correctly than cloud volumes with a single hydrometeor type. During M-PACE several days of persistent, single-layer mixed-phase clouds were sampled by the University of North Dakota Citation aircraft (Verlinde et al., 2007). Measurements from multiple cloud probes were merged and processed by McFarquhar and Zhang (2007) using the method described in their paper (McFarquhar et al., 2007). The mixed-phase and snow-training data in this study are obtained from 9 October 2004. This case has been studied extensively with aircraft and ground-based remote sensing instruments indicating a long-lived single-layer mixed-phase cloud with a thin liquid layer top and large ice particles falling out the base of the cloud (Forbes and Ahlgrimm, 2014; Klein et al., 2009; McFarquhar et al., 2007).

Sampling differences between aircraft and remote sensing data introduce a significant challenge in using the in situ measurements to provide truth data for the remote sensing retrievals. For example, Fig. 3 shows the flight track of the airplane for the mixed-phase case used in this study, showing that the aircraft does not fly directly over the ground-measurement station (yellow pin). This is true in all flights during the M-PACE campaign. Thus, the comparison between aircraft and remote sensing data must be done in a statistical sense rather than a direct point-by-point comparison. On 9 October 2004, a statistical comparison is reasonable because measurements were made in a long-lasting, relatively homogeneous cloud (Verlinde et al., 2007). Profiles of ice water mass fraction as a function of atmospheric temperature from the aircraft in situ measurements (Fig. 4a) show that, at temperatures between -14 and -16 ∘C, the cloud is composed primarily of liquid drops with a small amount of ice present (ice fraction near 0.0). Between -14 and -10 ∘C the cloud contains a substantial mass of both ice and liquid, with a gradual increase in ice mass fraction as temperature increases (altitude decreases). At roughly -10 ∘C the cloud has shifted to primarily ice (ice fraction near 1.0). Here ice fraction is calculated as, Fice=IWC/(LWC+IWC), where IWC is ice water content and LWC liquid water content.

Lidar depolarization ratio, δ, measurements are particularly sensitive to cloud hydrometeor phase. Previous measurements indicate that liquid drops have small linear δ ranging from 0.0–0.09 and ice crystals have values of roughly δ > 0.1 (Luke et al., 2010; Schotland et al., 1971; Shupe, 2007).

Profiles of δ during two different time periods of the aircraft flight (Fig. 4b and c) indicate a shift in δ from ∼ 10-2 to ∼ 1.0 near -10 ∘C, which indicates a shift from mixed phase to ice. The large δ at warmer temperatures indicate aspherical hydrometeors typical of snow or ice, whereas the smaller δ at colder temperatures (higher in the cloud) indicate that spherical liquid droplets dominate the lidar signal. This also corresponds with the aircraft measurements of ice fraction showing mixed-phase conditions. At temperatures around -13.5 ∘C the lidar attenuates and the data higher in the cloud is not used for training the phase algorithm. The lower horizontal line in Fig. 4b and c show a temperature threshold of -9.2 ∘C. At temperatures warmer than this threshold, after 17:00 UTC, hydrometeors were identified as snow as indicated by the white lines plotted in Fig. 5. This threshold was chosen from visual inspection of the depolarization ratios in Fig. 4, and was set to exclude cases that were not dominated by ice. The radar reflectivity measurements (Fig. 5) are relatively large (-10 to +10 dBZ), which indicates the hydrometeors are large, typical of snow. During the same time period, remote sensing data were considered mixed phase when temperatures were colder than -10 ∘C. A mix of high and low depolarization ratios are seen for temperatures colder than -10 ∘C (Fig. 4c), which may indicate the changing mixed-phase conditions that are dominated by ice or liquid as shown in the variability of the aircraft measurements (Fig. 4a). Finally, the time period 00:00–14:00 UTC (shown as a white box in Fig. 5) was also included as mixed phase in order to give sufficient data to train the algorithm. Since the liquid cloud base level occurs at a lower altitude (determined by HSRL extinction) and the depolarization ratios are not as high below the liquid level as later in the day (Fig. 5), no distinction was made between mixed phase and snow in this part of the cloud.

Lidar depolarization measurements are also used to distinguish liquid drops and ice crystals to identify training data cases consisting of all ice or all liquid. We use δ to identify two ice cases to use for training the ice phase for the detection algorithm (Figs. 6 and 7). Both cases have significant depolarization ratios (indicating aspherical hydrometeors with aspect ratios less than 1), extinction coefficients indicative of ice clouds (Young and Vaughan, 2009), and reflectivity values indicating the presence of large particles (Atlas et al., 1995). These factors, along with the macrophysical cloud structure, and the fact that the lidar does not significantly attenuate are typical of cirrus clouds. Additionally, at least part of the cloud on 22 October (Fig. 7) is colder than -38 ∘C, the temperature where hydrometeors freeze by homogenous freezing mechanisms.

The liquid training case used in the algorithm is displayed in the white box in Fig. 8. Several measurements confirm our decision to identify this as a liquid cloud. First, the lidar backscatter reveals that the lidar beam is completely attenuated before reaching the cloud top, indicative of high drop concentrations (∼ 100 cm-3) typically found in Arctic liquid layer clouds (Rangno and Hobbs, 2001; Shupe et al., 2001). Second, the HSRL circular depolarization ratio remains below 0.09, which falls below the δ threshold for identifying liquid layers using lidar measurements. Note the change in δ from < 0.09 to > 1.0 as the below cloud precipitation in Fig. 8 transitions from liquid to ice (or possibly mixed) phase just after 12:00 UTC. Finally, the radar reflectivity during this time period is less than -25 dBZ, indicating hydrometeors are small, also typical of liquid clouds. Aircraft measurements in Arctic non-precipitating liquid clouds show that drop sizes are generally < 9 µm, which correspond to reflectivity less than -20 dBZ (Shupe et al., 2001).

The cloud-phase identification problem was treated as a multivariate statistics classification problem: classifying volumes within the cloud into one of four possible cloud-phase populations: ice, liquid, mixed phase, and snow. Note that at our current resolution, each day of measurement data can have tens of thousands to a million pixels (depending on the occurrence of clouds during the day). For example, the mixed-phase case on 9 October, has about 100 000 pixels of good data. Thus, to create an algorithm that can be run operationally on multiple years of data at multiple sites, we need a solution that is computationally efficient.

Figures 10–13 show the application of the 5- and 10-variable algorithms to the test cases. Posterior probabilities are plotted for each phase category in gray scale in percentages. A phase identification that uses a threshold of 60 % probability to define a phase category is plotted in color.

This paper describes a proof-of-concept cloud-phase identification algorithm for vertically pointing lidar (HSRL) and cloud radar (MMCR) at the Barrow, AK, ARM site. The algorithm uses a simple Bayesian classifier to calculate posterior probabilities that a given cloud volume is made of ice, liquid, snow, or mixed phase (a mixture of ice and liquid hydrometeors). The four cloud-phase categories are assumed to follow multivariate normal probability density functions with parameters robustly estimated from cloud-phase data identified by comparison with aircraft in situ measurements and known properties of the remote sensing data.

This work builds on previous studies such as the phase classifier work done by Shupe (2007), but tests two new approaches. First, calculating Bayesian posterior probabilities gives information on the uncertainty of the phase classification, which is important when comparing observations and models or using the classification in microphysical cloud property retrievals. Second, additional information was included from the MicroARSCL processing of the radar Doppler spectra. By and large, the three radar variables used in the Shupe (2007) algorithm contained sufficient information to classify liquid and ice phase clouds, but had difficulty in distinguishing mixed-phase and snow categories when lidar data were unavailable. Additional variables from the MicroARSCL processing of the radar Doppler spectra improved the ability of the algorithm to distinguish phase-in-cloud volumes without lidar measurements.

This phase classification method has a number of strengths. It is a relatively simple algorithm that is easy to parallelize and run operationally. The method is also very useful for understanding the sensitivity of the results to data input into the algorithm as is seen in the comparison of results using 5- or 10-variable inputs and statistics when lidar measurements are or are not available. It includes the information from the covariance between multiple variables in a seamless way that can handle missing input variables. As long as that missing data are random with respect to phase, the loss of information content is reflected in the phase probability estimates. Since the mean and covariance matrices are trained with operational data, the algorithm also inherently includes random measurement uncertainties in the posterior probability estimates.

The primary limitation of this classification method is that it is only as good as the accuracy and representativeness of the data used to train it. This is of course a primary limitation in any attempt to estimate measurement or retrieval uncertainty, because without a valid standard of truth, there is no way to define uncertainty. This is a particularly difficult problem in cloud remote sensing retrievals. Aircraft in situ measurements are the typical truth data set used to validate remote sensing retrievals, but the challenges associated with collocation of aircraft and remote sensing data as well as sampling issues are not trivial. The proof of concept algorithm shown in this study uses training data for liquid, ice, mixed-phase, and snow cases that could fairly reliably be identified from a combination of aircraft in situ measurements and expert interpretation of remote sensing data. The 4 days used in this study are not representative of all hydrometeor conditions encountered at the Barrow site, however, and to create an operational retrieval additional categories and training data sets are needed. For example, one short period that was identified in the literature as a super-cooled drizzle case (Verlinde et al., 2013) was examined. The 10-variable algorithm identified that period either as liquid or no solution. These were reasonable results given the categories used to train the algorithm, and examination of observational values suggested that drizzle could be retrieved reliably if sufficient training data for known drizzle cases were available.

What is most needed to improve this retrieval algorithm is additional training data that are representative of the span of cloud conditions seen in the atmosphere. The ARM Airborne Carbon Measurements (ARM-ACME-V) field campaign based out of Deadhorse, AK, will have aircraft flights focused over Oliktok Point, AK, where an ARM Mobile Facility (AMF-3) is located, and Barrow, AK, where a fixed ARM site is located, will fly transects between the two locations and create an opportunity to collect more cloud condition data. ACME-V will occur during the summer of 2015 and will provide routine (2–3 flights per week) aircraft in situ cloud measurements over a 3-month period with many over-flights of the two ground sites that will sample the same clouds with lidar and radar instruments. This field experiment has the potential to provide an extensive truth data set to better train and evaluate the cloud-phase algorithm under a wider range of cloud conditions.

In addition, future development is planned to test and train the algorithm at different sites in the mid-latitudes and tropics that may include higher updraft speeds and turbulence, and thus impact the values of radar Doppler spectra variables associated with hydrometeor classes. When a more complete training data set is available that describes the full space of potential atmospheric conditions, sensitivity tests will be performed to evaluate the optimal set of radar Doppler spectra variables that give sufficient accuracy at the lowest computational cost.

All data in this paper are available at the ARM archive, as cited in the references (Eloranta, 2005b; McFarquhar and Zhang, 2007; Johnson and Jensen, 2009; Jensen et al., 2016).