Radar Sampling Volume Research Articles

Detection of small drizzle droplets in a large cloud chamber using ultrahigh-resolution radar

Abstract. A large convection–cloud chamber has the potential to produce drizzle-sized droplets, thus offering a new opportunity to investigate aerosol–cloud–drizzle interactions at a fundamental level under controlled environmental conditions. One key measurement requirement is the development of methods to detect the low-concentration drizzle drops in such a large cloud chamber. In particular, remote sensing methods may overcome some limitations of in situ methods. Here, the potential of an ultrahigh-resolution radar to detect the radar return signal of a small drizzle droplet against the cloud droplet background signal is investigated. It is found that using a small sampling volume is critical to drizzle detection in a cloud chamber to allow a drizzle drop in the radar sampling volume to dominate over the background cloud droplet signal. For instance, a radar volume of 1 cubic centimeter (cm3) would enable the detection of drizzle embryos with diameter larger than 40 µm. However, the probability of drizzle sampling also decreases as the sample volume reduces, leading to a longer observation time. Thus, the selection of radar volume should consider both the signal power and the drizzle occurrence probability. Finally, observations from the Pi Convection–Cloud Chamber are used to demonstrate the single-drizzle-particle detection concept using small radar volume. The results presented in this study also suggest new applications of ultrahigh-resolution cloud radar for atmospheric sensing.

Particle inertial effects on radar Doppler spectra simulation

Abstract. Radar Doppler spectra observations provide a wealth of information about cloud and precipitation microphysics and dynamics. The interpretation of these measurements depends on our ability to simulate these observations accurately using a forward model. The effect of small-scale turbulence on the radar Doppler spectra shape has been traditionally treated by implementing the convolution process on the hydrometeor reflectivity spectrum and environmental turbulence. This approach assumes that all the particles in the radar sampling volume respond the same to turbulent-scale velocity fluctuations and neglects the particle inertial effect. Here, we investigate the inertial effects of liquid-phase particles on the forward modeled radar Doppler spectra. A physics-based simulation (PBS) is developed to demonstrate that big droplets, with large inertia, are unable to follow the rapid change of the velocity field in a turbulent environment. These findings are incorporated into a new radar Doppler spectra simulator. Comparison between the traditional and newly formulated radar Doppler spectra simulators indicates that the conventional simulator leads to an unrealistic broadening of the spectrum, especially in a strong turbulent environment. This study provides clear evidence to illustrate the droplet inertial effect on radar Doppler spectrum and develops a physics-based simulator framework to accurately emulate the Doppler spectrum for a given droplet size distribution (DSD) in a turbulence field. The proposed simulator has various potential applications for the cloud and precipitation studies, and it provides a valuable tool to decode the cloud microphysical and dynamical properties from Doppler radar observation.

Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000–850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area.

Application of Nonuniform Beam Filling (NUBF) Doppler Velocity Error Correction on Airborne Radar Measurements

Non-uniform beam filling (NUBF) within an atmospheric radar sampling volume can cause significant Doppler velocity errors when the radar is located on a fast-moving platform. A case of strong NUBF errors was identified in ER-2 X-band Radar (EXRAD) data from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) 2020 field campaign. A basic NUBF correction was employed using an estimate of the along-track radar reflectivity gradient. The relationship is verified empirically using co-located Doppler velocity measurements from the higher-resolution Cloud Radar System (CRS).

Turbulence kinetic energy dissipation rates estimated from concurrent UAV and MU radar measurements

We tested models commonly used for estimating turbulence kinetic energy dissipation rates varepsilon from very high frequency stratosphere–troposphere radar data. These models relate the root-mean-square value sigma of radial velocity fluctuations assessed from radar Doppler spectra to varepsilon. For this purpose, we used data collected from the middle and upper atmosphere (MU) radar during the Shigaraki unmanned aerial vehicle (UAV)—radar experiment campaigns carried out at the Shigaraki MU Observatory, Japan, in June 2016 and 2017. On these occasions, UAVs equipped with fast-response and low-noise Pitot tube sensors for turbulence measurements were operated in the immediate vicinity of the MU radar. Radar-derived dissipation rates varepsilon estimated from the various models at a range resolution of 150 m from the altitude of 1.345 km up to the altitude of ~ 4.0 km, a (half width half power) beam aperture of 1.32° and a time resolution of 24.6 s, were compared to dissipation rates (varepsilon_{U}) directly obtained from relative wind speed spectra inferred from UAV measurements. Firstly, statistical analysis results revealed a very close relationship between enhancements of sigma and varepsilon_{U} for varepsilon_{U} ,{ gtrsim },10^{ - 5} ,{text{m}}^{2} ,{text{s}}^{ - 3}, indicating that both instruments detected the same turbulent events with varepsilon_{U} above this threshold. Secondly, varepsilon_{U} was found to be statistically proportional to sigma^{3}, whereas a sigma^{2} dependence is expected when the size of the largest turbulent eddies is smaller than the longitudinal and transverse dimensions of the radar sampling volume. The sigma^{3} dependence was found even after excluding convectively generated turbulence in the planetary boundary layer and below clouds. The best agreement between varepsilon_{U} and radar-derived varepsilon was obtained with the simple formulation based on dimensional analysis varepsilon = sigma^{3} /L_{c} where LC ≈ 50–70 m. This empirical expression constitutes a simple way to estimate dissipation rates in the lower troposphere from MU radar data whatever the sources of turbulence be, in clear air or cloudy conditions, consistent with UAV estimates.

Unraveling hydrometeor mixtures in polarimetric radar measurements

Abstract. Radar-based hydrometeor classification typically comes down to determining the dominant type of hydrometeor populating a given radar sampling volume. In this paper we address the subsequent problem of inferring the secondary hydrometeor types present in a volume – the issue of hydrometeor de-mixing. The present study relies on the semi-supervised hydrometeor classification proposed by Besic et al. (2016) but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, oriented towards synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote-sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar hydrometeor classification in Besic et al. (2016), the proposed method, based on the hypothesis of the reduced random interferences of backscattered signals, estimates the proportions of different hydrometeor types in a given radar sampling volume, without considering the neighboring spatial context. Plausibility and performances of the method are evaluated using C- and X-band radar measurements, compared with hydrometeor properties derived from a Multi-Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential residual random interference contribution in the backscattering from different hydrometeors populating a radar sampling volume. This part consists in adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the residual incoherency is found to be limited, justifying the hypothesis of the reduced random interferences even in a case of mixed volumes and confirming the applicability of the proposed bin-based approach, which essentially relies on first-order statistics.

On the forward modeling of radar Doppler spectrum width from LES: Implications for model evaluation.

Large-eddy simulations of an observed single-layer Arctic mixed-phase cloud are analyzed to study the value of forward modeling of profiling millimeter-wave cloud radar Doppler spectral width for model evaluation. Individual broadening terms and their uncertainties are quantified for the observed spectral width and compared to modeled broadening terms. Modeled turbulent broadening is narrower than the observed values when the turbulent kinetic energy dissipation rate from the subgrid-scale model is used in the forward model. The total dissipation rates, estimated with the subgrid-scale dissipation rates and the numerical dissipation rates, agree much better with both the retrieved dissipation rates and those inferred from the power spectra of the simulated vertical air velocity. The comparison of the microphysical broadening provides another evaluative measure of the ice properties in the simulation. To accurately retrieve dissipation rates as well as each broadening term from the observations, we suggest a few modifications to previously presented techniques. First, we show that the inertial subrange spectra filtered with the radar sampling volume is a better underlying model than the unfiltered -5/3 law for the retrieval of the dissipation rate from the power spectra of the mean Doppler velocity. Second, we demonstrate that it is important to filter out turbulence and remove the layer-mean reflectivity-weighted mean fall speed from the observed mean Doppler velocity to avoid overestimation of shear broadening. Finally, we provide a method to quantify the uncertainty in the retrieved dissipation rates, which eventually propagates to the uncertainty in the microphysical broadening.

Statistical Properties of Polarimetric Weather Radar Returns for Nonuniformly Filled Beams

An explicit expression for stochastic processes describing the dual-polarization weather radar echoes is presented. The probability distributions of the proposed model are defined in terms of the point values assumed by polarimetric and Doppler variables in the relevant radar sampling volume. The statistical properties of the model are discussed in order to verify its faithfulness as representative of real radar signals. The discussion considers the most general situation, i.e., it is not related to specific hydrometeor distributions or beam filling conditions.

A Comparison of X-Band Polarization Parameters with In Situ Microphysical Measurements in the Comma Head of Two Winter Cyclones

AbstractSince the advent of dual-polarization radar, methods of classifying hydrometeors by type from measured polarization variables have been developed. The deterministic approach of existing hydrometeor classification algorithms of assigning only one dominant habit to each radar sample volume does not properly consider the distribution of habits present in that volume, however. During the Profiling of Winter Storms field campaign, the “NSF/NCAR C-130” aircraft, equipped with in situ microphysical probes, made multiple passes through the comma heads of two cyclones as the Mobile Alabama X-band dual-polarization radar performed range–height indicator scans in the same plane as the C-130 flight track. On 14–15 February and 21–22 February 2010, 579 and 202 coincident data points, respectively, were identified when the plane was within 10 s (~1 km) of a radar gate. For all particles that occurred for times within different binned intervals of radar reflectivity ZHH and of differential reflectivity ZDR, the reflectivity-weighted contribution of each habit and the frequency distributions of axis ratio and sphericity were determined. This permitted the determination of habits that dominate particular ZHH and ZDR intervals; only 40% of the ZHH–ZDR bins were found to have a habit that contributes over 50% to the reflectivity in that bin. Of these bins, only 12% had a habit that contributes over 75% to the reflectivity. These findings show the general lack of dominance of a given habit for a particular ZHH and ZDR and suggest that determining the probability of specific habits in radar volumes may be more suitable than the deterministic methods currently used.

A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments

AbstractA fuzzy-logic-based hydrometeor classification (HC) method for X-band polarimetric radar (X-pol), which is suitable for observation of solid hydrometeors under moist environments producing little or no hail, is constructed and validated. This HC method identifies the most likely hydrometeor at each radar sampling volume from eight categories: 1) drizzle, 2) rain, 3) wet snow aggregates, 4) dry snow aggregates, 5) ice crystals, 6) dry graupel, 7) wet graupel, and 8) rain–hail mixture. Membership functions are defined on the basis of previous studies. The HC method uses radar reflectivity Zh, differential reflectivity Zdr, specific differential phase Kdp, and correlation coefficient ρhv as its main inputs, and temperature with some consideration of relative humidity as supplemental information. The method is validated against ground and in situ observations of solid hydrometeors (dry graupel, dry snow aggregates, and ice crystals) under a moist environment. Observational data from a ground-based imaging system are used to validate the HC method for dry graupel and dry snow aggregates. For dry snow aggregates and ice crystals, the HC method is validated using simultaneous observations from a balloonborne instrument [hydrometeor videosonde (HYVIS)] and an X-pol range–height indicator directed toward the HYVIS. The HC method distinguishes effectively between dry graupel, dry snow aggregates, and ice crystals, and is therefore valid for HC under moist environments.

Precipitation hydrometeor type relative to the mesoscale airflow in mature oceanic deep convection of the Madden‐Julian Oscillation

AbstractComposite analysis of mature near‐equatorial oceanic mesoscale convective systems (MCSs) during the active stage of the Madden‐Julian Oscillation (MJO) shows where different hydrometeor types occur relative to convective updraft and stratiform midlevel inflow layers. The National Center for Atmospheric Research (NCAR) S‐PolKa radar observed these MCSs during the Dynamics of the Madden‐Julian Oscillation/Atmospheric Radiation Measurement‐MJO Investigation Experiment (DYNAMO/AMIE). NCAR's particle identification algorithm (PID) is applied to S‐PolKa's polarimetric data to identify the dominant hydrometeor type in each radar sample volume. Combining S‐PolKa's Doppler velocity data with the PID demonstrates that hydrometeors have a systematic relationship to the airflow within mature MCSs. In the convective region, moderate rain occurs within the updraft core; the heaviest rain occurs just downwind of the core; wet aggregates occur immediately below the melting layer; narrow zones containing graupel/rimed aggregates occur just downstream of the updraft core at midlevels; dry aggregates dominate above the melting level; and smaller ice particles occur along the edges of the convective zone. In the stratiform region, rain intensity decreases toward the anvil; melting aggregates occur in horizontally extensive but vertically thin regions at the melting layer; intermittent pockets of graupel/rimed aggregates occur atop the melting layer; dry aggregates and small ice particles occur sequentially above the melting level; and horizontally oriented ice crystals occur between −10°C and −20°C in turbulent air above the descending midlevel inflow, suggesting enhanced depositional growth of dendrites. The organization of hydrometeors within the midlevel inflow layer is insensitive to the presence or absence of a leading convective line.

Mixed-phase cloud phase partitioning using millimeter wavelength cloud radar Doppler velocity spectra

Retrieving and quantifying cloud liquid drop contributions to radar returns from mixed-phase clouds remains a challenge because the radar signal is frequently dominated by the returns from the ice particles within the radar sample volume. We present a technique that extracts the weak cloud liquid drop contributions from the total radar returns in profiling cloud radar Doppler velocity spectra. Individual spectra are first decomposed using a continuous wavelet transform, the resulting coefficients of which are used to identify the region in the spectra where cloud liquid drops contribute. By assuming that the liquid contribution to each Doppler spectrum is Gaussian shaped and centered on an appropriate peak in the wavelet coefficients, the cloud liquid drop contribution may be estimated by fitting a Gaussian distribution centered on the velocity of this peak to the original Doppler spectrum. The cloud liquid drop contribution to reflectivity, the volume mean vertical air motion, subvolume vertical velocity variance, and ice particle mean fall speed can be estimated based on the separation of the liquid contribution to the radar Doppler spectrum. The algorithm is evaluated using synthetic spectra produced from output of a state-of-the-art large eddy simulation model study of an Arctic mixed-phase cloud. The retrievals of cloud liquid drop mode reflectivities were generally consistent with the original model values with errors less than a factor of 2. The retrieved volume mean vertical air velocities reproduced the updraft and downdraft structures, but with an overall bias of approximately −0.06 m s−1. Retrievals based on Ka-band Atmospheric Radiation Measurement Program Zenith Radar observations from Barrow, Alaska, during October 2011 are also presented.

Wind Retrieval Algorithms for the IWRAP and HIWRAP Airborne Doppler Radars with Applications to Hurricanes

Abstract Algorithms for the retrieval of atmospheric winds in precipitating systems from downward-pointing, conically scanning airborne Doppler radars are presented. The focus is on two radars: the Imaging Wind and Rain Airborne Profiler (IWRAP) and the High-Altitude IWRAP (HIWRAP). The IWRAP is a dual-frequency (C and Ku bands), multibeam (incidence angles of 30°–50°) system that flies on the NOAA WP-3D aircraft at altitudes of 2–4 km. The HIWRAP is a dual-frequency (Ku and Ka bands), dual-beam (incidence angles of 30° and 40°) system that flies on the NASA Global Hawk aircraft at altitudes of 18–20 km. Retrievals of the three Cartesian wind components over the entire radar sampling volume are described, which can be determined using either a traditional least squares or variational solution procedure. The random errors in the retrievals due to the airborne radar geometry and noise in the Doppler velocities are evaluated using both an error propagation analysis with least squares theory and a numerical simulation of a hurricane. These analyses show that the vertical and along-track wind errors have strong across-track dependence with values ranging from 0.25 m s−1 at nadir to 2.0 and 1.0 m s−1 at the swath edges, respectively. The average across-track wind errors are ~2.5 m s−1 or 7% of the hurricane wind speed. For typical rotated figure-four flight patterns through hurricanes, the zonal and meridional wind speed errors are ~1.5–2.0 m s−1. Evaluations of both retrieval methods show that the variational procedure is generally preferable to the least squares procedure. Examples of measured data retrievals from IWRAP during an eyewall replacement cycle in Hurricane Isabel (2003) and from HIWRAP during the development of Tropical Storm Matthew (2010) are shown. Comparisons of IWRAP-measured data retrievals at nadir to flight-level data show errors of ~2.0 m s−1 for vertical winds and ~4.0 m s−1 for horizontal wind speed (~7% of the hurricane wind speed). Additional sources of error, such as hydrometeor fall speed uncertainties and a small height offset in the comparisons, are likely responsible for the larger vertical wind errors when compared to the simulated error analyses.

Turbulence in Continental Stratocumulus, Part I: External Forcings and Turbulence Structures

Comprehensive, ground-based observations from the US Department of Energy Atmospheric Radiation Measurements program Southern Great Plains site are used to study the variability of turbulence forcings and cloud-scale turbulence structures in a continental stratocumulus cloud. The turbulence observations are made from an upward facing cloud (35 GHz) Doppler radar. Cloud base and liquid water path are characterized using a lidar at the surface and a microwave radiometer. The turbulence characterizations are compared and contrasted with those observed in marine stratocumulus clouds. During the 16-h observation period used in this study the cloud-base and cloud-top heights evolve with time and changes in liquid water path observed by the radiometer are consistent with variations in cloud depth. Unlike marine stratocumulus clouds, a diurnal cycle of cloud thickness and liquid water path is not observed. The observed surface latent, sensible, and virtual sensible heat fluxes and the radiative fluxes exhibit a diurnal cycle with values increasing from sunrise to afternoon and decreasing afterwards. During the night, the sensible heat, virtual sensible heat and the net radiative fluxes at the surface are slightly negative. Solar radiative heating prevails in the cloud layer during the day and strong radiative cooling exists at cloud top even during the day. Unlike marine stratocumulus, surface heating described by the convective velocity scale \(W_\mathrm{s}^{*}\) and cloud-top cooling described by \(W_\mathrm{r}^{*}\) are both important in driving the in-cloud turbulence during the day, whereas cloud-top cooling is the exclusive contributor during the night. The combined \(W_\mathrm{s}^{*}\) and \(W_\mathrm{r}^{*}\) (the total velocity scale \(W_\mathrm{t}^{*})\) provides a useful way to track the evolution of the turbulence structure in the cloud. The variance of the radar-measured radial velocity, which is related to resolved turbulence, follows the diurnal cycle and is consistent with the total velocity scale \(W_\mathrm{t}^{*}\) variations. It is higher during the day and lower during the night, which is contrary to that in marine stratocumulus. The \(W_\mathrm{t}^{*}\) values are lowest around sunset when the radiative cooling is also small due to upper-level clouds observed above the low-level stratus. The vertical distribution of the variance results from the surface heating during the day and cloud-top cooling during the night. The squared spectrum width, which is related to turbulence structures within the radar sampling volume (unresolved turbulence) also follows the diurnal cycle. Its vertical distribution indicates that the unresolved turbulence more closely relates to the processes near cloud top. Turbulence in the cloud requires about an hour to respond to the external forcings of surface heating and cloud-top radiative cooling. Positive skewness prevails during the day and negative skewness prevails at night with a sharp transition around sunset. Resolved turbulence dominates near cloud base whereas unresolved turbulence dominates near cloud top. The turbulence characteristics and variability defined in this study can be used to evaluate the time evolution of turbulence structures in large eddy simulation forced by surface and cloud-top radiative forcings.

Estimating animal densities in the aerosphere using weather radar: To Z or not to Z?

Weather radars provide near‐continuous recording and extensive spatial coverage, which is a valuable resource for biologists, who wish to observe and study animal movements in the aerosphere over a wide range of temporal and spatial scales. Powerful biological inferences can be garnered from radar data that have been processed primarily with the intention of understanding meteorology. However, when seeking to answer certain quantitative biological questions, e.g., those related to density of animals, assumptions made in processing radar data for meteorological purposes interfere with biological inference. In particular, values of the radar reflectivity factor (Z) reported by weather radars are not well suited for biological interpretation. The mathematical framework we present here allows researchers to interpret weather radar data originating from biological scatterers (bioscatterers) without relying on assumptions developed specifically for meteorological phenomena. The mathematical principles discussed are used to interpret received echo power as it relates to bioscatterers. We examine the relationships among measurement error and these bioscatter signals using a radar simulator. Our simulation results demonstrate that within 30–90 km from a radar, distances typical for observing aerial vertebrates such as birds and bats, measurement error associated with number densities of animals within the radar sampling volume are low enough to allow reasonable estimates of aerial densities for population monitoring. The framework presented for using radar echoes for quantifying biological populations observed by radar in their aerosphere habitats enhances use of radar remote‐sensing for long‐term population monitoring as well as a host of other ecological applications, such as studies on phenology, movement, and aerial behaviors.

Effects of Melting Layer in Airborne Meteorological X-Band Radar Observations

Most civil aviation aircraft are equipped with meteorological radar working at the X-band ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</i> ≈ 10 GHz; λ ≈ 3.2 cm) . These radars use a small antenna and, thus, a large beamwidth, around 3°-4°, for observations over long distances (up to 350 km). In the presence of microphysical inhomogeneities inside a radar sampling volume, as filling by different hydrometeor categories, radar reflectivity measurements are biased. An important bias occurs when a radar cell is cut by a nonresolved layer of melting snowflakes whose attenuation is high compared to those of rain and dry snow. This paper illustrates the effects in precipitation-attenuation correction schemes (PACS) of the inhomogeneity associated with the 0 °C isotherm. It proposes a method to take into account these effects in PACS. Adaptation of the method to other frequency bands and ground-based radar observations is easy.

Comparing meteorological spaceborne and ground-based radars: optimal satellite overpass distance from a ground-based radar site

Spaceborne and ground-based radars (GRs) provide a complementary view of the complex precipitation field. As there are significant differences between the two instruments, it is possible to use the peculiarities of one sensor to check the consistency of the results of the other. For instance, the radar measurement is affected by changes in the spatial resolution, which ‘worsens’ with the square of the range. Unfortunately, the GR has to measure rain from small to large distances. In contrast, changes in size of the Tropical Rainfall Measuring Mission (TRMM) radar sampling volume are almost negligible, since its measurements originate from similar distances of 400–420 km. However, the TRMM precipitation radar (TPR) vertical resolution becomes poorer when the angle of incidence increases (i.e. when the distance of the TPR samples from the nadir increases). This article shows that part of this problem, which affects the reference radar (namely the TPR), can be transferred to the radar under investigation (namely the GR), depending on the satellite overpass distance from the GR site. Given the present swath of the TPR (∼240 km), the worst cases occur when the satellite passes close or at the maximum range of the GR. A small influence is found when the satellite passes at approximately half of the GR range. These results, which have been derived for the first-ever spaceborne radar (the TRMM radar), can be easily adapted to future missions (e.g. the global precipitation measuring dual-wavelength radar).

Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications

[1] Several aspects of spectral broadening and drizzle growth in shallow liquid clouds remain not well understood. Detailed, cloud-scale observations of microphysics and dynamics are essential to guide and evaluate corresponding modeling efforts. Profiling, millimeter-wavelength (cloud) radars can provide such observations. In particular, the first three moments of the recorded cloud radar Doppler spectra, the radar reflectivity, mean Doppler velocity, and spectrum width, are often used to retrieve cloud microphysical and dynamical properties. Such retrievals are subject to errors introduced by the assumptions made in the inversion process. Here, we introduce two additional morphological parameters of the radar Doppler spectrum, the skewness and kurtosis, in an effort to reduce the retrieval uncertainties. A forward model that emulates observed radar Doppler spectra is constructed and used to investigate these relationships. General, analytical relationships that relate the five radar observables to cloud and drizzle microphysical parameters and cloud turbulence are presented. The relationships are valid for cloud-only, cloud mixed with drizzle, and drizzle-only particles in the radar sampling volume and provide a seamless link between observations and cloud microphysics and dynamics. The sensitivity of the five observed parameters to the radar operational parameters such as signal-to-noise ratio and Doppler spectra velocity resolution are presented. The predicted values of the five observed radar parameters agree well with the output of the forward model. The novel use of the skewness of the radar Doppler spectrum as an early qualitative predictor of drizzle onset in clouds is introduced. It is found that skewness is a parameter very sensitive to early drizzle generation. In addition, the significance of the five parameters of the cloud radar Doppler spectrum for constraining drizzle microphysical retrievals is discussed.

Using Doppler Spectra to Separate Hydrometeor Populations and Analyze Ice Precipitation in Multilayered Mixed-Phase Clouds

Multimodality of cloud radar Doppler spectra is used to partition cloud particle phases and separate distinct ice populations in the radar sample volume, thereby facilitating the analysis of individual ice showers in multilayered mixed-phase clouds. A 35-GHz cloud radar located at Barrow, Alaska, during the Mixed-Phase Arctic Cloud Experiment collected the Doppler spectra. Data from a pair of collocated depolarization lidars confirmed the presence of two liquid cloud layers reported in this letter. Both of these cloud layers were embedded in ice precipitation yet maintained their liquid. The spectral separation of the ice precipitation yielded two distinct ice populations: the ice initiated within the two liquid cloud layers and the ice precipitation formed in the higher cloud layers. The comparisons of ice fall velocity-versus-radar reflectivity relationships derived for distinct showers reveal that a single relationship does not properly represent the ice showers during this period.

Detection of supercooled liquid in mixed‐phase clouds using radar Doppler spectra

Cloud phase identification from active remote sensors in the temperature range from 0 to −40°C, where both liquid and ice hydrometeor phases are sustainable, is challenging. Millimeter wavelength cloud radars (MMCR) are able to penetrate and detect multiple cloud layers. However, in mixed‐phase conditions, ice crystals dominate the radar signal, rendering the detection of liquid droplets from radar observables more difficult. The technique proposed here overcomes this fundamental limitation by using morphological features in MMCR Doppler spectra to detect supercooled liquid droplets in the radar sampling volume in the presence of ice particles. High lidar backscatter and near‐zero lidar depolarization measurements (good indicators of the presence of liquid droplets) from the Mixed‐Phase Arctic Clouds Experiment (MPACE) conducted in Barrow, Alaska, are used to train the technique and evaluate its potential for detecting mixed‐phase conditions. Ceilometer, microwave radiometer, and radiosonde measurements provide additional independent validation. Because of the ability of MMCRs to penetrate multiple liquid layers, this radar‐based technique does not suffer from the extinction limitations of lidars and is thus able to expand cloud phase identification methods to cloud regions beyond where lidars can penetrate, providing output at the native radar resolution. The technique is applicable to all profiling radars that have sufficient sensitivity to observe the small amount of liquid in mixed‐phase clouds.