Degree Of Riming Research Articles

Turbulence as a Key Driver of Ice Aggregation and Riming in Arctic Low‐Level Mixed‐Phase Clouds, Revealed by Long‐Term Cloud Radar Observations

AbstractTurbulence in clouds is known to enhance particle collision rates, as widely demonstrated for warm rain formation. A similar impact on ice growth processes is expected but a solid observational basis is missing. A statistical analysis of a 15‐month data set of cloud radar observations allows for the first time to quantify the impact of turbulence on ice aggregation and riming in Arctic low‐level mixed‐phase clouds. Increasing eddy dissipation rate (EDR), from below 10−4 to above 10−3 m2 s−3, yields larger ice aggregates, and higher particle concentration, likely caused by increasing fragmentation. In conditions more favorable to riming, higher EDR is associated with dramatically higher particle fall velocities (by up to 125%), under similar liquid water paths, indicative of markedly higher degrees of riming. Our findings thus reveal the key role of turbulence for cold precipitation formation, and highlight the need for an improved understanding of turbulence‐hydrometeor interactions in cold clouds.

Doppler spectra from DWD's operational C-band radar birdbath scan: sampling strategy, spectral postprocessing, and multimodal analysis for the retrieval of precipitation processes

Abstract. This study explores the potential of using Doppler (power) spectra from vertically pointing C-band radar birdbath scans to investigate precipitating clouds above the radar. First, the new birdbath scan strategy for the network of dual-polarization C-band radars operated by the German Meteorological Service (Deutscher Wetterdienst, DWD) is outlined, and a novel spectral postprocessing and analysis method is presented. The postprocessing algorithm isolates the weather signal from non-meteorological contributions in the radar output based on polarimetric attributes, identifies the statistically significant precipitation modes contained in each Doppler spectrum, and calculates characteristics of every precipitation mode as well as multimodal properties that describe the relation among different modes when more than a single mode is identified. To achieve a high degree of automation and flexibility, the postprocessing chain combines classical signal processing with clustering algorithms. Uncertainties in the calculated modal and multimodal properties are estimated from the small variations associated with smoothing the measured radar signal. The analysis of five birdbath scans recorded at different radar sites and for various precipitation conditions delivers reliable profiles of the derived modal and multimodal properties for two snowfall cases and for stratiform precipitation above and below the melting layer. To help identify the dominant precipitation growth mechanism, Doppler spectra from DWD's birdbath scans can be used to retrieve the typical degree of riming for individual snow modes. Here, the automatically identified snow modes span a wide range of riming conditions with estimated rime mass fractions (RMFs) of up to RMF>0.5. The evaluation of Doppler spectra inside the melting layer and for an intense frontal shower, with observed radar reflectivities of up to about 40 dBZ, occasionally shows erroneously identified precipitation modes and spurious results for the calculated higher-order Doppler moments of skewness and kurtosis. Nonetheless, the Doppler spectra from DWD's operational C-band radar birdbath scan provide a detailed view into the precipitating clouds and allow for calculating a high-resolution profile of radar reflectivity, mean Doppler velocity, and spectral width even in intense frontal precipitation.

Triple-frequency radar retrieval of microphysical properties of snow

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.

A Double‐Moment SBU‐YLIN Cloud Microphysics Scheme and Its Impact on a Squall Line Simulation

AbstractA double‐moment version of the SBU‐YLIN cloud microphysical scheme in WRF is introduced. It predicts the mass and number mixing ratios of cloud droplet, rain, cloud ice, and precipitating ice. In addition, a number of physical processes, like rain evaporation, collection between rain and snow are also optimized in the new scheme. The scheme is evaluated and compared with the original one‐moment scheme for a squall line case. We found that the double‐moment approach gives a better representation of rain evaporation, which is critical for the development, morphology, and evolution of the simulated squall line, especially for the enhanced trailing stratiform cloud and leading convective line. The relationship between key microphysical processes and squall line dynamics is investigated to identify the driving mechanisms of the descending rear inflow, cold pool, and slantwise updraft. Furthermore, formation of the transition zone in the simulated squall line strongly depends on the flexible description of ice particle properties, such as size, degree of riming and fall speed.

Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images

Abstract. This paper presents a method named 3D-GAN, based on a generative adversarial network (GAN), to retrieve the total mass, 3D structure and the internal mass distribution of snowflakes. The method uses as input a triplet of binary silhouettes of particles, corresponding to the triplet of stereoscopic images of snowflakes in free fall captured by a multi-angle snowflake camera (MASC). The 3D-GAN method is trained on simulated snowflakes of known characteristics whose silhouettes are statistically similar to real MASC observations, and it is evaluated by means of snowflake replicas printed in 3D at 1:1 scale. The estimation of mass obtained by 3D-GAN has a normalized RMSE (NRMSE) of 40 %, a mean normalized bias (MNB) of 8 % and largely outperforms standard relationships based on maximum size and compactness. The volume of the convex hull of the particles is retrieved with NRMSE of 35 % and MNB of +19 %. In order to illustrate the potential of 3D-GAN to study snowfall microphysics and highlight its complementarity with existing retrieval algorithms, some application examples and ideas are provided, using as showcases the large available datasets of MASC images collected worldwide during various field campaigns. The combination of mass estimates (from 3D-GAN) and hydrometeor classification or riming degree estimation (from independent methods) allows, for example, to obtain mass-to-size power law parameters stratified on hydrometeor type or riming degree. The parameters obtained in this way are consistent with previous findings, with exponents overall around 2 and increasing with the degree of riming.

Orographically Modified Ice-Phase Precipitation Processes During the Olympic Mountains Experiment (OLYMPEX)

AbstractOver mountainous terrain, windward enhancement of stratiform precipitation results from a combination of warm-rain and ice-phase processes. In this study, ice-phase precipitation processes are investigated within frontal systems during the Olympic Mountains Experiment (OLYMPEX). An enhanced layer of radar reflectivity (ZH) above the melting level bright band (i.e., a secondary ZH maximum) is observed over both the windward slopes of the Olympic Mountains and the upstream ocean, with a higher frequency of occurrence and higher ZH values over the windward slopes indicating an orographic enhancement of ice-phase precipitation processes. Aircraft-based in situ observations are evaluated for the 01-02 and 03 December 2015 orographically-enhanced precipitation events. Above the secondary ZH maximum, the hydrometeors are primarily horizontally oriented dendritic and branched crystals. Within the secondary ZH maximum, there are high concentrations of large (> ~2 mm diameter) dendrites, plates, and aggregates thereof, with a significant degree of riming. In both events, aggregation and riming appear to be enhanced within a turbulent layer near sheared flow at the top of a low-level jet impinging on the terrain and forced to rise above the melting level. Based on windward ground sites at low-, mid-, and high-elevations, secondary ZH maxima periods during all of OLYMPEX are associated with increased rain rates and larger mass-weighted mean drop diameters compared to periods without a secondary ZH maximum. This result suggests that precipitation originating from secondary ZH maxima layers may contribute to enhanced windward precipitation accumulations through the formation of large, dense particles that accelerate fallout.

Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Abstract. Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.

Assessing the Effect of Riming on Snow Microphysics: The First Observational Study in East China

AbstractThrough the collision and freezing of supercooled droplets on the surface of snow particles, riming can change the density, fall velocity and aspect ratio of snow particles. Riming plays an important role in the formation of precipitation in cold clouds. To our knowledge, the impact of riming on snow microphysics is yet well understood, and there is a lack of observations obtained in China addressing this issue. For the first time in East China, the connection between riming and snow microphysical properties was investigated quantitatively during snow events in two winters. To quantify the degree of riming, the rime mass fraction (FR) is derived using the combination of a 2‐D video disdrometer (2DVD) and a weighing gauge. FR is added to the density‐diameter and fall velocity‐diameter relations, and a quantitative relation between riming and shapes of snowflakes is established based on the in situ observation of the 2DVD. The results show that riming can well explain the variability in the density and fall velocity of snowflakes. The changes in the shape of snowflakes can be divided into two distinct stages with increasing riming: in the initial stage (FR < 0.5), the aspect ratio increases very slowly, while in the later stage (FR > 0.5), the aspect ratio increases rapidly. Direct observations of the continuous changes in the shapes of snowflakes with riming are in good agreement with the retrieval results of radar.

Method for Classification of Snowflakes Based on Images by a Multi-Angle Snowflake Camera Using Convolutional Neural Networks

AbstractTaking advantage of the recent developments in machine learning, we propose an approach to automatic winter hydrometeor classification based on utilization of convolutional neural networks (CNNs). We describe the development, implementation, and evaluation of a method and tool for classification of snowflakes based on geometric characteristics and riming degree, respectively, obtained using CNNs from high-resolution images by a Multi-Angle Snowflake Camera (MASC). These networks are optimal for image classification of winter precipitation particles due to their high accuracy, computational efficiency, automatic feature extraction, and application versatility. They require little initial preparation, enable the use of smaller training sets through transfer learning techniques, come with large supporting communities and a wealth of resources available, and can be applied and operated by nonexperts. We illustrate both the ease of implementation and the usefulness of operation the CNN architecture offers as a tool for researchers and practitioners utilizing in situ optical observational devices. A training dataset containing 1450 MASC images is developed primarily from two storm events in December 2014 and February 2015 in Greeley, Colorado, by visual inspection of recognizable snowflake geometries. Defined geometric classes are aggregate, columnar crystal, planar crystal, small particle, and graupel. The CNN trained on this dataset achieves a mean accuracy of 93.4% and displays excellent generalization (ability to classify new data). In addition, a separate training dataset is developed by sorting snowflakes into three classes and showcasing distinct degrees of riming. The CNN riming degree estimator yields promising initial results but would benefit from larger training sets.

Measurement of specific surface area of fresh solid precipitation particles in heavy snowfall regions of Japan

Abstract. In countries like Japan, particular solid precipitation particles (PPs), such as unrimed PPs and graupel, often form a weak layer in snow, which triggers slab avalanches. An understanding of weak PP layers is therefore essential for avalanche prevention authorities to design a predictive model for slab avalanches triggered by those layers. Specific surface area (SSA) is a parameter that could characterize the physical properties of PPs. The SSAs of solid PPs in Nagaoka – a city in Japan experiencing the heaviest snowfall in the country – were measured for four winters (from 2013/2014 to 2016/2017). More than 100 SSAs of PP were measured during the study period using the gas absorption method. The measured SSA values range from 42 to 153 m2 kg−1. Under melting conditions, PPs showed comparatively smaller values. Unrimed and slightly rimed PPs exhibited low SSA, whereas heavily rimed PPs and graupel exhibited high SSA. The degree of PP riming depends on the synoptic meteorological conditions. Based on the potential of weak PP layer formation with respect to the degree of riming of PPs, the results indicate that SSA is a useful parameter for describing the characteristics of PP, and consequently predicting avalanches triggered by weak PP layers. The study found that the values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. SSA increases with increase in WS and decreases with increase in Tw. An equation to empirically estimate the SSA of fresh PPs in Nagaoka using WS and Tw was established. The equation successfully reproduced the fluctuation of SSA. The SSA equation, along with the meteorological data, is an efficient first step toward describing the development of weak PP layers in the snow cover models.

The Microphysics of Stratiform Precipitation During OLYMPEX: Compatibility Between Triple‐Frequency Radar and Airborne In Situ Observations

AbstractThe link between stratiform precipitation microphysics and multifrequency radar observables is thoroughly investigated by exploiting simultaneous airborne radar and in situ observations collected from two aircraft during the OLYMPEX/RADEX (Olympic Mountain Experiment/Radar Definition Experiment 2015) field campaign. Above the melting level, in situ images and triple‐frequency radar signatures both indicate the presence of moderately rimed aggregates. Various mass‐size relationships of ice particles and snow scattering databases are used to compute the radar reflectivity from the in situ particle size distribution. At Ku and Ka band, the best agreement with radar observations is found when using the self‐similar Rayleigh‐Gans approximation for moderately rimed aggregates. At W band, a direct comparison is challenging because of the non‐Rayleigh effects and of the probable attenuation due to ice aggregates and supercooled liquid water between the two aircraft. A variational method enables the retrieval of the full precipitation profile above and below the melting layer, by combining the observations from the three radars. Even with three radar frequencies, the retrieval of rain properties is challenging over land, where the integrated attenuation is not available. Otherwise, retrieved mean volume diameters and water contents of both solid and liquid precipitation are in agreement with in situ observations and indicate local changes of the degree of riming of ice aggregates, on the scale of 5 km. Finally, retrieval results are analyzed to explore the validity of using continuity constraints on the water mass flux and diameter within the melting layer in order to improve retrievals of ice properties.

Snowfall retrieval at X, Ka and W bands: consistency of backscattering and microphysical properties using BAECC ground-based measurements

Abstract. Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using co-located ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Ze, measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these combined observations, power-law Ze–S relationships are derived for all three frequencies considering the influence of riming. Using microwave radiometer observations of liquid water path, the measured precipitation is divided into lightly, moderately and heavily rimed snow. Interestingly lightly rimed snow events show a spectrally distinct signature of Ze–S with respect to moderately or heavily rimed snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are performed by using the particle size distribution provided by the in situ video disdrometer and retrieved ice particle masses. The latter are carried out by using both the T-matrix method (TMM) applied to soft-spheroid particle models with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for rimed aggregates. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multi-frequency Ze–S relations if a proper spheroid aspect ratio is selected. The latter may depend on the degree of riming in snowfall. A further analysis of the backscattering simulations reveals that TMM cross sections are higher than the DDA ones for small ice particles, but lower for larger particles. The differences of computed cross sections for larger and smaller particles are compensating for each other. This may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations under study.

Snowflake Melting Simulation Using Smoothed Particle Hydrodynamics

AbstractMotivated by the need to understand the microphysics and improve the remote sensing of the melting layer of precipitation, we have developed a numerical 3‐D model for the melting of single snowflakes. The model uses the smoothed particle hydrodynamics method and is forced by surface tension that controls the flow of meltwater on the ice surface. Heat transfer from the environment to the snowflake is simulated with a Monte Carlo scheme. In model experiments with snowflakes of various sizes and densities, we observed that the meltwater tends to initially gather in concave regions of the snowflake surface. These liquid water regions merge as they grow, and as meltwater is added, they form a shell of liquid around an ice core. This eventually develops into a water drop. The observed features during melting are consistent with experimental findings from earlier research, which suggests that the model is adequate for exploring the physics of snowflake melting. The principal remaining uncertainties arise from the omission of aerodynamic forces from the model. The results suggest that the degree of riming has a significant influence on the melting process: During initial melting, liquid water is apparent on the surface of unrimed or lightly rimed particles, while rime provides a porous structure that can absorb a relatively large amount of meltwater. Riming also strengthens the connections between different parts of the snowflake, making rimed snowflakes less prone to breakup during melting, while unrimed ones break up rather easily.

Evaluation of the Rayleigh–Gans approximation for microwave scattering by rimed snowflakes

We have evaluated the applicability of the Rayleigh–Gans approximation (RGA) and self‐similar Rayleigh–Gans approximation (SSRGA) for microwave scattering by rimed snowflakes. This study extends previous findings that showed that, for unrimed snowflakes, the RGA is in good agreement with the discrete dipole approximation (DDA), which we used as a reference method. When riming is introduced, the RGA‐derived scattering properties of individual snowflakes deviate significantly – up to 20–25 dB for the backscattering cross‐section in the W band – from the corresponding DDA results. In contrast, the average scattering properties given by RGA are in good agreement with DDA for all but the most heavily rimed snowflakes: the mean bias in the backscattering cross‐section rarely exceeds 1 dB for light and moderate riming. We also found that an adjustment that accounts for the non‐spherical shapes of ice crystals can help eliminate the small constant bias found in RGA in earlier studies. The SSRGA approximates the RGA results with good accuracy at all degrees of riming, indicating that it, too, can be used with up to moderately rimed snowflakes.

Evaluation of MRMS Snowfall Products over the Western United States

Abstract Snow is important to water resources and is of critical importance to society. Ground-weather-radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snowpack Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation <5 mm) because of the quality control module in MRMS filtering out weak signals (<5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of −77.37% between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures ranging from −10° to 0°C. A constant reflectivity–SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity–SWE relationships considering the degree of riming and snowflake size.

Microphysical Properties of Snow and Their Link to Ze–S Relations during BAECC 2014

AbstractThis study uses snow events from the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass–dimensional relations are represented in the power-law form m = . It is shown that the retrieved prefactor am and exponent bm react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Ze and snowfall precipitation rate S (Ze = ) are determined. For the studied events, the prefactor of the Ze–S relation varied between 53 and 782 and the exponent was in the range of 1.19–1.61. The dependence of the factors azs and bzs on the m(D) relation and PSD are investigated. The exponent of the Ze–S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor azs depends on both the intercept parameter N0 of the PSD and the prefactors of the m(D) and υ(D) relations. Changes in azs for a given N0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.

Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera

Abstract. A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

Growth of ice particle mass and projected area during riming

Abstract. There is a long-standing challenge in cloud and climate models to simulate the process of ice particle riming realistically, partly due to the unrealistic parameterization of the growth of ice particle mass (m) and projected area (A) during riming. This study addresses this problem, utilizing ground-based measurements of m and ice particle maximum dimension (D) as well as theory to formulate simple expressions describing the dependence of m and A on riming. It was observed that β in the m − D power law m = α Dβ appears independent of riming during the phase 1 (before the formation of graupel), with α accounting for the ice particle mass increase due to riming. This semi-empirical approach accounts for the degree of riming and renders a gradual and smooth ice particle growth process from unrimed ice particles to graupel, and thus avoids discontinuities in m and A during accretional growth. Once the graupel with quasi-spherical shape forms, D increases with an increase in m and A (phase 2 of riming). The treatment for riming is explicit, and includes the parameterization of the ice crystal–cloud droplet collision efficiency (Ec) for hexagonal columns and plates using hydrodynamic theory. In particular, Ec for cloud droplet diameters less than 10 µm are estimated, and under some conditions observed in mixed-phase clouds, these droplets can account for roughly half of the mass growth rate from riming. These physically meaningful yet simple methods can be used in models to improve the riming process.

Discrimination between Winter Precipitation Types Based on Spectral-Bin Microphysical Modeling

AbstractA new approach for distinguishing precipitation types at the surface, the spectral bin classifier (SBC), is presented. This algorithm diagnoses six categories of precipitation: rain (RA), snow (SN), a rain–snow mix (RASN), freezing rain (FZRA), ice pellets (PL), and a freezing rain–ice pellet mix (FZRAPL). It works by calculating the liquid-water fraction fw for a spectrum of falling hydrometeors given a prescribed temperature T and relative humidity profile. Demonstrations of the SBC output show that it provides reasonable estimates of fw of various-sized hydrometeors for the different categories of precipitation. The SBC also faithfully represents the horizontal distribution of precipitation type inasmuch as the model analyses and surface observations are consistent with each other. When applied to a collection of observed soundings associated with RA, SN, FZRA, and PL, the classifier has probabilities of detection (PODs) that range from 62.4% to 98.3%. The PODs do decrease when the effects of model uncertainty are accounted for. This decrease is modest for RA, SN, and PL but is large for FZRA as a result of the fact that this form of precipitation is very sensitive to small changes in the thermal profile. The effects of the choice of the degree of riming above the melting layer, the drop size distribution, and the assumed temperature at which ice nucleates are also examined. Recommendations on how to mitigate all forms of uncertainty are discussed. These include the use of dual-polarized radar observations, incorporating output from the microphysical parameterization scheme, and the use of ensemble model forecasts.

Snow event classification with a 2D video disdrometer — A decision tree approach

Snowfall classification according to crystal type or degree of riming of the snowflakes is import for many atmospheric processes, e.g. wet deposition of aerosol particles. 2D video disdrometers (2DVD) have recently proved their capability to measure microphysical parameters of snowfall. The present work has the aim of classifying snowfall according to microphysical properties of single hydrometeors (e.g. shape and fall velocity) measured by means of a 2DVD. The constraints for the shape and velocity parameters which are used in a decision tree for classification of the 2DVD measurements, are derived from detailed on-site observations, combining automatic 2DVD classification with visual inspection. The developed decision tree algorithm subdivides the detected events into three classes of dominating crystal type (single crystals, complex crystals and pellets) and three classes of dominating degree of riming (weak, moderate and strong). The classification results for the crystal type were validated with an independent data set proving the unambiguousness of the classification. In addition, for three long-term events, good agreement of the classification results with independently measured maximum dimension of snowflakes, snowflake bulk density and surrounding temperature was found. The developed classification algorithm is applicable for wind speeds below 5.0ms-1 and has the advantage of being easily implemented by other users.