Fall Speed Research Articles

Analysis of a Precipitation System that Exists above Freezing Level Using a Multi-Parameter Phased Array Weather Radar

An X-band multi-parameter phased array weather radar (MP-PAWR) was developed in 2017. The scan concept of the MP-PAWR is electronic scanning in elevation by combining fan beam transmissions and pencil beam receptions with digital beam-forming techniques and mechanical scanning along the azimuth. The MP-PAWR realized three-dimensional (60 km in radius and 15 km in height) observations without gaps of 30 s. Although the MP-PAWR is supposed to be suitable for observations of rapidly changing convective systems, it can be advantageous for observations of stratiform rainfall because of continuous vertical pointing observations and its ability to apply the Velocity Azimuth Display (VAD) method with a constant radius for a vertical profile of dynamic parameters such as divergence and deformation. In this study, a precipitation system that existed mainly above the freezing level (in this case, it was approximately 5 km in height) observed from 14:00 to 17:00 Japan Standard Time on 6 September 2018 was analyzed using MP-PAWR data. The averaged area of the vertical profile of Z, and the Doppler velocity with fixed elevation showed a stationary structure with time. The average differential reflectivity factor (ZDR) profile with fixed elevation angles showed values that were close to zero and increased with height. Similar characteristics were shown in the average Specific Differential Phase (KDP) profile. Vertical pointing data, especially for Z, ZDR, and Doppler velocity, were utilized when the echo passed over the radar site, and the Doppler velocity showed the acceleration of fall speed below the freezing level. The vertical profile of divergence with a fixed radius was calculated using the VAD method, and the vertical velocity was calculated using the fall speed profile from the vertical pointing data and by assuming the vertical velocity at the cloud base was zero. The results indicate that the updraft region corresponds to higher ZDR and KDP regions.

The Shape and Density Evolution of Snow Aggregates

Abstract Snow aggregates evolve into a variety of observed shapes and densities. Despite this diversity, models and observational studies employ fractal or Euclidean geometric measures that are assumed universal for all aggregates. This work therefore seeks to improve understanding and representation of snow aggregate geometry and its evolution by characterizing distributions of both observed and Monte Carlo–generated aggregates. Two separate datasets of best-fit ellipsoid estimates derived from Multi-Angle Snowflake Camera (MASC) observations suggest the use of a bivariate beta distribution model for capturing aggregate shapes. Product moments of this model capture shape effects to within 4% of observations. This mathematical model is used along with Monte Carlo simulated aggregates to study how combinations of monomer properties affect aggregate shape evolution. Plate aggregates of any aspect ratio produce a consistent ellipsoid shape evolution whereas thin column aggregates evolve to become more spherical. Thin column aggregates yield fractal dimensions much less than the often-assumed value of 2.0. Ellipsoid densities and fractal analogs of density (lacunarity) are much more variable depending on combinations of monomer size and shape. Simple mathematical scaling relationships can explain the persistent triaxial ellipsoid shapes that appear in both observed and modeled aggregates. Overall, both simulations and observations prove aggregates are rarely oblate. Therefore, the use of the proposed bivariate ellipsoid distribution in models will allow for similar-sized aggregates to exhibit a realistic dispersion of masses and fall speeds.

Impact of nearshore vegetation on coastal dune erosion: assessment through laboratory experiments

The primary objective of this study was to delineate the impact of vegetation on dune erosion under the influence of storm waves. This was achieved through laboratory flume experiments. Specifically, physical model experiments were used to identify the features responsible for coastal dune erosion generated by storm waves under the protection and non-protection of vegetation cover. Observations were then formulated based on the effective variables such as wave height and period, vegetation and sediment characteristics through Buckingham pi theorem to obtain dimensionless parameters like fall speed, vegetation length, depth and wave steepness parameters. Among the dimensionless parameters, the vegetated length parameter was effective in defining, measuring and comparing the coastal dune erosion. Based on vegetated length parameter, the change in erosion volume per unit width was calculated as 9.7–74% for regular wave conditions and 2.9–70.1% for irregular wave conditions. Furthermore, as the vegetated length parameter reduced wave damping ratio was not significantly affected, whereas the increase in the vegetated length parameter increases the wave damping ratio up to 0.5. As a result, the wave attenuation was recorded between 45 and 50%. An approach to correlate the wave energy coming out of the vegetation field with the energy necessary to erode the coastal dune profile depicted a linear correlation. As a result, empirically based equations have been developed to be used in soft coastal defense approaches which predict the volume of dune erosion and the wave attenuation under the protection of emergent vegetation. In addition, the energy required for the evolution of dune erosion is formulated in terms of attenuated wave energy.

The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow

Abstract. The accurate representation of ice particles is essential for both remotely sensed estimates of clouds and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to interrogate the internal structure of aggregate snowflakes and to distinguish more dense and homogeneous rimed particles from aggregates. In this study we investigate which parameters of the morphology and size distribution of ice particles most affect the triple-frequency radar signature and must therefore be accounted for in order to carry out triple-frequency radar retrievals of snow. A range of ice particle morphologies are represented, using a fractal representation for the internal structure of aggregate snowflakes and homogeneous spheroids to represent graupel-like particles; the mass–size and area–size relations are modulated by a density factor. We find that the particle size distribution (PSD) shape parameter and the parameters controlling the internal structure of aggregate snowflakes both have significant influences on triple-frequency radar signature and are at least as important as that of the density factor. We explore how these parameters may be allowed to vary in order to prevent triple-frequency radar retrievals of snow from being over-constrained, using two case studies from the Biogenic Aerosols – Effects of Clouds and Climate (BAECC) 2014 field campaign at Hyytiälä, Finland. In a case including heavily rimed snow followed by large aggregate snowflakes, we show that triple-frequency radar measurements provide a strong constraint on the PSD shape parameter, which can be estimated from an ensemble of retrievals; however, resolving variations in the PSD shape parameter has a limited impact on estimates of snowfall rate from radar. Particle density is more effectively constrained by the Doppler velocity than triple-frequency radar measurements, due to the strong dependence of particle fall speed on density. Due to the characteristic signatures of aggregate snowflakes, a third radar frequency is essential for effectively constraining the size of large aggregates. In a case featuring rime splintering, differences in the internal structures of aggregate snowflakes are revealed in the triple-frequency radar measurements. We compare retrievals assuming different aggregate snowflake models against in situ measurements at the surface and show significant uncertainties in radar retrievals of snow rate due to changes in the internal structure of aggregates. The importance of the PSD shape parameter and snowflake internal structure to triple-frequency radar retrievals of snow highlights that the processes by which ice particles interact may need to be better understood and parameterized before triple-frequency radar measurements can be used to constrain retrievals of ice particle morphology.

The terminal-velocity assumption in simulations of long-range ember transport

Ember transport and the subsequent development of spot fires is a significant mode of wildfire spread, particularly in extreme conditions. An important simplifying assumption made in early research into ember transport is the terminal-velocity assumption, in which embers are assumed to always fly at their terminal velocity relative to the wind field. With increases in computational power, it is now possible to directly simulate the atmospheric conditions resulting from wildfires and such simulations can resolve the larger of the turbulent processes involved. Because of the time-scales at which these processes occur, the terminal-velocity assumption may not be justified when modelling ember transport using these simulations. In this study we use a large eddy simulation of a turbulent plume to examine the validity of the terminal-velocity assumption when modelling the long-range transport of non-combusting embers. The results indicate that the use of the terminal-velocity assumption significantly overestimates the density of ember landings at long range, particularly for embers with higher terminal fall speeds.

Spruce representation in zonal woodlands may be overestimated when using pollen spectra from peatlands

The proportion of taxa in a pollen spectrum may not correspond to their proportion in vegetation. Quantitative reconstruction models therefore consider pollen productivities or fall speeds. We argue that azonal presence of spruce, an otherwise zonal tree species, in wetlands may confound the pollen-inferred reconstructions of vegetation cover as well. Based on a large database of vegetation plots from the Western Carpathians, we demonstrate that spruce is the tree species which most frequently colonizes peatlands, more so than alder, whose effect on local pollen spectra has been frequently admitted. Using 73 sequences we further demonstrated significantly greater pollen percentages of spruce of about 20–25% when the sediments contain spruce macrofossils. Finally, we compared the proportions of spruce and beech in modern surface pollen spectra with their real proportions in the surrounding landscape where beech acts as zonal tree. We found that even a small patch of spruce alters the proportion of spruce to beech. By contrast in an open fen we observed pollen spectrum corresponding well with the real proportions of spruce and beech. All these results suggest, contrary to the premises of current models of pollen-based quantitative reconstructions in Central Europe, that fossil pollen counts from wetlands did not underestimate the representation of spruce. Instead, its representation may even be overestimated where it occurred within or in close vicinity of a wetland. We appeal to palynologists to consider macrofossils or stomata in interpretations, and to adjust quantitative reconstruction models for sites that have experienced a woodland history.

Forecasting the 8 May 2017 Severe Hail Storm in Denver, Colorado, at a Convection-Allowing Resolution: Understanding Rimed Ice Treatments in Multimoment Microphysics Schemes and Their Effects on Hail Size Forecasts

AbstractDay-ahead (20–22 h) 3-km grid spacing convection-allowing model forecasts are performed for a severe hail event that occurred in Denver, Colorado, on 8 May 2017 using six different multimoment microphysics (MP) schemes including: the Milbrandt–Yau double-moment (MY2), Thompson (THO), NSSL double-moment (NSSL), Morrison double-moment graupel (MOR-G) and hail (MOR-H), and Predicted Particle Properties (P3) schemes. Hail size forecasts diagnosed using the Thompson hail algorithm and storm surrogates predict hail coverage. For this case hail forecasts predict the coverage of hail with a high level of skill but underpredict hail size. The storm surrogate updraft helicity predicts the coverage of severe hail with the most skill for this case. Model data are analyzed to assess the effects of microphysical treatments related to rimed ice. THO uses diagnostic equations to increase the size of graupel within the hail core. MOR-G and MOR-H predict small rimed ice aloft; excessive size sorting and increased fall speeds cause MOR-H to predict more and larger surface hail than MOR-G. The MY2 and NSSL schemes predict large, dense rimed ice particles because both schemes predict separate hail and graupel categories. The NSSL scheme predicts relatively little hail for this case; however, the hail size forecast qualitatively improves when the maximum size of both hail and graupel is considered. The single ice category P3 scheme only predicts dense hail near the surface while above the melting layer large concentrations of low-density ice dominate.

The Fall Speed Variability of Similarly Sized Ice Particle Aggregates

AbstractThe terminal velocity (Vt) of ice hydrometeors is of high importance to atmospheric modeling. Vt is governed by the physical characteristics of a hydrometeor, including mass and projected area, as well as environmental conditions. When liquid hydrometeors coalesce to form larger hydrometeors, the resulting hydrometeor can readily be characterized by its spherical or near-spherical shape. For ice hydrometeors, it is more complicated because of the variability of ice shapes possible in the atmosphere as well as the inherent randomness in the aggregation process, which leads to highly variable characteristics. The abundance of atmospheric processes affecting ice particle dimensional characteristics creates potential for highly variable Vt for ice particles that are predicted or measured to be of the “same size.” In this article we explore the variability of ice hydrometeor Vt both theoretically and through the use of experimental observations. Theoretically, the variability in Vt is investigated by analyzing the microphysical characteristics of randomly aggregated hexagonal shapes. The modeled dimensional characteristics are then compared to aircraft probe measurements to constrain the variability in atmospheric ice hydrometeor Vt. Results show that the spread in Vt can be represented with Gaussian distributions relative to a mean. Variability expressed as the full width at half maximum of the normalized Gaussian probability distribution function is around 20%, with somewhat higher values associated with larger particle sizes and warmer temperatures. Field campaigns where mostly convective clouds were sampled displayed low variability, while Arctic and midlatitude winter campaigns showed broader Vt spectra.

Possible roles of fall speed parameters of different graupel densities on microphysics and electrification in an idealized thunderstorm

AbstractGraupel is often parametrized as “medium‐density” ice particles with a bulk density of 400 kg m−3 and corresponding fixed fall speed parameters in numerical models. In natural clouds, however, graupel has an extensive range of densities, and its fall speed is closely related to its density ρg. In this study, the possible responses of the microphysical and electrical structures of a simulated thunderstorm to varying ρg and its corresponding fall speed parameters were examined using the Advanced Research Weather and Forecasting Model (ARW‐WRF) with an explicit charging and discharge lightning scheme. Six sensitivity tests were performed with different ρg and corresponding fall speed parameters. ρg ranges from 350–850 kg m−3, to represent relatively low‐ (350, 450 kg m−3), medium‐ (550, 650 kg m−3), and high‐ (750, 850 kg m−3) density assumptions. In low‐density cases, the ice water path (IWP) could be comparable with the liquid water path (LWP), while the LWP exceeds the contribution of the IWP to the total water path (TWP) in high‐density cases. The results show that melting rates and precipitation were enhanced when ρg was increased from low to high values, resulting in a smaller size and lighter mean mass due to a shorter residence time and faster fall speed.Different assumptions about graupel density also resulted in different electrical structures in the simulated clouds. The clouds produced in the low‐ and medium‐density cases are mainly charged with conventional tripole or positive dipole structures, while the ones formed in the high‐density cases present “bottom‐heavy” tripole structures. The upper positive regions become weaker, with reduced negative noninductive charging rates, as graupel falls faster and less graupel is negatively charged at higher altitude. It is also found that a faster fall speed of graupel does not necessarily mean stronger flash density, although lightning activities are correlated with higher fall speed.

The Relative Impact of Ice Fall Speeds and Microphysics Parameterization Complexity on Supercell Evolution

Abstract The use of bin or bulk microphysics schemes in model simulations frequently produces large changes in the simulated storm and precipitation characteristics, but it is still unclear which aspects of these schemes give rise to these changes. In this study, supercell simulations using either a bin or a double-moment bulk microphysics scheme are conducted with the Regional Atmospheric Modeling System (RAMS). The two simulations produce very different storm morphologies. An additional simulation is run for each scheme in which the diameter–fall speed relationships for ice hydrometeors are modified to be similar to those used by the other scheme. When fall speed relationships are homogenized, the two parameterization schemes simulate similar storm morphology. Therefore, despite the use of largely dissimilar approaches to parameterizing microphysics, the difference in storm morphology is found to be related to the choice of diameter–fall speed relationships for ice hydrometeors. This result is investigated further to understand why. Higher fall speeds lead to higher mixing ratios of hydrometeors at low levels and thus more melting. Consequently, stronger downdrafts and cold pools exist in the high fall speed storms, and these stronger cold pools lead to storm splitting and the intensification of a left mover. The results point to the importance of hydrometeor fall speed on the evolution of supercells. It is also suggested that caution be used when comparing the response of a cloud model to different classes of microphysics schemes since the assumptions made by the schemes may be more important than the scheme class itself.

Raindrop shapes and fall velocities in “turbulent times”

Abstract. Raindrop shapes and fall velocities measured by 2-dimensional video disdrometer are presented for 2 high-wind/turbulent events. The shapes were reconstructed using a relatively new technique. 10 m height wind sensor data are used to derive proxy-indicators for turbulent intensities. Our results show strong gusts, directional wind shifts (i.e. shear) and/or inferred high turbulence intensity are correlated with reduced fall speeds, reaching values ∼25 %–30 % less than the expected values, i.e. sub-terminal fall speeds. Significant percentage (20 %–35 %) of asymmetric drops (> 2 mm) deviating from the most probable axisymmetric shapes were also detected for some events with high turbulent intensities.

Salt comets in hand sanitizer: A simple probe of microgel collapse dynamics

A single grain of salt, placed on the surface of everyday hand sanitizer, slowly bores a hole through it, leaving a milky ``comet trail'' in its wake. The fall speed does not depend on the size of the salt grain, but does depend on salt type, and reveals subtle aspects of collapsing microgels.

Estimation of Snowfall Properties at a Mountainous Site in Norway Using Combined Radar and In Situ Microphysical Observations

AbstractThe ability of in situ snowflake microphysical observations to constrain estimates of surface snowfall accumulations derived from coincident, ground-based radar observations is explored. As part of the High-Latitude Measurement of Snowfall (HiLaMS) field campaign, a Micro Rain Radar (MRR), Precipitation Imaging Package (PIP), and Multi-Angle Snow Camera (MASC) were deployed to the Haukeliseter Test Site run by the Norwegian Meteorological Institute during winter 2016/17. This measurement site lies near an elevation of 1000 m in the mountains of southern Norway and houses a double-fence automated reference (DFAR) snow gauge and a comprehensive set of meteorological observations. MASC and PIP observations provided estimates of particle size distribution (PSD), fall speed, and habit. These properties were used as input for a snowfall retrieval algorithm using coincident MRR reflectivity measurements. Retrieved surface snowfall accumulations were evaluated against DFAR observations to quantify retrieval performance as a function of meteorological conditions for the Haukeliseter site. These analyses found differences of less than 10% between DFAR- and MRR-retrieved estimates over the field season when using either PIP or MASC observations for low wind “upslope” events. Larger biases of at least 50% were found for high wind “pulsed” events likely because of sampling limitations in the in situ observations used to constrain the retrieval. However, assumptions of MRR Doppler velocity for mean particle fall speed and a temperature-based PSD parameterization reduced this difference to +16% for the pulsed events. Although promising, these results ultimately depend upon selection of a snowflake particle model that is well matched to scene environmental conditions.

Relative sensitivities of simulated rainfall to fixed shape parameters and collection efficiencies

AbstractRainfall prediction by weather forecasting models is strongly dependent on the microphysical parametrization being utilized within the model. As forecasting models have become more advanced, they are more commonly using double‐moment bulk microphysical parametrizations. While these double‐moment schemes are more sophisticated and require fewer a priori parameters than single‐moment parametrizations, a number of parameter values must still be fixed for quantities that are not Prognosed or diagnosed. Two such parameters, the width of the rain drop size distribution and the choice of collection efficiencies between liquid hydrometeors, are examined here. Simulations of deep convective storms were performed in which the collection efficiency dataset and the a priori width of the rain drop size distribution (RSD) were individually and simultaneously modified. Analysis of the results show that the a priori width of the RSD was a larger control on the total accumulated precipitation (a change of up to 75% over the typical values tested in this article) than the choice of collection efficiency dataset used (a change of up to 10%). Changing the collection efficiency dataset produces most of the impacts on precipitation rates through changes in the warm rain process rates. On the other hand, the decrease in precipitation with narrowing RSDs occurs in association with the following processes: (a) decreased rain production due to increased evaporation, (b) decreased rain production due to decreased ice melting, and (c) slower raindrop fall speed which leads to longer residency times and changes in rain self‐collection. These results add to the growing body of work showing that the representation of hydrometeor size distributions is critically important, and suggests that more work should be done to better represent the width of the RSD in models, including further development of triple‐moment and bin schemes.

Assessing the Influence of Microphysical and Environmental Parameter Perturbations on Orographic Precipitation

AbstractMicrophysical (MP) schemes contain parameters whose values can impact the amount and location of forecasted precipitation, and sensitivity is typically explored by perturbing one parameter at a time while holding the rest constant. Although much can be learned from these “one-at-a-time” studies, the results are limited as these methods do not allow for nonlinear interactions of multiple perturbed parameters. This study applies the Morris one-at-a-time (MOAT) method, a robust statistical tool allowing for simultaneous perturbation of numerous parameters, to explore orographic precipitation sensitivity to changes in microphysical and environmental parameters within an environment characteristic of an atmospheric river. Results show parameters associated with snow fall speed coefficient As and density ρs, ice-cloud water collection efficiency (ECI), rain accretion (WRA), relative humidity, zonal wind speed, and surface potential temperature cause the largest influence on simulated precipitation. MP and environmental parameter perturbations can cause precipitation changes of similar magnitude, but results vary by location on the mountain. Different environments are also tested, with As being the most influential MP parameter regardless of environment. Fewer MP parameters influence precipitation in a faster-wind-speed environment, possibly due to the stronger dynamical forcing upwind and different wave dynamics downwind, compared to a slower-wind-speed environment. Finally, perturbing MP parameters within a single scheme can result in precipitation variations of similar magnitude compared to using entirely different microphysics schemes. MOAT results presented in this study have implications for Bayesian parameter estimation methods and stochastic parameterization within ensemble forecasting.

Multiphase plumes in a stratified ambient

We report on experiments of turbulent particle-laden plumes descending through a stratified environment. We show that provided the characteristic plume speed $(B_{0}N)^{1/4}$ exceeds the particle fall speed, where the plume buoyancy flux is $B_{0}$ and the Brunt–Väisälä frequency is $N$, then the plume is arrested by the stratification and initially intrudes at the neutral height associated with a single-phase plume of the same buoyancy flux. If the original fluid phase in the plume has density equal to that of the ambient fluid at the source, then as the particles sediment from the intruding fluid, the fluid finds itself buoyant and rises, ultimately intruding at a height of about $0.58\pm 0.03$ of the original plume height, consistent with new predictions we present based on classical plume theory. We generalise this result, and show that if the buoyancy flux at the source is composed of a fraction $F_{s}$ associated with the buoyancy of the source fluid, and a fraction $1-F_{s}$ from the particles, then following the sedimentation of the particles, the plume fluid intrudes at a height $(0.58+0.22F_{s}\pm 0.03)H_{t}$, where $H_{t}$ is the maximum plume height. This is key for predictions of the environmental impact of any material dissolved in the plume water which may originate from the particle load. We also show that the particles sediment at their fall speed through the fluid below the maximum depth of the plume as a cylindrical column whose area scales as the ratio of the particle flux at the source to the fall speed and concentration of particles in the plume at the maximum depth of the plume before it is arrested by the stratification. We demonstrate that there is negligible vertical transport of fluid in this cylindrical column, but a series of layers of high and low particle concentration develop in the column with a vertical spacing which is given by the ratio of the buoyancy of the particle load and the background buoyancy gradient. Small fluid intrusions develop at the side of the column associated with these layers, as dense parcels of particle-laden fluid convect downwards and then outward once the particles have sedimented from the fluid, with a lateral return flow drawing in ambient fluid. As a result, the pattern of particle-rich and particle-poor layers in the column gradually migrates upwards owing to the convective transport of particles between the particle-rich layers superposed on the background sedimentation. We consider the implications of the results for mixing by bubble plumes, for submarine blowouts of oil and gas and for the fate of plumes of waste particles discharged at the ocean surface during deep-sea mining.

On the Relative Sensitivity of a Tropical Deep Convective Storm to Changes in Environment and Cloud Microphysical Parameters

Abstract Monte Carlo and Morris screening techniques are used to examine the relative sensitivity of deep convective simulations to changes in initial conditions (IC) versus changes to microphysical parameters (MP). IC are perturbed using a set of empirical orthogonal function–principal component pairs obtained from a database of tropical soundings, while MP are perturbed consistent with their range of realistic values. Monte Carlo experiments provide a broad overview of parameter–output response, while Morris screening techniques identify the most significant influences on specific model output variables. Changes to MP produce a similar order-of-magnitude response in convective hydrologic cycle, dynamics, and latent heating as changes to IC. Changes in IC appear to have a larger effect on radiative fluxes than perturbations to MP. The distribution of low-level latent heating reveals that changes in MP have a larger influence on cold pool properties than changes to the environment. The dominant effects are produced by a subset of cloud MP and thermodynamic structure functions, indicating perturbation of a subset of the control factors may be used to produce most of the variability in a short-term convective-scale ensemble forecast. The most influential MP are the autoconversion threshold, the rain particle size distribution intercept, and the ice particle fall speed parameters. The most influential EOFs are those that correspond to variability in lower- to midtropospheric temperature and water vapor, as well as zonal low-level shear. The results have implications for both the understanding of what influences convective development and the design of ensemble-based prediction and data assimilation systems.

Dual-wavelength radar technique development for snow rate estimation: a case study from GCPEx

Abstract. quantitative precipitation estimation (QPE) of snowfall has generally been expressed in power-law form between equivalent radar reflectivity factor (Ze) and liquid equivalent snow rate (SR). It is known that there is large variability in the prefactor of the power law due to changes in particle size distribution (PSD), density, and fall velocity, whereas the variability of the exponent is considerably smaller. The dual-wavelength radar reflectivity ratio (DWR) technique can improve SR accuracy by estimating one of the PSD parameters (characteristic diameter), thus reducing the variability due to the prefactor. The two frequencies commonly used in dual-wavelength techniques are Ku- and Ka-bands. The basic idea of DWR is that the snow particle size-to-wavelength ratio is falls in the Rayleigh region at Ku-band but in the Mie region at Ka-band. We propose a method for snow rate estimation by using NASA D3R radar DWR and Ka-band reflectivity observations collected during a long-duration synoptic snow event on 30–31 January 2012 during the GCPEx (GPM Cold-season Precipitation Experiment). Since the particle mass can be estimated using 2-D video disdrometer (2DVD) fall speed data and hydrodynamic theory, we simulate the DWR and compare it directly with D3R radar measurements. We also use the 2DVD-based mass to compute the 2DVD-based SR. Using three different mass estimation methods, we arrive at three respective sets of Z–SR and SR(Zh, DWR) relationships. We then use these relationships with D3R measurements to compute radar-based SR. Finally, we validate our method by comparing the D3R radar-retrieved SR with accumulated SR directly measured by a well-shielded Pluvio gauge for the entire synoptic event.

Dependence of Vertical Alignment of Cloud and Precipitation Properties on Their Effective Fall Speeds

AbstractThe vertical structure of clouds unresolved in large‐scale weather prediction and climate models is controlled by an overlap assumption. When a binary representation (cloud or no cloud) of subgrid horizontal variability is replaced by a probability density function (PDF) treatment of cloud‐related variables, a cloud occurrence overlap needs to be replaced by a PDF overlap. The PDF overlap can be quantified by a correlation length scale, z0, indicating how rapidly rank correlation of distributions at two levels diminishes with increasing level separation. In this study, we show that z0 varies widely for different properties (e.g., number and mass mixing ratios) and different hydrometeor types (cloud liquid and ice, rain, snow, and graupel) and that corresponding fall speed, Vf, is the primary factor controlling the degree of their vertical alignment, with vertical shear of the horizontal wind playing a smaller role. Linear and power law parametric relationships between z0 and Vf are derived using cloud‐resolving simulations of convection under midlatitude continental and tropical oceanic conditions, as well as observations from vertically pointing dual‐frequency radar profilers near Darwin, Australia. The functional form of z0‐Vf relationship is further examined using simple conceptual models that link variability in horizontal and vertical directions and provide insights into the role of Vf and wind shear. Being based on a physical property (i.e., fall speed) of hydrometeors rather than artificially defined and model‐specific hydrometeor types, the proposed parameterization of vertical PDF overlap can be applied to a wide range of microphysics treatments in regional and global models.

Cloud‐Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties

AbstractIn this second part of a cloud microphysics scheme intercomparison study, we focus on biases and variabilities of stratiform precipitation properties for a midlatitude squall line event simulated with a cloud‐resolving model implemented with eight cloud microphysics schemes. Most of the microphysics schemes underestimate total stratiform precipitation, mainly due to underestimation of stratiform precipitation area. All schemes underestimate the frequency of moderate stratiform rain rates (2–6 mm/hr), which may result from low‐biased ice number and mass concentrations for 0.2–2‐mm diameter particles in the stratiform ice region. Most simulations overestimate ice water content (IWC) at altitudes above 7 km for temperatures colder than −20 °C but produce a decrease of IWC approaching the melting level, which is opposite to the trend shown by in situ observations. This leads to general underestimations of stratiform IWC below 5‐km altitude and rainwater content above 1‐km altitude for a given rain rate. Stratiform precipitation area positively correlates with the convective condensate detrainment flux but is modulated by hydrometeor type, size, and fall speed. Stratiform precipitation area also changes by up to 17%–25% through alterations of the lateral boundary condition forcing frequency. Stratiform precipitation, rain rate, and area across the simulations vary by a factor of 1.5. This large variability is primarily a result of variability in the stratiform downward ice mass flux, which is highly correlated with convective condensate horizontal detrainment strength. The variability of simulated local microphysical processes in the stratiform region plays a secondary role in explaining variability in simulated stratiform rainfall properties.