Terminal Fall Velocity Research Articles

PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations

Abstract. Cloud radar Doppler spectra are of particular interest for investigating cloud microphysical processes, such as ice formation, riming and ice multiplication. When hydrometeor types within a cloud radar observation volume have different terminal fall velocities, they can produce individual Doppler spectrum peaks. The peaks of different particle types can overlap and be further broadened and blended by turbulence and other dynamical effects. If these (sub-)peaks can be separated, properties of the underlying hydrometeor populations can potentially be estimated, such as their fall velocity, number, size and to some extent their shape. However, this task is complex and dependent on the operation settings of the specific cloud radar, as well as atmospheric dynamics and hydrometeor characteristics. As a consequence, there is a need for adjustable tools that are able to detect peaks in cloud radar Doppler spectra to extract the valuable information contained in them. This paper presents the synergistic use of two algorithms used for analyzing the peaks in Doppler spectra: PEAKO and peakTree. PEAKO is a supervised machine learning tool that can be trained to obtain the optimal parameters for detecting peaks in Doppler spectra for specific cloud radar instrument settings. The learned parameters can then be applied by peakTree, which is used to detect, organize and interpret Doppler spectrum peaks. The application of the improved PEAKO–peakTree toolkit is demonstrated in two case studies. The interpretation is supported by forward-simulated cloud radar Doppler spectra by the Passive and Active Microwave TRAnsfer tool (PAMTRA), which are also used to explore the limitations of the algorithm toolkit posed by turbulence and the number of spectral averages chosen in the radar settings. From the PAMTRA simulations, we can conclude that a minimum number of n = 20–40 spectral averages is desirable for Doppler spectrum peak discrimination. Furthermore, small liquid peaks can only be reliably separated for eddy dissipation rate values up to approximately 0.0002 m2 s−3 in the simulation setup which we tested here. The first case study demonstrates that the methods work for different radar systems and settings by comparing the results for two cloud radar systems which were operated simultaneously at a site in Punta Arenas, Chile. Detected peaks which can be attributed to liquid droplets agree well between the two systems, as well as with an independent liquid-predicting neural network. The second case study compares PEAKO–peakTree-detected cloud radar Doppler spectrum peaks to in situ observations collected by a balloon-based holographic imager during a campaign in Ny-Ålesund, Svalbard. This case demonstrates the algorithm toolkit's ability to identify different hydrometeor types but also reveals its limitations posed by strong turbulence and a low n. Despite these challenges, the algorithm toolkit offers a powerful means of extracting comprehensive information from cloud radar observations. In the future, we envision PEAKO–peakTree applications on the one hand for interpreting cloud microphysics in case studies. The identification of liquid cloud peaks emerges as a valuable asset, e.g., in studies on cloud radiative effects, in seeder–feeder processes, or for tracing vertical air motions. Furthermore, the computation of the moments for each subpeak enables the tracking of hydrometeor populations and the observation of growth processes along fallstreaks. On the other hand, PEAKO–peakTree applications could be extended to statistical evaluations of longer data sets. Both algorithms are openly available on GitHub, offering accessibility for the scientific community.

The Preliminary Application of Spectral Microphysics in Numerical Study of the Effects of Aerosol Particles on Thunderstorm Development

Progress in numerical models and improved computational capabilities have significantly advanced our comprehension of how aerosol particles impact thunderstorm clouds. Yet, much of this research has focused on employing bulk microphysics models to explain the impacts of aerosol particles acting as cloud condensation nuclei (CCN) on electrical activities in thunderstorm clouds. The bulk thunderstorm models use mean sizes of particles and terminal-fall velocities. This causes calculation deviation in the electrification simulation, which in turn leads to deviations in the simulation of lightning processes. Developing this further, we established a three-dimensional high-resolution cloud–aerosol bin thunderstorm model with electrification and lightning to provide more accurate microphysics and dynamic fields for studying electrical activities. For evaluating the impacts of aerosol particles, specifically CCN, on the properties of continental thunderclouds, aerosols from both clean and polluted continental environments were selected. Cloud simulations indicate that droplets develop a narrower spectrum in polluted continental conditions, and weakened ice crystal growth increases the number of small ice crystals compared to clean conditions. Smaller droplets and ice crystals result in less effective riming and decreased graupel concentration and mass. Consequently, a significant decrease in large ice particles leads to a weakened process of charge separation under conditions of pollution. As a direct result, there is about a 43% reduction in lightning frequency and a delay of approximately 5 min in the lightning process under polluted conditions.

Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models

Abstract. Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to assess due to nonlinear interactions between different components of the WAM. Here we present a fundamentally new approach to the problem by approximating the behavior of a numerical model – here the Icosahedral Nonhydrostatic (ICON) model – through a statistical surrogate model based on universal kriging, a general form of Gaussian process regression, which allows for a comprehensive global sensitivity analysis. The main steps of our analysis are as follows: (i) identify the most important uncertain model parameters and their probability density functions, for which we employ a new strategy dealing with non-uniformity in the kriging process. (ii) Define quantities of interest (QoIs) that represent general meteorological fields, such as temperature, pressure, cloud cover and precipitation, as well as the prominent WAM features, namely the tropical easterly jet, African easterly jet, Saharan heat low (SHL) and intertropical discontinuity. (iii) Apply a sampling strategy with regard to the kriging method to identify model parameter combinations which are used for numerical modeling experiments. (iv) Conduct ICON model runs for identified model parameter combinations over a nested limited-area domain from 28° W to 34° E and from 10° S to 34° N. The simulations are run for August in 4 different years (2016 to 2019) to capture the peak northward penetration of rainfall into West Africa, and QoIs are computed based on the mean response over the whole month in all years. (v) Quantify sensitivity of QoIs to uncertain model parameters in an integrated and a local analysis. The results show that simple isolated relationships between single model parameters and WAM QoIs rarely exist. Changing individual parameters affects multiple QoIs simultaneously, reflecting the physical links between them and the complexity of the WAM system. The entrainment rate in the convection scheme and the terminal fall velocity of ice particles show the greatest effects on the QoIs. Larger values of these two parameters reduce cloud cover and precipitation and intensify the SHL. The entrainment rate primarily affects 2 m temperature and 2 m dew point temperature and causes latitudinal shifts, whereas the terminal fall velocity of ice mostly affects cloud cover. Furthermore, the parameter that controls the evaporative soil surface has a major effect on 2 m temperature, 2 m dew point temperature and cloud cover. The results highlight the usefulness of surrogate models for the analysis of model uncertainty and open up new opportunities to better constrain model parameters through a comparison of the model output with selected observations.

To Study the Effect of Confining Walls on Permeable, Flexible Planktonic Particle using Immersed Boundary Method

Objective: The main goal of the current research was to investigate the influence of a wall-bounded medium on the settling behaviour of flexible planktonic particles. The particles are permeable and immersed in a Newtonian, incompressible, and viscous fluid. Method: This study employed the Immersed Boundary Method to analyze the interaction between the fluid and the flexible planktonic particles. Results: The research findings revealed a notable correlation between the terminal (fall) velocity of the particles and distance amongst the confining walls, referred to as the "wall gap." Specifically, as the wall gap increased, the terminal velocity of the particle also increased. Additionally, the study demonstrated that the degree of distortion experienced by the flexible planktonic particles increased with the expansion of the wall gap. Conclusion: In conclusion, the distance between the confining walls plays a significant role in determining the terminal velocity of the flexible planktonic particles during settling. This study highlights the importance of considering the wall gap as a crucial factor when examining the behaviour of these particles in a wall-bounded medium.

Study on the effect of particle morphology on transient dispersion behavior and ignition of Al[sbnd]Mg alloy powder

Particle morphology has a significant effect on the explosion of energetic dust which includes heat transfer and transient dispersion behavior. This study aimed to investigate the differences in transient dispersion characteristics of Al-Mg alloy powders with different shapes using Particle Image Velocimetry (PIV) and to elucidate their effects on subsequent flame propagation and explosion parameters. The results demonstrated that irregular dust exhibited lower agglomeration and terminal fall velocity (TFV) compared to spherical dust, leading to a higher overall concentration of dust involved in the explosion. In addition, the turbulence intensity in the critical ignition zone of irregular particles was relatively high, resulting in particles having more active motion characteristics during the initial ignition phase. Therefore, irregular dust exhibited more intense flame phenomena and higher explosion parameters. These findings emphasize the importance of considering particle morphology when assessing the risk associated with metal dust explosions.

A fast, physically based model of firebrand transport by bushfire plumes

Spotfires are a major problem in bushfire management, greatly complicate suppression efforts, and contribute to fires breaking control lines. Firebrands are often implicated in structure loss, and in extreme circumstances have been observed to ignite new fires over 30km ahead of the parent fire. Existing prediction techniques do not accommodate the problem of such long-range spotting, and the meteorological and fire conditions that lead to such events are not well understood. We present a computationally inexpensive, physically based model of ember transport within bushfire plumes, with four components: an integral plume model, a model of turbulence within the plume, a probabilistic model of ember transport by the plume, and a model of transport beneath the plume. The predicted ember landing distributions from this model compare satisfactorily to the explicit ember-transport calculations of Thurston et al. (2017) and to observations (Cruz et al., 2012). We examine the sensitivity of the simple model to its input parameters. The 90th percentile of spotting distance increases with increasing fire power and decreases with increasing ember terminal fall velocity and fire radius. Where there is a meteorological inversion or stable layer and the plume updraft is sufficiently strong to carry embers to that height, the stable layer substantially limits the transport distance, with increased height favoring longer-range transport. The effect of wind speed is complex, due to the competing factors of faster horizontal transport but suppressed vertical plume development with stronger winds.

Frozen Hydrometeor Terminal Fall Velocity Dependence on Particle Habit and Riming as Observed by Vertically Pointing Radars

Abstract Vertically pointing Ka-band radar measurements are used to derive fall velocity–reflectivity factor () relations for frozen hydrometeor populations of different habits during snowfall events observed at Oliktok Point, Alaska, and at the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Case study events range from snowfall with highly rimed particles observed during periods with large amounts of supercooled liquid water path (LWP > 320 g m−2) to unrimed snowflakes including instances when pristine planar crystals were the dominant frozen hydrometeor habit. The prefactor a and the exponent b in the observed Vt–Ze relations scaled to the sea level vary in the approximate ranges 0.5–1.4 and 0.03–0.13, respectively (reflectivities are in mm6 m−3 and velocities are in m s−1). The coefficient a values are the smallest for planar crystals (a ∼ 0.5) and the largest (a > 1.2) for particles under severe riming conditions with high LWP. There is no clear distinction between b values for high and low LWP conditions. The range of the observed Vt–Ze relation coefficients is in general agreement with results of modeling using fall velocity–size (υt = αDβ) relations for individual particles found in literature for hydrometeors of different habits, though there is significant variability in α and β coefficients from different studies even for a same particle habit. Correspondences among coefficients in the Vt–Ze relations for particle populations and in the individual particle υt–D relations are analyzed. These correspondences and the observed Vt–Ze relations can be used for evaluating different frozen hydrometeor fall velocity parameterizations in models. Significance Statement Frozen hydrometeor fall velocities influence cloud life cycles and the moisture transport in the atmosphere. The knowledge of these velocities is also needed to enhance remote sensing of snowfall parameters. In this study, the relations between fall velocities and radar reflectivities of snowflakes of different types and shapes are quantitively analyzed using observations with vertically pointing radars.

Improvements to the parametrization of snow in AROME in the context of ice crystal icing

AbstractIce crystal icing (ICI) poses a threat nowadays for airplane pilots crossing the anvils of tropical mesoscale convective systems (MCSs). The use of fine‐scale operational numerical weather predictions as provided by the French limited‐area model AROME could help to better understand this phenomenon and to help its anticipation. To enable AROME to simulate ICI‐prone conditions, modifications of its single‐moment microphysical scheme Intercity‐Express 3 (ICE3) are tested. Using a temperature‐dependent snow particle distribution deeply impacts the organization and the ice phase of the simulated MCS. Notably, while the size of convective regions decreases, the size of anvil clouds increases and the low stratiform rain increases as well. As a result, by increasing the quantity of snow and decreasing the quantity of graupel, the simulation of ICI‐prone conditions in the anvils of convective systems is enabled. Using this parametrization, further modifications fine‐tune the representation of snow and further increase the size of the anvil cloud. The Marshall–Palmer snow distribution is replaced by a generalized gamma and the terminal fall velocities of snow hydrometeors are parametrized so that they are in closer agreement with observations.

Quantifying Uncertainty in Ice Particle Velocity–Dimension Relationships Using MC3E Observations

Abstract Ice particle terminal fall velocity (Vt) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of Vt are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio (Ar), and maximum dimension (Dmax). These estimates are not conducive for use in most models since model parameterizations often take the form , where (α, β) depend on habit and Dmax. A previously developed framework is used to determine surfaces of equally plausible (α, β) coefficients whereby ice particle size/shape distributions are combined with Vt best estimates to determine mass- (VM) or reflectivity-weighted (VZ) velocities that closely match parameterized VM,SD or VZ,SD calculated using the (α, β) coefficients using two approaches. The first uses surfaces of equally plausible (a, b) coefficients describing mass (M)–dimension relationships (i.e., ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine (α, β) coefficients. The second investigates how uncertainties in Ar, Dmax, and size distribution N(D) affect VM or VZ. For seven of nine flight legs flown on 20 and 23 May 2011 during the Mesoscale Continental Convective Clouds Experiment (MC3E), uncertainty from natural parameter variability—namely, the variability in ice particle parameters in similar meteorological conditions—exceeds uncertainties arising from different Ar assumptions or Dmax estimates. The combined uncertainty between Ar, Dmax, and N(D) produced smaller variability in (α, β) compared to varying M(D), demonstrating M(D) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions. Significance Statement Ice particle fall velocity is fundamental for numerous processes within clouds, and hence is a critical property that must be accurately represented in weather and climate models. Using aircraft observations of ice particle shapes and sizes obtained in clouds behind midlatitude thunderstorms, this work develops a new framework for estimating ice particle fall velocities and their uncertainty, including quantifying the importance of different uncertainty sources from cloud microphysics measurements. Natural parameter variability contributes the most uncertainty in ice particle fall velocity estimates, although other sources can also be important contributors to uncertainty in certain conditions. Additional work examining ice particle data is needed to further understand how dependent uncertainty in certain ice particle properties are to local environmental conditions.

3D trajectories and velocities of rainfall drops in a multifractal turbulent wind field

Abstract. Weather radars measure rainfall in altitude, whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind, which affects the location of the measured field. The governing equation of a rain drop's motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. It depends non-linearly on the instantaneous relative velocity between the drop and the local wind, which yields complex behaviour. Here, the drag force is expressed in a standard way with the help of a drag coefficient expressed as a function of the Reynolds number. Corrections accounting for the oblateness of drops greater than 1–2 mm are suggested and validated through a comparison of the retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature. An explicit numerical scheme is then implemented to solve this equation for a 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. It appears that multifractal features of the input wind are simply transferred to the drop velocity with an additional fractional integration whose level depends on the drop size, and a slight time shift. Using an actual high-resolution 3D sonic anemometer and a scale invariant approach to simulate realistic fluctuations of wind in space, trajectories of drops of various sizes falling form 1500 m are studied. For a strong wind event, drops located within a radar gate in altitude during 5 min are spread on the ground over an area of the size of a few kilometres. The spread for drops of a given diameter is found to cover a few radar pixels. Consequences on measurements of hydro-meteorological extremes that are needed to improve the resilience of urban areas are discussed.

Vertical Structure of Ice Clouds and Vertical Air Motion from Vertically Pointing Cloud Radar Measurements

The vertical structure of ice clouds and vertical air motion (Vair) were investigated using vertically pointing Ka-band cloud radar. The distributions of reflectivity (Z), Doppler velocity (VD), and spectrum width (SW) were analyzed for three ice cloud types, namely, cirrus, anvil, and stratiform clouds. The radar parameters of the cirrus clouds showed narrower distributions than those of the stratiform and anvil clouds. In the vertical structures, the rapid growth of Z and VD occurred in the layer between 8 and 12 km (roughly a layer of −40 °C to −20 °C) for all ice clouds. The prominent feature in the stratiform clouds was an elongated “S” shape in the VD near 7–7.5 km (at approximately −16 °C to −13 °C) due to a significant decrease in an absolute value of VD. The mean terminal fall velocity (Vt) and Vair in the ice clouds were estimated using pre-determined Vt–Z relationships (Vt = aZb) and the observed VD. Although the cirrus clouds demonstrated wide distributions in coefficients a and exponents b depending on cloud heights, they showed a smaller change in Z and Vt values compared to that of the other cloud types. The anvil clouds had a larger exponent than that of the stratiform clouds, indicating that the ice particle density of anvil clouds increases at a faster rate compared with the density of stratiform clouds for the same Z increment. The significant positive Vair appeared at the top of all ice clouds in range up to 0.5 m s−1, and the anvil clouds showed the deepest layer of upward motion. The stratiform and anvil clouds showed a dramatic increase in vertical air motion in the layer of 6–8 km as shown by the rapid decrease of VD. This likely caused increase of supersaturation above. A periodic positive Vair linked with a significant reduction in VD appeared at the height of 7–8 km (approximately −15 °C) dominantly in the stratiform clouds. This layer exhibited a bi-modal power spectrum produced by pre-existing larger ice particles and newly formed numerous smaller ice particles. This result raised a question on the origins of smaller ice particles such as new nucleation due to increased supersaturation by upward motion below or the seeder-feeder effect. In addition, the retrieved Vair with high-resolution data well represented a Kelvin-Helmholtz wave development.

The Terminal Velocity of Axisymmetric Cloud Drops and Raindrops Evaluated by the Immersed Boundary Method

AbstractThe terminal velocity of cloud drops and raindrops used in numerical model calculations can significantly affect weather predictions. Current formulations rely on laboratory experiments made in the 1940s and 1960s. Because these experiments were performed only at typical environmental conditions of 20°C and 1013 hPa, parameterizations have been introduced to deduce the terminal velocity aloft without rigorous evaluation. In this study, an incompressible two-phase flow direct numerical simulation model is used to calculate the free-falling motion of axisymmetric drops with diameters between 0.025 and 0.5 mm to study the terminal fall velocity. Simulated terminal fall velocities of free-falling drops at 20°C and 1013 hPa agree within 3.2% with the previous empirical parameterization (Beard formula), and 4.5% with existing laboratory data in the diameter range between 0.3 and 0.5 mm. The velocities converge to the analytic Hadamard–Rybczynski solution within 2% for small Reynolds numbers, demonstrating the robustness of our simulations. Simulations under various atmospheric conditions show that existing empirical parameterizations that account for the air density dependence of the terminal velocity have errors up to 11.8% under the conditions examined in this study. We propose a new empirical formula that describes the air density dependence of the terminal velocity. It is also shown that the falling speed of a small drop is not sensitive to shape oscillation, and the terminal velocity decreases by only less than 1.3% when the axis ratio increases by 12% with reduced surface tension. Internal circulation within falling drops is also presented and compared with previous studies.

The summer 2019 basaltic Vulcanian eruptions (paroxysms) of Stromboli

Stromboli is an active, open conduit mafic volcano, whose persistent mild Strombolian activity is occasionally punctuated by much stronger explosions, known as paroxysms. During summer 2019, the volcano unexpectedly produced one such paroxysm on July 3, followed by intense explosive and intermittent effusive activity culminating in a second paroxysm on August 28. Visual observations and the analysis of the fall deposits associated with the two paroxysms allowed us to reconstruct ballistic exit velocities of up to 160 m s−1. Plume heights of ~ 8.4 km and 6.4 km estimated for the two events correspond to mass eruption rates of 1.1 × 106 kg s−1 and 3.6 × 105 kg s−1, respectively. This is certainly an underestimate as directional pyroclastic flows into which mass was partitioned immediately formed, triggering small tsunamis at the sea entrance. The mass of ballistic spatters and blocks erupted during the July 3 event formed a continuous cover at the summit of the volcano, with a mass calculated at ~ 1.4 × 108 kg. The distribution of fall deposits of both the July 3 and August 28 events suggests that pyroclasts characterized by terminal fall velocities < 10–20 m s−1 remained fully suspended within the convective region of the plume and did not fall at distances closer than ca 1700 m to the vent. Based on the impulsive, blast-like phenomenology of paroxysms as well as the deposit distribution and type, paroxysms are classified as basaltic Vulcanian in style. The evolution of the summer 2019 eruptive events was not properly captured within the framework of the alert level system which is focused on tsunamigenic processes, and this is discussed so as to provide elements for the implementation of the reference scenarios and an upgrade of the system to take into account such events. In particular we find that, although still largely unpredictable, at least at operational time scales, and not necessarily tsunamigenic, Vulcanian eruptions and the subsequent evolution of the eruptive phenomena should be considered for the alert level system. This serves as a warning to the implementation of alert systems where the unexpected needs to be taken into account, even at systems that are believed to be relatively “predictable” as is the case at many persistently active, open vent mafic systems.

Multi-Instrument Rainfall-Rate Estimation in the Peruvian Central Andes

AbstractAgriculture is one of the main economic activities in the Peruvian Andes; rainwater alone irrigates more than 80% of the fields used for agriculture purposes. However, the cloud and rain generation mechanisms in the Andes still remain mostly unknown. In early 2014, the Instituto Geofísico del Perú (IGP) decided to intensify studies in the central Andes to better understand cloud microphysics; the Atmospheric Microphysics And Radiation Laboratory officially started operations in 2015 at IGP’s Huancayo Observatory. In this work, a Ka-band cloud profiler [cloud and precipitation profiler (MIRA-35c)], a UHF wind profiler [Clear-Air and Rainfall Estimation (CLAIRE)], and a VHF wind profiler [Boundary Layer and Tropospheric Radar (BLTR)] are used to estimate rainfall rate at different conditions. The height dependence of the drop size diameter versus the terminal velocity, obtained by the radars, in the central Andes (3350 m MSL) was evaluated. The estimates of rainfall rate are validated to ground measurements through a disdrometer [second-generation Particle, Size, and Velocity (PARSIVEL2)] and two rain gauges. The biases in the cumulative rainfall totals for the PARSIVEL2, MIRA-35c, and CLAIRE were 18%, 23%, and −32%, respectively, and their respective absolute biases were 19%, 36%, and 63%. These results suggest that a real-time calibration of the radars, MIRA-35c and CLAIRE, is necessary for better estimation of precipitation at the ground. They also show that the correction of the raindrop terminal fall velocity, obtained by separating the vertical wind velocity (BLTR), used in the estimation the raindrop diameter is not sufficient, especially in convective conditions.

Ice crystal number concentration from lidar, cloud radar and radar wind profiler measurements

Abstract. A new method for the retrieval of ice crystal number concentration (ICNC) from combined active remote-sensing measurements of Raman lidar, cloud radar and radar wind profiler is presented. We exploit – for the first time – measurements of terminal fall velocity together with the radar reflectivity factor and/or the lidar-derived particle extinction coefficient in clouds for retrieving the number concentration of pristine ice particles with presumed particle shapes. A lookup table approach for the retrieval of the properties of the particle size distribution from observed parameters is presented. Analysis of methodological uncertainties and error propagation is performed, which shows that a retrieval of ice particle number concentration based on terminal fall velocity is possible within 1 order of magnitude. Comparison between a retrieval of the number concentration based on terminal fall velocity on the one hand and lidar and cloud radar on the other shows agreement within the uncertainties of the retrieval.

Hydrometeor Lofting and Mesoscale Snowbands

Abstract Environments that accompany mesoscale snowbands in extratropical cyclones feature strong midlevel frontogenesis and weak symmetric stability, conditions conducive to vigorous ascent. Prior observational and numerical studies document the occurrence of upward vertical velocities in excess of 1 m s−1 near the comma head of winter cyclones. These values roughly correspond to the terminal fall velocity of snow; snow lofting has been measured directly with vertically pointing radars. Here, we investigate the occurrence of lower-tropospheric snow lofting near mesoscale bands and its contribution to snowfall heterogeneity. We test the hypothesis that hydrometeor lofting substantially influences snowfall distributions by analyzing the vertical snow flux in case-study simulations, by computing snow trajectories, and by testing sensitivity of simulated snowbands to parameterized snow terminal fall velocity and advection. These experiments confirm the presence of upward snow flux in the lower troposphere upstream of simulated mesoscale snowbands for two events (27 January 2015 and 2 February 2016). The band of lower-tropospheric lofting played a more important role in the January 2015 case relative to the February 2016 event. Lofting enhances the horizontal advection of snow by increasing hydrometeor residence time aloft, influencing the surface snowfall distribution. Experimental simulations illustrate that while lofting and advection influence the snow distribution, these processes reduce snowfall heterogeneity, contrary to our initial hypothesis. Our findings indicate that considerable horizontal displacement can occur between the locations of strongest ascent and heaviest surface snowfall. Numerical forecasts of snowbands are sensitive to formulations of terminal fall velocity of snow in microphysical parameterizations due to this lofting and transport process.

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

Abstract. In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

Terminal fall velocity: the legacy of Stokes from the perspective of fluvial hydraulics

This review article, dedicated to the bicentenary celebration of Sir George Gabriel Stokes' birthday, presents the state-of-the-science of terminal fall velocity, highlighting his rich legacy from the perspective of fluvial hydraulics. It summarizes the fluid drag on a particle and the current status of the drag coefficient from both the theoretical and empirical formulations, highlighting the three major realms-Stokesian, transitional and Newtonian realms. The force system that drives the particle motion falling through a fluid is described. The response of terminal fall velocity to key factors, which include particle shape, hindered settling and turbulence (nonlinear drag, vortex trapping, fast tracking and effects of loitering), is delineated. The article puts into focus the impact of terminal fall velocity on fluvial hydraulics, discussing the salient role that the terminal fall velocity plays in governing the hydrodynamics of the sediment threshold, bedload transport and suspended load transport. Finally, an innovative perspective is presented on the subject's future research track, emphasizing open questions.

A three-dimensional numerical model to estimate the fall velocity of sediment particles

The fall velocity of sediment particles plays a key role in sediment transport studies. Researchers have attempted to determine the terminal fall velocity, and most of the studies in this regard have been based on experimental, quasi-experimental, and in-situ measurements. The present study aimed to use a numerical model to estimate the fall velocity of a single sediment particle in distilled and motionless water. We used spherical quartz particles with the diameters of 0.77, 1.09, 2.18, and 4.36 millimeters and density of 2,650 kg/m3. The Flow-3D software was applied to estimate the fall velocity based on the environment of experiment by Ferguson and Church (2004) using the void of flow method. The main objective of this research was to demonstrate the power of the numerical model to simulate the fall velocity of sediment particles. To validate the results of the model, they were compared with the experimental results and 26 well-known publications during 1933-2016 using the root-square-mean and mean-absolute-percentage errors. The results showed good agreement between the experimental and numerical data. Therefore, the proposed numerical model could be used to determine the fall velocity of sediment particles with a wide range of diameters in the proposed environment and particle types.

Combining cloud radar and radar wind profiler for a value added estimate of vertical air motion and particle terminal velocity within clouds

Abstract. Vertical-stare observations from a 482 MHz radar wind profiler and a 35 GHz cloud radar are combined on the level of individual Doppler spectra to measure vertical air motions in clear air, clouds and precipitation. For this purpose, a separation algorithm is proposed to remove the influence of falling particles from the wind profiler Doppler spectra and to calculate the terminal fall velocity of hydrometeors. The remaining error of both vertical air motion and terminal fall velocity is estimated to be better than 0.1 m s−1 using numerical simulations. This combination of instruments allows direct measurements of in-cloud vertical air velocity and particle terminal fall velocity by means of ground-based remote sensing. The possibility of providing a profile every 10 s with a height resolution of &lt;100 m allows further insight into the process scale of in-cloud dynamics. The results of the separation algorithm are illustrated by two case studies, the first covering a deep frontal cloud and the second featuring a shallow mixed-phase cloud.