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Zachary J. Lebo

Abstract

Changes in the aerosol number concentration are reflected by changes in raindrop size and number concentration that ultimately affect the strength of cold pools via evaporation. Therefore, aerosol perturbations can potentially alter the balance between cold pool–induced and low-level wind shear–induced circulations. In the present work, simulations with increased aerosol loadings below approximately 3 km, between approximately 3 and 10 km, and at all vertical levels are performed to specifically address both the overall sensitivity of a squall line to the vertical distribution of aerosols and the extent to which low-level aerosols can affect the convective strength of the system. The results suggest that low-level aerosol perturbations have a negligible effect on the overall storm strength even though they act to enhance low-level latent heating rates. A tracer analysis shows that the low-level aerosols are either predominantly detrained at or below the freezing level or are rapidly lifted to the top of the troposphere or the lower stratosphere within the strongest convective cores. Moreover, it is shown that midlevel aerosol perturbations have nearly the same effect as perturbing the entire domain, increasing the convective updraft mass flux by more than 10%. These changes in strength are driven by a complex chain of events caused by smaller supercooled droplets, larger graupel, and larger raindrops. Combined, these changes tend to reduce the low-level bulk evaporation rate, thus weakening the cold pool and enhancing updraft strength. The results presented herein suggest that midlevel aerosol perturbations may exhibit a much larger effect on squall lines, at least in the context of this idealized framework.

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Zachary J. Lebo and Hugh Morrison

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A novel two-moment bulk aerosol parameterization is derived from a state-of-the-art 2D bin microphysics model using power-law relationships and a semi-analytical technique for activation. The activation scheme predicts both number and mass of a lognormal aerosol distribution and permits the evolution of the modal mass with time. The newly developed bulk aerosol scheme is formulated for use in traditional two-moment bulk microphysics models. The new explicit scheme is compared with the 2D bin scheme and a simple scaling aerosol parameterization, in which all the aerosol processes are scaled to the respective cloud process rates, in a kinematic model with a specified flow field. Hybrid simulations in which the explicit activation formulation is coupled to the scaling parameterization are also performed. Model results demonstrate the significance of including a physically realistic representation of aerosols contained in haze, cloud droplets, and rain. It is shown that the explicit aerosol parameterization and scaling method predict similar bulk aerosol quantities and match the results of the 2D bin model only if an explicit treatment of aerosol activation—that is, both aerosol number and mass transfer because of activation—is included in the microphysics model.

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Zachary J. Lebo and Hugh Morrison

Abstract

The dynamical effects of increased aerosol loading on the strength and structure of numerically simulated squall lines are explored. Results are explained in the context of Rotunno–Klemp–Weisman (RKW) theory. Changes in aerosol loading lead to changes in raindrop size and number that ultimately affect the strength of the cold pool via changes in evaporation. Thus, the balance between cold pool and low-level wind shear–induced vorticities can be changed by an aerosol perturbation. Simulations covering a wide range of low-level wind shears are performed to study the sensitivity to aerosols in different environments and provide more general conclusions. Simulations with relatively weak low-level environmental wind shear (0.0024 s−1) have a relatively strong cold pool circulation compared to the environmental shear. An increase in aerosol loading leads to a weakening of the cold pool and, hence, a more optimal balance between the cold pool– and environmental shear–induced circulations according to RKW theory. Consequently, there is an increase in the convective mass flux of nearly 20% in polluted conditions relative to pristine. This strengthening coincides with more upright convective updrafts and a significant increase (nearly 20%) in cumulative precipitation. An increase in aerosol loading in a strong wind shear environment (0.0064 s−1) leads to less optimal storms and a suppression of the convective mass flux and precipitation. This occurs because the cold pool circulation is weak relative to the environmental shear when the shear is strong, and further weakening of the cold pool with high aerosol loading leads to an even less optimal storm structure (i.e., convective updrafts begin to tilt downshear).

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Kevin G. Kacan and Zachary J. Lebo

Abstract

The dynamics of convective systems are inherently linked to microphysical processes through phase changes that result in warming or cooling. This is especially true of near-surface cooling via evaporation and melting of falling hydrometeors. In most numerical simulations, the melting of frozen hydrometeors (e.g., hail, graupel, snow) is computed within parameterized bulk microphysics schemes, many of which lack the ability to accurately represent mixed-phase hydrometeors (i.e., partially melted ice), which can affect hydrometeor sedimentation, melting, and evaporation of shed drops. To better understand the microphysical and dynamical effects of melting in convective storms, a bin microphysics scheme was used in the Weather Research and Forecasting Model for two idealized cases: a supercell storm and a squall line. Physically based predicted liquid fraction, instantaneous melting, and instantaneous shedding schemes were used to examine the role and importance of melting hydrometeors for these two storm modes. The results suggest that the amount of precipitation is dependent on the representation of melting. Moreover, the dynamic and thermodynamic characteristics of the simulated storms are found to differ substantially between the melting scenarios, resulting in varied storm system evolution; these differences are found to be dependent on the ambient aerosol concentration, although the differences induced by changing the representation of melting generally outweigh those of changing the aerosol loading. The results highlight the large role of melting in convective storm characteristics and suggest that further model improvements are needed in the near future.

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Matthew R. Kumjian, Zachary J. Lebo, and Aaron M. Ward

Abstract

Hail-bearing storms produce substantial socioeconomic impacts each year, yet challenges remain in forecasting the type of hail threat supported by a given environment and in using radar to estimate hail sizes more accurately. One class of hail threat is storms producing large accumulations of small hail (SPLASH). This paper presents an analysis of the environments and polarimetric radar characteristics of such storms. Thirteen SPLASH events were selected to encompass a broad range of geographic regions and times of year. Rapid Refresh model output was used to characterize the mesoscale environments associated with each case. This analysis reveals that a range of environments can support SPLASH cases; however, some commonalities included large precipitable water (exceeding that day’s climatological 90th-percentile values), CAPE < 2500 J kg−1, weak storm-relative wind speeds (<10 m s−1) in the lowest few kilometers of the troposphere, and a weak component of the storm-relative flow orthogonal to the 0–6-km shear vector. Most of the storms were weak supercells that featured distinctive S-band radar signatures, including compact (<200 km2) regions of reflectivity factor > 60 dBZ, significant differential attenuation evident as negative differential reflectivity extending downrange of the hail core, and anomalously large specific differential phase K DP. The K DP values often approached or exceeded the operational color scale’s upper limit (10.7° km−1); reprocessing the level-II data revealed K DP >17° km−1, the highest documented in precipitation at S band. Electromagnetic scattering calculations using the T-matrix method confirm that large quantities of small melting hail mixed with heavy rain can plausibly explain the observed radar signatures.

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Matthew R. Kumjian, Zachary J. Lebo, and Hughbert C. Morrison

Abstract

Deep convective storms produce raindrops through three mechanisms: condensation and coalescence growth of cloud liquid droplets (i.e., warm processes), melting of ice hydrometeors, and shedding from wet hailstones. To investigate the relative importance of these mechanisms and their contributions to exotic drop size distributions (DSDs) observed near the surface in supercell storms, an idealized simulation of a supercell is performed using a modified version of the Morrison two-moment microphysics scheme. The modified scheme includes separate categories for warm, shed, and melted rain.

Rain originating from melting ice dominates the rain mass at low levels, especially along the right forward-flank precipitation shield, whereas shed-rain drops dominate a region within the left forward flank. Warm rain is only dominant in the upshear portion of the rear flank of the storm at low levels, though it dominates the total rain mass within the main updraft aloft. The warm-rain mass at low levels is associated with strong low-level downdrafts, consistent with previously published hypotheses based on polarimetric radar observations. Raindrops produced via warm processes are smaller on average than those produced by shedding and melting; drops in the latter class tend to be the largest.

Overall, the simulations fail to reproduce the diverse nature of observed supercell DSDs, although the modified microphysics scheme does increase the variability of surface DSDs compared to the Control run. This implies that more sophisticated treatment of rain microphysics is needed to capture the natural variability of supercell DSDs, including the ability to evolve the DSD spectral shape through sedimentation and collisional processes.

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Kara J. Sulia, Zachary J. Lebo, Vanessa M. Przybylo, and Carl G. Schmitt

Abstract

A novel methodology for modeling ice–ice aggregation is presented. This methodology combines a modified hydrodynamic collection algorithm with bulk aggregate characteristic information from an offline simulator that collects ice particles, namely, the Ice Particle and Aggregate Simulator, and has been implemented into the Adaptive Habit Microphysics scheme in the Weather Research and Forecasting Model. Aggregates, or snow, are formed via collection of cloud ice particles, where initial ice characteristics and the resulting geometry determine aggregate characteristics. Upon implementation, idealized squall-line simulations are performed to examine the new methodology in comparison with commonly used bulk microphysics schemes. It is found that the adaptive habit aggregation parameterization develops snow and reduces ice mass and number concentrations compared to other schemes. The development of aggregates through the new methodology cascades into other interesting effects, including enhancements in ice and snow growth, as well as homogeneous freezing. Further microphysical analyses reveal varying sensitivities, where snow processes are most sensitive to the new parameterization, followed by ice, then cloud, rain, and graupel processes. Further, the new scheme results in enhancements in surface precipitation due to the persistence of snow at lower altitudes. This persistence is a result of shape-dependent melting and sublimation, increasing the residence time. Moreover, these low-level enhancements are reflected in increases in radar reflectivity at the surface and its spatial distribution. Finally, the ability to predict snow shape and density allows for the simulation of polarimetric radar quantities, resulting in signature enhancements compared to schemes that do not consider spatial and temporal variations in snow shape and density.

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Carl G. Schmitt, Kara Sulia, Zachary J. Lebo, Andrew J. Heymsfield, Vanessa Przybyo, and Paul Connolly

Abstract

The terminal velocity (V t) of ice hydrometeors is of high importance to atmospheric modeling. V t 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 V t for ice particles that are predicted or measured to be of the “same size.” In this article we explore the variability of ice hydrometeor V t both theoretically and through the use of experimental observations. Theoretically, the variability in V t 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 V t. Results show that the spread in V t 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 V t spectra.

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Vanessa M. Przybylo, Kara J. Sulia, Carl G. Schmitt, Zachary J. Lebo, and William C. May

Abstract

Aggregation, the process by which two or more ice particles attach to each other, is typically observed in clouds that span a range of temperatures and is influenced by the crystal shape (habit). In this study, the resulting characteristics of ice–ice two-monomer aggregation is investigated, which is expected to improve microphysical parameterizations through more precise aggregate characteristics and in turn better predict the rate of aggregation and snow development. A systematic way to determine the aspect ratio of the aggregate was developed, which takes into account the expected falling orientations, overlap of each monomer, and any contact angle that may form through so-called constrained randomization. Distributions were used to obtain the most frequent aspect ratio, major axis, and minor axis of aggregated particles with respect to the monomer aspect ratio. Simulations were completed using the Ice Particle and Aggregate Simulator (IPAS), a model that uses predefined three-dimensional geometries, (e.g., hexagonal prisms) to simulate ice crystal aggregation and allows for variation in crystal size, shape, number, and falling orientation. In this study, after collection in a theoretical grid space, detailed information is extracted from the particles to determine the properties of aggregates. It was found that almost all monomer aspect ratios aggregate to less extreme aggregate aspect ratios at nearly the same rate. Newly formed aggregate properties are amenable to implementation into more sophisticated bulk microphysical models designed to predict and evolve particle properties, which is crucial in realistically evolving cloud ice mass distribution and for representing the collection process.

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Matthew R. Kumjian, Kevin A. Bowley, Paul M. Markowski, Kelly Lombardo, Zachary J. Lebo, and Pavlos Kollias

Abstract

An engaged scholarship project called “Snowflake Selfies” was developed and implemented in an upper-level undergraduate course at The Pennsylvania State University (Penn State). During the project, students conducted research on snow using low-cost, low-tech instrumentation that may be readily implemented broadly and scaled as needed, particularly at institutions with limited resources. During intensive observing periods (IOPs), students measured snowfall accumulations, snow-to-liquid ratios, and took microscopic photographs of snow using their smartphones. These observations were placed in meteorological context using radar observations and thermodynamic soundings, helping to reinforce concepts from atmospheric thermodynamics, cloud physics, radar, and mesoscale meteorology courses. Students also prepared a term paper and presentation using their datasets/photographs to hone communication skills. Examples from IOPs are presented. The Snowflake Selfies project was well received by undergraduate students as part of the writing-intensive course at Penn State. Responses to survey questions highlight the project’s effectiveness at engaging students and increasing their enthusiasm for the semester-long project. The natural link to social media broadened engagement to the community level. Given the successes at Penn State, we encourage Snowflake Selfies or similar projects to be adapted or implemented at other institutions.

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