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`Using 3D Laser Scanning Technology to Create Digital Models of Hailstones
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`Research Gate
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`Article  in  Bulletin of the American Meteorological Society · October 2016
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`DOI: 10.1175/BAMS-D-15-00314.1
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`CITATIONS
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`4 authors, including:
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`Ian M. Giammanco
`Insurance Institute for Business & Home Safety
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`28 PUBLICATIONS   500 CITATIONS   
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`Heather Estes
`University of North Carolina at Wilmington
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`5 PUBLICATIONS   42 CITATIONS   
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`The DOI for this manuscript is doi: 10.1175/BAMS-D-15-00314.1
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`If you would like to cite this EOR in a separate work, please use the following full
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`Giammanco, I., B. Maiden, H. Sommers, and T. Brown-Giammanco, 2016: Using
`3D Laser Scanning Technology to Create Digital Models of Hailstones. Bull.
`Amer. Meteor. Soc. doi:10.1175/BAMS-D-15-00314.1, in press.
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`© 2016 American Meteorological Society
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`Manuscript (non-LaTeX)
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`Click here to download Manuscript (non-LaTeX)
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`Using 3D Laser Scanning Technology to Create Digital Models of Hailstones
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`1Ian M. Giammanco
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`1Benjamin R. Maiden
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`1Heather E. Estes
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`1Tanya M. Brown-Giammanco
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`1Insurance Institute for Business & Home Safety
`Richburg, SC
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`For Submission to: Bulletin of the American Meteorological Society
`Corresponding Author Address:
`Ian M. Giammanco
`Insurance Institute for Business & Home Safety
`5335 Richburg Rd
`Richburg, SC 29729
`Email: igiammanco@ibhs.org
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`Abstract
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`The emergence of 3D scanning technologies has provided a new opportunity to explore
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`the shape characteristics of hailstones in great detail. The ability to effectively map the shape of
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`hailstones will improve assessments of hailstone aerodynamic properties, how their density
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`relates to their strength, and how radar energy is scattered. Ultimately, 3D scanning of hailstones
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`will contribute toward research in hail detection, forecasting, and damage mitigation of severe
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`hail, which accounts for well over $1 billion in annual insured losses.
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`The use of a handheld 3D laser scanner in a field setting was explored during field
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`campaigns in 2015 and 2016. Hailstones were collected following thunderstorm passages and
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`were measured, weighed, and scanned. The system was successful in capturing 3D models of
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`over 40 hailstones. A full scan takes approximately three minutes to complete and data can be
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`captured at a resolution of 0.008 cm. It is believed this is the first time such a system has been
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`used to produce 3D digital hailstone models. Analysis of the model data has showed that
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`hailstones depart from spherical shapes as they increase in diameter and that bulk density and
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`strength show little correlation. While the dataset presented here is small, the use of 3D scanners
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`in the field is a practical method to obtain detailed datasets on hailstone characteristics. In
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`addition, these data could be used to 3D-print hailstones to explore their aerodynamics, to
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`produce cavity molds for ice impact tests, and for modeling radar scattering properties of natural
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`hailstone shapes.
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`3D laser scanning and hail
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`Hailstorms account for over $1 billion dollars in annual insured property losses and their
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`increasing trend seen over the past two decades has outpaced advances in observation,
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`forecasting, and mitigation of hail damage (Changnon et al. 2009; Roeder 2012; Kunkel et al.
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`2013). Beginning in 2012, the Insurance Institute for Business & Home Safety (IBHS) began a
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`comprehensive research program with the overarching goal to help mitigate property losses from
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`severe hail. A component of this initiative included determining the properties of hailstones that
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`must be accounted for in laboratory material impact tests such that the results of these
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`standardized test methods would be reasonably predictive of real-world performance of building
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`materials. Subsequently, this led to a field campaign to measure the physical and material
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`properties of hail and to explore emerging technologies to aid in this effort.
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`It is well known that hailstones are found in a variety of non-homogeneous shapes and
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`can have large protuberances, which makes characterizing their true shape difficult using
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`conventional means (i.e., caliper or ruler). Obtaining an accurate volume through physical
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`measurements is also difficult even when measuring multiple dimensions. In the past, record-
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`breaking hailstones were kept in cold storage so a cast could be made of the hailstone. The
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`impact craters of giant hailstones have also been examined and molds made of their shapes as
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`well (Knight and Knight 2001). While the process is effective in capturing the hailstone shape, it
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`is cumbersome and time-consuming. A method was needed that provided accurate 3D
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`measurement data without substantial contamination or melting of the hailstone prior to strength
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`testing. The fine-scale, non-homogeneous nature of hailstones provided the motivation to
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`investigate how 3D laser scanners could be applied toward hail research.
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`The emergence of 3D scanning technology has led to new research opportunities across a
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`wide range of fields (e.g., medical, mechanical and civil engineering, archaeology, etc.) but with
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`little application within physical meteorology. In the atmospheric sciences, measurement systems
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`such as LIDAR, particle imagers, laser disdrometers, scintillometers, optical rain gauges, and
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`visibility sensors come to mind when considering laser-based applications. These systems are
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`focused on in-situ measurement of atmospheric particles or rely on backscattered energy from
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`these particles. For 3D laser scanners, most atmospheric particles are too small and their in-situ
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`collection is too difficult for a manually operated laser scanning system to be of use to map their
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`shape. However, hailstones are large enough and their shape is complex enough for laser scans
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`and the 3D models that are produced to be scientifically beneficial. 3D laser scanners are also
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`efficient for collecting sizeable datasets to evaluate the complex shapes of hailstones. During
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`field campaigns in 2015 and 2016, a handheld 3D laser scanner was used successfully by IBHS
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`to collect full digital 3D models of hailstones. It is believed this is the first time this technology
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`has been used in this manner.
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`Evolution of 3D scanner technology
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`The development of scanning technology to obtain accurate and precise measurements of
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`objects began in the 1960s with advances in computer technology. Optical methods proved to be
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`much faster, did not require direct physical contact with specimens, and were well suited for
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`complex shapes. The foundational research that integrated both passive photogrammetric and
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`active laser techniques was pioneered by the National Research Council of Canada (Mayer
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`1999). Modern systems apply an active laser and passive photogrammetric components to
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`capture point-cloud data to produce the digitized 3D model. At each data point, the distance and
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`angle from the object to the system is recorded in a scanner-relative coordinate system. For large
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`objects, several footprints of data are needed to stitch together the full 3D shape. Processing
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`algorithms assimilate these footprints and remove duplicate data. Most current systems connect
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`the point-cloud data by applying a non-uniform rational basis (NURB) spline fit. The result is
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`faceted polygons (typically triangles), which produces the 3D surface. With advancements in
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`reducing the size and cost of electronic components, small, single-operator, handheld units have
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`become less cost-prohibitive for a wide range of research projects including field studies and
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`commercial applications.
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`Capability scoping
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`The system selected to explore 3D scanning of natural hailstones was a handheld
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`HandySCAN EXAscanTM system, manufactured by Creaform Inc. The system is a non-contact
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`active scanner that employs a class II eye-safe laser to project a beam on a target. An array of
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`cameras track the projected laser location, as shown in the conceptual diagram in Fig. 1. Its
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`relatively small size, low weight (~ 1.5 kg), and simple operation by a single person made it ideal
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`for use in a field vehicle, under non-optimal conditions (Fig. 2A and B). To operate the unit,
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`information must be simultaneously collected on the unit’s position while it is scanning the
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`specimen. Additionally, the unit must be calibrated prior to operation periods. The scanner is
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`calibrated by using a plate with a grid of reflective targets, supplied by the manufacturer
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`(reflective targets are identified in Fig. 2A and B). The precise dimensions and target locations of
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`the plate are stored in the operating software which is able to identify and adjust for any small
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`bias errors. Small errors may result from temperature changes and the expansion and contraction
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`of hardware components such that calibration is recommended prior to scanning sessions. The
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`reflective positioning targets are also used to define the coordinate system with respect to the
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`specimen being scanned. Targets are scanned separately (only one time) prior to data collection.
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`The information is stored by the operating software and applied when scanning of the specimen
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`is underway. The targets are adhered either to the specimen itself or to a mounting system such
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`that the unit always has several positioning targets in its field of view (HandySCAN EXAscan™
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`requires at least three). If the minimum number of targets is not detected during data collection,
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`the software will cease logging until they are identified to automatically avoid data gaps due to
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`user error. The system has a trigger that toggles the laser projection, camera operation, and data
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`collection.
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`The unit has a maximum configurable resolution of 0.008 cm, an accuracy of ±0.004 cm,
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`and a maximum sampling rate of 25 kHz. It is tethered to a laptop computer running Creaform’s
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`VXelements software package to operate the scanner, view ongoing scans in real-time, and store
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`the data. The NURB spline-based polygon-mesh approach is used by VXelements to capture,
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`process, and display the 3D data. The processed dataset can then be quality-controlled to
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`synthetically fill in missing data, remove other objects that may have been in the field of view,
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`and filter spurious returns. Once the data have been processed, additional analyses can be
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`performed on the digital model to extract more information on the characteristics of the
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`hailstone. Data can also be exported in a .STL file format for use in standard CAD packages or
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`other computational analysis tools.
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`Laboratory ice testing
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`The EXAscanTM system’s ability to detect and map ice surfaces was tested using ice
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`spheres made with pure distilled water (very clear ice) and water with diffused carbon dioxide
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`gas (bubble-filled, opaque ice). The ice spheres were then chipped or deformed to introduce
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`small shape changes to evaluate the scanner’s ability to detect these deformations. It was quickly
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`discovered during initial testing that ice surfaces are difficult mediums to effectively scan. Clear
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`ice surfaces and ice surfaces coated with a large amount of liquid water scattered the projected
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`laser such that it was not well defined on the object surface. Subsequently, the photogrammetric
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`camera tracking functionality could not resolve the true location of the projected laser. The result
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`was large gaps in the digitized model. Performance was improved when opaque, bubble-filled
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`ice was tested but required long scanning durations and revisiting scanned areas to capture a
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`complete model. To reduce the amount of scatter, a light dusting of a fine powder (i.e., athlete’s
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`foot spray) was used, enabling the system to adequately track the projected beam and map the ice
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`surfaces. At times, compressed air was also used to help remove any liquid water on the surface
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`of the hailstone. Although this introduces a foreign substance onto the hailstone similar to an
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`immersion test, compressive strength testing yielded no detectable influence between coated and
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`uncoated laboratory ice spheres. The method is still more practical than immersion testing in a
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`field setting, especially when considering substances used in past research (i.e., liquid mercury).
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`During this initial testing, it was also determined that full scans can be completed in less than 1
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`minute at low sampling resolutions, while higher resolution scans can take 2–3 minutes to
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`complete. The length of time needed for a complete scan was determined to be suitable for a
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`pilot field application to help mitigate the melting of stones while they were being scanned.
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`Scanning hailstones in the field
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`The scanner system was pilot tested in the field for the first time in 2015 to determine if it
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`would be effective for use during the 2016 field measurement program. Calibration was
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`performed after the target storm was selected but prior to data collection. This helped mitigate
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`any measurement errors from temperature changes and possible expansion and contraction of
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`hardware components during transit. Hailstones were collected from a target thunderstorm
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`following its passage across an identified roadway. Liquid water present on the surface of the
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`hailstone was quickly wiped clean or blown off using compressed air prior to the powder
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`application.
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`To allow the operator to quickly scan the full volume, a custom mount was designed and
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`3D-printed to support the stone. The ABS plastic material helped reduce melting resulting from
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`the direct contact between the hailstone and the supports. The mount used three points of contact
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`to support the stone with as little interference as possible. Reflective positioning targets were
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`permanently fixed to the mount to calibrate the scanner position relative to the mount and allow
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`for the mount to be placed on a turntable. The reflective targets allow the unit to “know” its
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`relative position in three-dimensional space. The turntable allowed the hailstone to be rotated so
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`that the sides of the stones could be scanned without the operator needing to move frequently
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`within the vehicle. The mount also allowed enough space between supports so the bottom of a
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`hailstone could be captured by the operator simply turning the unit to allow the laser to pass
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`across the underside of the hailstone. The support mount is detected during scanning, but is
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`removed in data processing, leaving just the 3D model of the hailstone. An example of a
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`hailstone being scanned in the field and the resulting 3D model can be seen at
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`https://vimeo.com/167924554. Before a hailstone was scanned, specimens were photographed,
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`measured with a caliper, and weighed.
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`2015 pilot field testing
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`The system was first deployed during a period of active severe weather in the Central
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`Plains from 15–18 September 2015. The field team intercepted a supercell thunderstorm near
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`Atchison, Kansas, on 17 September 2015, which produced a relatively high bulk concentration of
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`small hail (< 2 cm). Attempts to collect a full scan of small hailstones (< 1 cm) were
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`unsuccessful due to the original design of the prototype mount (corrected in a later version). The
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`hailstones were too small to effectively support as they began to melt. Fortunately, a larger
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`hailstone (2.5 cm in diameter) was gathered and a successful scan was made. The data were
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`processed to remove scanner interference, synthetically fill any small data gaps, and produce the
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`full 3D model (Fig. 2C and D). It is believed that this was the first successful 3D laser scan of a
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`hailstone. The scan of this particular hailstone was completed in approximately 3 minutes at a
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`resolution of 0.008 cm and used a maximum sampling rate of 25 kHz. The fully scanned
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`hailstone had a mass of 2.50 g, and a maximum diameter of 2.504 cm. The diameter was defined
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`as the longest straight line between two points, which passed through the center of the hailstone
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`model. The volume, determined from the model, was 3.654 cm3 which was 54% less than a
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`sphere of the same diameter (Fig. 2E). The volume coupled with the measured mass yielded a
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`bulk density of 0.68 g cm-3. The digitized 3D model was used to 3D-print a cavity mold based
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`upon the highly detailed hailstone shape (Fig. 2F) and demonstrated the linkage between 3D
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`scanning and printing technology. The success of integrating the digital hail model into a CAD
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`design application and 3D-printing a model highlighted the ability to duplicate natural hailstone
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`shapes and their intricate details in a laboratory setting. This coupled with exploration of diffused
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`gas ice mixtures could lead to the re-creation of laboratory hailstones that match the physical and
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`material properties of hailstones observed in the field.
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`2016 field measurement program and analysis
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`The 2016 field measurement program focused on obtaining 3D models of hailstones and
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`performing corresponding compressive strength tests. The efforts produced 42 digital hailstone
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`models collected primarily from supercell thunderstorms in the Southern Great Plains of the U.S.
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`in May and June of 2016. A subset of scanned hailstones, showing the variety of shapes that
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`were captured are shown in Fig. 3. The high resolution models allowed for an accurate volume
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`estimate to be obtained for each hailstone. It is acknowledged that some melting may have
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`occurred prior to collection and/or liquid water contained within small cavities in the hailstone
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`may have drained resulting in a small bias. It is also possible that protuberances may have been
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`rounded off because of melting or impact with the ground. When compared with hailstone
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`densities estimated using physical measurements and shape assumptions, the errors are expected
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`to be reduced.
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`Throughout historical literature, summarized by Knight and Knight (2001), hailstones are
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`commonly referred to as “hard” or “soft” with no quantification of their strength. It is frequently
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`assumed that hailstone strength and their damage potential scales with bulk density (Knight et al.
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`2008). The true relationship between density and strength is unknown at this time. The use of
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`3D-scanned hailstones combined with recent advances in the ability to test hailstones for their
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`compressive strength can help clarify the relationship and determine if laboratory impact tests
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`must replicate it in order to accurately produce a true correlation with real-world performance of
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`building materials.
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`The 3D-scanned hailstones were subjected to compressive strength testing which applies
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`an increasing compressive force with a strain rate on the order of 10-1 s-1 to the hailstone until it
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`fractures. The peak force at the time of fracture is captured and then scaled by the cross-sectional
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`area (i.e., plane) in which the force was exerted to produce an estimate of uniaxial compressive
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`stress. The compressive stress was used as a proxy to represent the hardness property of the
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`hailstone (Giammanco et al. 2015). These stones were also examined with respect to the
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`diameter-to-mass relationship, bulk density, their volume normalized by that of a sphere with the
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`same maximum diameter, and their compressive strength (Fig. 4). The observations also showed
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`that hailstone densities trend closer to pure ice (0.9 g cm-3) as they get larger. Three hailstones
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`exhibited a density greater than 0.9 g cm-3 and were characterized by nearly all clear ice with no
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`visible layering structure. The hailstones also had notable protuberances. The high density of
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`these stones raises the question of whether “super” density ice occurs in hailstones or this was
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`the result of a measurement error. It is possible that some mass loss between measurement and
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`scanning occurred such that the density estimate contained an error; however, the maximum
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`diameter measured using a caliper was within 0.04 cm for all three hailstones when compared to
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`the scanner-based diameter. The scale used has a precision of 0.01 g but any shaking or
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`movement of the scale could have introduced some source of measurement error. The use of this
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`system in the field will help improve the understanding of hailstone bulk density distributions
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`and determine if high-density and/or low-density hailstones are more prevalent than historical
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`literature would suggest. It was clear that the measured hailstones departed from spherical shapes
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`with increasing diameter which is in agreement with recent field observations (Heymsfield et al.
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`2014) (Fig. 4C).
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`Throughout historical literature, low-density hailstones were often associated with being
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`soft and of low strength. There has been little quantitative analysis to substantiate this
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`expectation or to investigate a potentially different relationship. The datasets collected through
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`3D scanning and compressive strength testing allowed for a preliminary examination of how the
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`two variables may be related. The relationship between the measured peak forces showed a
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`general linear trend, with a larger force required for higher densities (not shown). However, the
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`peak force must be scaled by the area of the plane in which the force was applied to produce an
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`appropriate measure of strength. As shown in Fig. 4D, the slight linear trend was toward weaker
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`hailstones with higher bulk densities. It is noted that the sample size shown here is only 42
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`hailstones and larger datasets are needed. The ability to evaluate these properties is a notable
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`advance that will foster new research toward understanding hailstone characteristics and
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`determining their properties that affect damage potential.
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`Research applications of 3D hailstone models
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`The first effort to 3D-scan hailstones was successful in proving the system could be
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`operated efficiently in the field, collect a quality number of 3D models, and allow for further
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`strength testing. The ability to collect these digital representations of natural hailstones will open
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`the door to new investigations of their shapes and material properties. By eliminating the need
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`for contact or immersion methods of determining hailstone densities, quantifying the relationship
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`between bulk density and hailstone strength was possible. This capability will help improve ice-
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`based laboratory impact test methods to ensure they are representing the necessary properties of
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`natural hail. Cavity molds can also be used to determine if hailstone shape influences the type of
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`damage for different materials.
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`The aerodynamics of hailstones is an area which could also benefit from 3D hail model
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`datasets. Digitized hail shapes could also be leveraged to explore the aerodynamic drag
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`characteristics of hail through experimental and computational methods which are vital to
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`ensuring that proper kinetic energies are used in material impact tests. Current test standards use
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`impact kinetic energies determined through assumptions that drag coefficients for spherical
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`shapes can be used for natural hailstones (Heymsfield et al. 2014). Assumptions regarding
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`hailstone drag, terminal velocities, and kinetic energies are also made within hydrometeor
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`parameterization schemes for numerical weather prediction models (Morrison et al. 2015). The
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`aerodynamic applications described here could be leveraged to improve the hail-related portions
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`of these schemes. Experiments could also shed light on the tumbling of hailstones, which can
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`complicate radar detection especially for dual-polarimetric radars (Straka et al. 2000). An
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`improved understanding of this effect may provide the ability to extract more detailed
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`hydrometeor information (i.e., mean shape, concentration) from the dual-polarimetric moments.
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`The use of 3D laser scanning systems continues to grow rapidly across a wide range of fields.
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`Until now, their use in the atmospheric sciences has been limited. The pilot investigation
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`presented here has shown how the technology can be used effectively to understand the
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`characteristics of hail beyond what is considered in historical studies. These data will foster new
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`research into the aerodynamics of hailstone shapes, the relationship between strength and
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`density, radar hail detection, and hail damage severity. Each of these applications rely on the
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`accurate representation of hail, and can be used to improve material impact testing practices,
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`improve hailstorm post-event characterizations, and develop new risk assessment methods
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`through numerical modeling efforts. Each will ultimately aid in mitigating the large amount of
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`property loss that occurs each year from severe hail.
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`Acknowledgments
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`Funding for the field program and analysis efforts are provided by the Insurance
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`Institute for Business & Home Safety through the annual operating budget. We would like to
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`acknowledge Kevin Outz from Matrix CAD Design, Inc. for his invaluable assistance in proof-
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`of-concept testing of 3D scanners for this application.
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`For further reading
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`American Society for Testing of Materials, 2010: Standard test method for hail impact resistance
`for aerospace transparent enclosures, ASTM, West Conshocken, Pa. [Available at
`http://www.astm.org/Standards/F320.html]
`
`Beraldin, J.-A., F. Blais, L. Cournoyer, M. Rioux, S.H. El-Hakim, R. Rodella, F. Bernier, and N.
`Harrison, 1999: 3D Digital Imaging and Modeling, Proceedings Second International
`Conference on 3D digital imaging and modelling, Ottawa, Canada, pp. 34-43.
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`Besl, P.J., 1988: Range Imaging Sensors. Machine Vision and Applications, 1, 127-152.
`
`Changnon, S.A., D. Changnon, and S. D. Hilberg, 2009: Hailstorms Across the Nation: An Atlas
`about Hail and its Damages. Illinois State Water Survey, 95pp.
`
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`FM Approvals, 2005: Specification test standard for impact resistance testing of rigid roofing
`materials by impacting with freezer ice balls (FM 4473), West Gloucester, RI, FM
`Approvals, 8 pp., https://www.fmglobal.com/assets/pdf/fmapprovals/4473.pdf.
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`Giammanco, I.M., T.M. Brown, R.G. Grant, D.L. Dewey, J.D. Hodel, and R.A. Stumpf, 2015:
`Evaluating the hardness characteristics of hail through compressive strength measurements.
`J. Atmos. Oceanic Tech., 32, 2100-2112.
`
`Heymsfield, A.J., I.M. Giammanco, and R.L. Wright, 2014: Terminal velocities and kinetic
`energies of natural hailstones. Geo. Phys. Res. Lett., 41, 8666-8672.
`
`Kim, H. and J. N. Keune, 2007: Compressive strength of ice at impact strain rates. J. Mater. Sci.,
`42, 2802-2806.
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`Knight, C.A. and N.C. Knight, 2001: Hailstorms. Severe Convective Storms, Meteor. Monogr.,
`No. 50, Amer. Meteor. Soc., 223-254.
`
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`Knight, C.A., P. T. Schlatter, and T. W. Schlatter, 2008: An unusual hailstorm on 24 June 2006
`in Boulder, Colorado. Part II: Low density growth of hail. Mon. Wea. Rev., 136, 2833-2848.
`
`Knight, N.C., 1986: Hailstone shape factor and its relationship to radar interpretation of hail. J.
`Climate Appl. Meteor., 25, 1956-1958.
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`Kunkel, K. and Coauthors, 2013: Monitoring and understanding trends in extreme storms. Bull.
`Amer. Meteor. Soc., 94 (4) 499-514.
`
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`Laurie, J. A. P., 1960: Hail and its effects on buildings. Council for Scientific and Industrial
`Research Rep. 176, 12 pp.
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`Mayer, R., 1999: Scientific Canadian: Invention and Innovation From Canada's National
`Research Council. Raincoast Books, 192 pp.
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`Morrison, H., A. Morales, and C. Villanueva-Birriel, 2015: Concurrent sensitivities of an
`idealized deep convective storm to parameterization of microphysics, horizontal grid
`resolution, and environmental static stability. Mon. Wea. Rev., 143, 2082-2104.
`
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`Roeder, P., Ed., 2012: Severe weather in North America: Perils, risks, and insurance.
`Knowledge Series: Natural Hazards, Munich RE Rep., 274 pp.
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`Straka, J.M., D.S. Zrnic, A.V. Ryzhkov, 2000: Bulk hydrometeor classification and
`quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39,
`1341-1372.
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`FIGURE CAPTIONS
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`Conceptua