Evaluation of Alberta Hail Growth Model Using Severe Hail Proximity Soundings from the United States

Jewell, Ryan E.; Brimelow, Julian C.

doi:10.1175/2009waf2222230.1

Cited by 51 publications

(45 citation statements)

References 28 publications

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“…Adding to this challenge, relationships between hail size and environment have proven particularly difficult to determine with certainty due to comparatively small sample sizes of reliable hail observations, and regional variations in the necessary parameters (e.g. Brimelow et al, ; Edwards & Thompson, ; Groenemeijer & van Delden, ; Jewell & Brimelow, ; Johnson & Sugden, ; Manzato, ; Púčik et al, ; Smith et al, ; Tuovinen et al, ). The issues limiting this type of approach arise partly because of the microphysical impacts on the growth rate of hail and the role of storm mode (Gagne et al, ; Grams et al, ; Smith et al, ), both of which cannot be inferred solely from proximity soundings with a reasonable level of confidence.…”

Section: Environmental Forecasting Parameters and Climatologymentioning

confidence: 99%

Understanding Hail in the Earth System

Allen

Giammanco

Kumjian

et al. 2020

Reviews of Geophysics

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The processes leading to the development of hail and the distribution of these events worldwide are reviewed. Microphysical and physical characteristics of hail development are described to provide context of the notable gaps in our understanding of what drives hail to grow large, or what determines how it falls to the ground. Distributional characteristics of hail are explored, utilizing both surface observations of hailstones and remotely sensed observational data sets to identify opportunities and needs for new observations. These observational deficiencies contribute to our limited capacity to both forecast hail or its expected size and reduce the effectiveness of using favorable conditions for hail development as a proxy to frequency where observations are unavailable. Given the substantive influences of both climate variability and the changing Earth system on hail, the latest understanding of their contributions to risk are addressed. Applying this understanding of the distribution and physical characteristics of hail, the damage by hail to agriculture and insured property is assessed. Much remains unknown about the processes leading to hail growth and environmental controls on hail occurrence, size, and magnitude, particularly outside of the United States and Europe. A better understanding of the global occurrence of hail is also needed to better anticipate the hazard and associated impacts.

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Section: Environmental Forecasting Parameters and Climatologymentioning

confidence: 99%

Understanding Hail in the Earth System

Allen

Giammanco

Kumjian

et al. 2020

Reviews of Geophysics

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“…Hail occurrence has previously been related to the thermodynamic potential of the severe thunderstorm environment [ Stumpf et al ., ; Giaiotti et al ., ; Groenemeijer and Van Delden , ; Kunz et al ., ; Grams et al ., ; Thompson et al ., ], particularly the steepness of midlevel lapse rates, suspension of ice nuclei in the optimum hail growth region, as well as moisture availability. While thermodynamic sources of energy are essential to promote the strong updrafts that support hail, shear enhancement of the vertical pressure gradient provides another important contribution [ Edwards and Thompson , ; Doswell and Markowski , ; Jewell and Brimelow , ; Grams et al ., ; Manzato , ]. The interaction between thermodynamic potential and vertical wind shear is quantified, for example, in the Significant Hail Parameter (SHIP; details of the formula for SHIP can be found at http://www.spc.noaa.gov/exper/mesoanalysis/help/help_sigh.html).…”

Section: Introductionmentioning

confidence: 99%

“…Attempts to relate hail occurrence to the large‐scale environment have generally focused on small regions utilizing synoptic composites [e.g., Cao , ; Kapsch et al ., ] or station proximity analyses [e.g., Edwards and Thompson , ]. More recently, empirical hail models have been applied to estimate frequency or size from the environmental conditions [e.g., Jewell and Brimelow , ; Sanderson et al ., ]. A small number of studies have investigated both univariate and multivariate, as well as linear and nonlinear discriminants for hail [e.g., Manzato , ; Eccel et al ., ; Manzato , ].…”

Section: Introductionmentioning

confidence: 99%

An empirical model relating U.S. monthly hail occurrence to large‐scale meteorological environment

Allen

Tippett

Sobel

2015

J Adv Model Earth Syst

107

147

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An empirical model relating monthly hail occurrence to the large-scale environment has been developed and tested for the United States (U.S.). Monthly hail occurrence for each 1 31 grid box is defined as the number of hail events that occur there during a month; a hail event consists of a 3 h period with at least one report of hail larger than 1 in. The model is derived using climatological annual cycle data only. Environmental variables are taken from the North American Regional Reanalysis (NARR; 1979-2012.The model includes four environmental variables convective precipitation, convective available potential energy, storm relative helicity, and mean surface to 90 hPa specific humidity. The model differs in its choice of variables and their relative weighting from existing severe weather indices. The model realistically matches the annual cycle of hail occurrence both regionally and for the contiguous U.S. (CONUS). The modeled spatial distribution is also consistent with the observed hail climatology. However, the westward shift of maximum hail frequency during the summer months is delayed in the model relative to observations, and the model has a lower frequency of hail just east of the Rocky Mountains compared to observations. Year-to-year variability provides an independent test of the model. On monthly and annual time scales, the model reproduces observed hail frequencies. Overall model trends are small compared to observed changes, suggesting that further analysis is necessary to differentiate between physical and nonphysical trends. The empirical hail model provides a new tool for exploration of connections between large-scale climate and severe weather.

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“…In this article, we use an observational analysis of an elevated MCS's near environment to demonstrate how numerical weather prediction (NWP) models' moisture errors within an MCS's EIL influence the ability of these models to accurately predict the MCS. Horizontal and temporal variations in CAPE and CIN influence an MCS's intensity, what direction it will move, the likelihood of the MCS maintaining itself (e.g., Crook and Moncrieff 1988;Coniglio et al 2007;Jirak and Cotton 2007;Trier et al 2014a,b;Peters and Schumacher 2016), and a storm's ability to produce hail (e.g., Brooks et al 2003;Edwards and Thompson 1998;Jewell and Brimelow 2009), tornadoes (e.g., Smith et al 2012; Thompson et al 2012), and damaging winds (e.g., Mahoney et al 2009). The presence of nonzero CIN implies that a parcel is negatively buoyant prior to reaching its level of free convection (LFC; the level after which an ascending parcel attains positive buoyancy relative to its surrounding environment).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Low-Level Moisture Errors on Model Forecasts of an MCS Observed during PECAN

Peters

Nielsen

Parker

et al. 2017

Monthly Weather Review

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This article investigates errors in forecasts of the environment near an elevated mesoscale convective system (MCS) in Iowa on 24-25 June 2015 during the Plains Elevated Convection at Night (PECAN) field campaign. The eastern flank of this MCS produced an outflow boundary (OFB) and moved southeastward along this OFB as a squall line. The western flank of the MCS remained quasi stationary approximately 100 km north of the system's OFB and produced localized flooding. A total of 16 radiosondes were launched near the MCS's eastern flank and 4 were launched near the MCS's western flank.Convective available potential energy (CAPE) increased and convective inhibition (CIN) decreased substantially in observations during the 4 h prior to the arrival of the squall line. In contrast, the model analyses and forecasts substantially underpredicted CAPE and overpredicted CIN owing to their underrepresentation of moisture. Numerical simulations that placed the MCS at varying distances too far to the northeast were analyzed. MCS displacement error was strongly correlated with models' underrepresentation of low-level moisture and their associated overrepresentation of the vertical distance between a parcel's initial height and its level of free convection (Dz LFC , which is correlated with CIN). The overpredicted Dz LFC in models resulted in air parcels requiring unrealistically far northeastward travel in a region of gradual meso-a-scale lift before these parcels initiated convection. These results suggest that erroneous MCS predictions by NWP models may sometimes result from poorly analyzed low-level moisture fields.

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Evaluation of Alberta Hail Growth Model Using Severe Hail Proximity Soundings from the United States

Cited by 51 publications

References 28 publications

Understanding Hail in the Earth System

Understanding Hail in the Earth System

An empirical model relating U.S. monthly hail occurrence to large‐scale meteorological environment

The Impact of Low-Level Moisture Errors on Model Forecasts of an MCS Observed during PECAN

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