Four different versions of the HAILCAST hail model have been tested as part of the 2014–16 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments. HAILCAST was run as part of the National Severe Storms Laboratory (NSSL) WRF Ensemble during 2014–16 and the Community Leveraged Unified Ensemble (CLUE) in 2016. Objective verification using the Multi-Radar Multi-Sensor maximum expected size of hail (MRMS MESH) product was conducted using both object-based and neighborhood grid-based verification. Subjective verification and feedback was provided by HWT participants. Hourly maximum storm surrogate fields at a variety of thresholds and Storm Prediction Center (SPC) convective outlooks were also evaluated for comparison. HAILCAST was found to improve with each version due to feedback from the 2014–16 HWTs. The 2016 version of HAILCAST was equivalent to or exceeded the skill of the tested storm surrogates across a variety of thresholds. The post-2016 version of HAILCAST was found to improve 50-mm hail forecasts through object-based verification, but 25-mm hail forecasting ability declined as measured through neighborhood grid-based verification. The skill of the storm surrogate fields varied widely as the threshold values used to determine hail size were varied. HAILCAST was found not to require such tuning, as it produced consistent results even when used across different model configurations and horizontal grid spacings. Additionally, different storm surrogate fields performed at varying levels of skill when forecasting 25- versus 50-mm hail, hinting at the different convective modes typically associated with small versus large sizes of hail. HAILCAST was able to match results relatively consistently with the best-performing storm surrogate field across multiple hail size thresholds.
Spain, E. A.; Johnson, S. C.; Hutton, B.; Whittaker, J. M.; Lucieer, V.; Watson, S. J.; Fox, J. M.; Lupton, J.; Arculus, R.; Bradney, A.; Coffin, M. F.;
Published Date:
2020
Source:
Earth and Space Science, 7(3)
Description:
Bubble emission mechanisms from submerged large igneous provinces remains enigmatic. The Kerguelen Plateau, a large igneous province in the southern Indian Ocean, has a long sustained history of active volcanism and glacial/interglacial cycles of sed...
J. Atmos. Oceanic Technol. (2018) 35 (11): 2169–2187.
Description:
Time series simulation is an important tool for developing and testing new signal processing algorithms for weather radar. The methods for simulating time series data have not changed much over the last few decades, but recent advances in computing t...
Rosenow, Andrew A.; Howard, Kenneth; Meitín, José G.;
Published Date:
2018
Source:
Mon. Wea. Rev. (2018) 146 (8): 2469–2481.
Description:
On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1...
The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physi...
Potvin, Corey K.; Broyles, Chris; Skinner, Patrick S.; Brooks, Harold E.; Rasmussen, Erik;
Published Date:
2019
Source:
Wea. Forecasting (2019) 34 (1): 15–30.
Description:
The Storm Prediction Center (SPC) tornado database, generated from NCEI’s Storm Data publication, is indispensable for assessing U.S. tornado risk and investigating tornado–climate connections. Maximizing the value of this database, however, requ...
A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-19...
Observations from three nights of the Plains Elevated Convection at Night (PECAN) field campaign were used in conjunction with Rapid Refresh model forecasts to find the cause of north–south lines of convection, which initiated away from obvious sur...
Jones, Thomas A.; Skinner, Patrick; Knopfmeier, Kent; Mansell, Edward; Minnis, Patrick; Palikonda, Rabindra; Smith, William Jr.;
Published Date:
2018
Source:
Wea. Forecasting (2018) 33 (6): 1681–1708.
Description:
Forecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project ...
Flora, Montgomery L.; Potvin, Corey K.; Wicker, Louis J.;
Published Date:
2018
Source:
Mon. Wea. Rev. (2018) 146 (8): 2361–2379.
Description:
As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensi...
Wade, Andrew R.; Coniglio, Michael C.; Ziegler, Conrad L.;
Published Date:
2018
Source:
Mon. Wea. Rev. (2018) 146 (8): 2403–2415.
Description:
A great deal of research focuses on how the mesoscale environment influences convective storms, but relatively little is known about how supercells modify the nearby environment. Soundings from three field experiments are used to investigate differen...
Tornado warnings are one of the flagship products of the National Weather Service. We update the time series of various metrics of performance in order to provide baselines over the 1986–2016 period for lead time, probability of detection, false al...
Cai, C.; Avise, J.; Kaduwela, A.; DaMassa, J.; Warneke, C.; Gilman, J. B.; Kuster, W.; de Gouw, J.; Volkamer, R.; Stevens, P.; Lefer, B.; Holloway, J. S.; Pollack, I. B.; Ryerson, T.; Atlas, E.; Blake, D.; Rappenglueck, B.; Brown, S. S.; Dube, W. P.;
Published Date:
2019
Source:
Journal of Geophysical Research: Atmospheres, 124(6)
Description:
United States Environmental Protection Agency guidance on the use of photochemical models for assessing the efficacy of an emissions control strategy for ozone requires that modeling be used in a relative sense. Consequently, testing a modeling syste...
Cui, Y. Y.; Henze, D. K.; Brioude, J.; Angevine, W. M.; Liu, Z.; Bousserez, N.; Guerrette, J.; McKeen, S. A.; Peischl, J.; Yuan, B.; Ryerson, T.; Frost, G.; Trainer, M.;
Published Date:
2019
Source:
Journal of Geophysical Research: Atmospheres, 124(6)
Description:
Quantifying methane (CH4) emissions from the oil and natural gas (O/NG) production sector is an important regulatory challenge in the United States. In this study, we conduct a set of inversion calculations using different methods to quantify lognorm...
Journal of the Association of Environmental and Resource Economists,7(3)
Description:
Estimating nonmarket benefits for erosion protection can help inform better decision making and policies for communities to adapt to climate change. We estimate private values for a coastal protection option in an empirical setting subject to irrever...
Koch, Steven E.; Fengler, Martin; Chilson, Philip B., 1963-; Elmore, Kimberly L.; Argrow, Brian M.; Andra, David; Lindley, T. Todd;
Published Date:
2018
Source:
J. Atmos. Oceanic Technol. (2018) 35 (11): 2265–2288.
Description:
The potential value of small unmanned aircraft systems (UAS) for monitoring the preconvective environment and providing useful information in real time to weather forecasters for evaluation at a National Weather Service (NWS) Forecast Office are addr...
Gallo, Burkely T.; Clark, Adam J.; Smith, Bryan T.; Thompson, Richard L.; Jirak, Israel; Dembek, Scott R.;
Published Date:
2019
Source:
Wea. Forecasting (2019) 34 (1): 151–164.
Description:
Probabilistic ensemble-derived tornado forecasts generated from convection-allowing models often use hourly maximum updraft helicity (UH) alone or in combination with environmental parameters as a proxy for right-moving (RM) supercells. However, when...
Petrolito, Anthony W.; Coleman, S. Hunter (Samuel Hunter); Linton, Richard J.;
Corporate Authors:
United States, National Weather Service
Published Date:
2012
Series:
Eastern Region technical attachment (National Weather Service (U.S.)) ; 2012-01
Description:
In the late 1990's, a probability of severe hail equation (LPSH75) for diameter greater than or equal to 0.75 in. (1.9 cm) was developed for the Columbia South Carolina County Warning Area (CAE CWA) using a logistic regression methodology. This equat...
United States, National Weather Service., Eastern Region,
Published Date:
2011
Series:
Eastern Region technical attachment (National Weather Service (U.S.)) ; 2011-05
Description:
The National Weather Service (NWS) changed the criterion for severe hail from 0.75 in (1.9 cm) to 1.00 in (2.5 cm) on 5 January 2010. Many techniques have been developed for forecasting severe hail, such as examining echo tops of various reflectivity...
Eastern Region technical attachment (National Weather Service (U.S.)) ; no. 2008-04
Description:
"The goal of this study was to develop a synoptic climatology of hail days in the Gray, ME (GYX) County Warning Area (CWA). Additionally, 'traditional' thermodynamic parameters for hail forecasting were reviewed, in order to determine their utility i...
Trapp, Robert J. (Robert Jeffrey), 1963-; Hoogewind, Kimberly A.; Lasher-Trapp, Sonia;
Published Date:
2019
Source:
J. Climate (2019) 32 (17): 5493–5509.
Description:
The effect of anthropogenically enhanced greenhouse gas concentrations on the frequency and intensity of hail depends on a range of physical processes and scales. These include the environmental support of the hail-generating convective storms and th...
Allen, John T.; Giammanco, Ian M.; Kumjian, Matthew R.; Punge, Heinz Jurgen; Zhang, Qinghong; Groenemeijer, Pieter; Kunz, Michael; Ortega, Kiel;
Published Date:
2020
Source:
Reviews of Geophysics, 58(1), e2019RG000665, 2020.
Description:
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 ...
File Type:
[PDF - 53.17 MB]
Exit
Notification/Disclaimer Policy
Links with this icon indicate that you are leaving the NOAA website.
The National Oceanic and Atmospheric Administration (NOAA)
cannot attest to the accuracy of a non-federal website.
Linking to a non-federal Website does not constitute an
endorsement by NOAA or any of its employees of the sponsors
or the information and products presented on the website.
You will be subject to the destination website's privacy
policy when you follow the link.
NOAA is not responsible for Section 508 compliance
(accessibility) on other federal or private websites.