Surface measurements of the 5 June 2013 damaging thunderstorm wind event near Pep, Texas

Gunter, W. Scott; Schroeder, John L.; Weiss, Christopher C.; Bruning, Eric C.

doi:10.12989/was.2017.24.2.185

Cited by 9 publications

(2 citation statements)

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“…This aspect of the data is largely driven by specific convective events. Most of the 2013 events occurred on the same day with a damaging bow echo and subsequent wake low on 5 June (Gunter et al 2017). The fewest severe convective gusts were in 2011, during an intense drought that occurred across much of the Great Plains (Fernando et al 2016).…”

Section: B Temporal Distributionmentioning

confidence: 99%

Mesonet-Based Climatology Of Severe Convective Winds in West Texas

Gunter,

Long

2024

EJSSM

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Multiple studies have investigated the occurrence of severe convective winds and have increased our understanding of the forces driving severe winds and their spatial and temporal patterns. Some of the data used in studies have come from airport stations maintained by the National Weather Service. Their standardization across the United States makes them ideal for research, but they are limited in their distribution. This study aims to create a climatology of severe convective winds in West Texas using a mesoscale network (“mesonet”). Like their ASOS (Automated Surface Observing System) counterparts, these stations are standardized and well maintained. This study provides a 15-year climatology of severe convective wind gusts measured by the West Texas Mesonet (WTM). After extracting and manually verifying the measured gusts from over 30 WTM stations, both spatial and temporal distributions are presented. While temporal patterns in the gust distribution generally matched previous research, the high spatial resolution of the mesonet elucidated differences across a regional escarpment known as the Caprock. Comparison with regional AWOS / ASOS stations also revealed potential effects of a larger urban area. In addition to gust data, thermodynamic characteristics and rainfall accumulations associated with each gust were also investigated. In doing so, a substantial contribution from dry thunderstorm outflow winds and heat bursts to the production of regional severe wind was documented.

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Section: B Temporal Distributionmentioning

confidence: 99%

Mesonet-Based Climatology Of Severe Convective Winds in West Texas

Gunter,

Long

2024

EJSSM

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“…In support of VORTEX-SE, a fleet of portable, nearsurface in situ sampling platforms-StickNets (Schroeder and Weiss 2008;Weiss and Schroeder 2008)-was deployed by Texas Tech University to sample near-storm environmental heterogenieties (e.g., McDonald and Weiss 2021) and supplement the existing observational network in northern Alabama and southern Tennessee. Whereas StickNet applications in rapid-deployment scenarios have been well documentedprimarily sampling supercell cold pools and outflow wind gusts (e.g., Skinner et al 2011Skinner et al , 2014Weiss et al 2015;Gunter et al 2017)-their utility as a portable, quickly deployed, and stationary near-surface sampling network for severe storm environments is relatively new. The placement of a dense, high-frequency in situ observation network during VORTEX-SE, sampling a relatively data-sparse region, provides a unique and valuable dataset to investigate forecast improvements of severe storms and their hazards in the Southeastern United States.…”

Section: Introductionmentioning

confidence: 99%

Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE

Hill

Weiss²,

Dowell

2021

Weather and Forecasting

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Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification and Origins of Rotation in Tornadoes EXperiment – Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the southeast United States. StickNet observations are assimilated with an experimental version of the HighResolution RapidRefresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

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