Search Results

You are looking at 101 - 110 of 914 items for :

  • Tornadogenesis x
  • Refine by Access: All Content x
Clear All
Zoe Schroder
and
James B. Elsner

the available statistical guidance for predicting outbreak characteristics particularly when combined with other models. Fig . 1. Example tornado clusters. Each point is the tornadogenesis location shaded by EF rating. The black line is the spatial extent of the tornadoes occurring on that convective day and is defined by the minimum convex hull encompassing the set of locations. In this paper, we focus on outbreaks rather than on individual tornadoes. The larger space and time scales associated

Free access
Johannes M. L. Dahl

development of tornado–cyclone-scale vorticity maxima at the lowest model level. That is, only the seed near-ground rotation for possible tornadogenesis is addressed herein. Whether or not this vorticity is actually concentrated into a strong tornado-like vortex by vertical stretching is a separate problem not considered in this study [but it is discussed elsewhere, e.g., by Markowski and Richardson (2014) ]. 2. Methods a. Experimental design The goal is to produce simulations of a supercell that

Full access
Richard Rotunno
,
George H. Bryan
,
David S. Nolan
, and
Nathan A. Dahl

Abstract

This study is the first in a series that investigates the effects of turbulence in the boundary layer of a tornado vortex. In this part, axisymmetric simulations with constant viscosity are used to explore the relationships between vortex structure, intensity, and unsteadiness as functions of diffusion (measured by a Reynolds number Re r ) and rotation (measured by a swirl ratio S r ). A deep upper-level damping zone is used to prevent upper-level disturbances from affecting the low-level vortex. The damping zone is most effective when it overlaps with the specified convective forcing, causing a reduction to the effective convective velocity scale W e . With this damping in place, the tornado-vortex boundary layer shows no sign of unsteadiness for a wide range of parameters, suggesting that turbulence in the tornado boundary layer is inherently a three-dimensional phenomenon. For high Re r , the most intense vortices have maximum mean tangential winds well in excess of W e , and maximum mean vertical velocity exceeds 3 times W e . In parameter space, the most intense vortices fall along a line that follows , in agreement with previous analytical predictions by Fiedler and Rotunno. These results are used to inform the design of three-dimensional large-eddy simulations in subsequent papers.

Full access
Fanyou Kong
,
Kelvin K. Droegemeier
, and
Nicki L. Hickmon

Abstract

In Part I, the authors used a full physics, nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing to generate the ensemble forecasts of an observed tornadic thunderstorm complex. The principal goal was to quantify the value added by fine grid spacing, as well as the assimilation of Doppler radar data, in both probabilistic and deterministic frameworks. The present paper focuses exclusively on 3-km horizontal grid spacing ensembles and the associated impacts on the forecast quality of temporal forecast sequencing, the construction of initial perturbations, and data assimilation. As in Part I, the authors employ a modified form of the scaled lagged average forecasting technique and use Stage IV accumulated precipitation estimates for verification. The ensemble mean and spread of accumulated precipitation are found to be similar in structure, mimicking their behavior in global models. Both the assimilation of Doppler radar data and the use of shorter (1–2 versus 3–5 h) forecast lead times improve ensemble precipitation forecasts. However, even at longer lead times and in certain situations without assimilated radar data, the ensembles are able to capture storm-scale features when the associated control forecast in a deterministic framework fails to do so. This indicates the potential value added by ensembles although this single case is not sufficient for drawing general conclusions. The creation of initial perturbations using forecasts of the same grid spacing shows no significant improvement over simply extracting perturbations from forecasts made at coarser spacing and interpolating them to finer grids. However, forecast quality is somewhat dependent upon perturbation amplitude, with smaller scaling values leading to significant underdispersion. Traditional forecast skill scores show somewhat contradictory results for accumulated precipitation, with the equitable threat score most consistent with qualitative performance.

Full access
Robert Davies-Jones
,
Vincent T. Wood
, and
Mark A. Askelson

Abstract

Two accepted postulates for applications of ground-based weather radars are that Earth’s surface is a perfect sphere and that all the rays launched at low-elevation angles have the same constant small curvature. To accommodate a straight vertically launched ray, we amend the second postulate by making the ray curvature dependent on the cosine of the launch angle. A standard atmospheric stratification determines the ray-curvature value at zero launch angle. Granted this amended postulate, we develop exact formulas for ray height, ground range, and ray slope angle as functions of slant range and launch angle on the real Earth. Standard practice assumes a hypothetical equivalent magnified earth, for which the rays become straight while ray height above radar level remains virtually the same function of the radar coordinates. The real-Earth and equivalent-earth formulas for height agree to within 1 m. Our ultimate goal is to place a virtual Doppler radar within a numerical or analytical model of a supercell and compute virtual signatures of simulated storms for development and testing of new warning algorithms. Since supercell models have a flat lower boundary, we must first compute the ray curvature that preserves the height function as the earth curvature tends to zero. Using an approximate height formula, we find that keeping planetary curvature minus the ray curvature at zero launch angle constant preserves ray height to within 5 m. For standard refraction the resulting ray curvature is negative, indicating that rays bend concavely upward relative to a flat earth.

Full access
Patrick S. Skinner
,
Christopher C. Weiss
,
Louis J. Wicker
,
Corey K. Potvin
, and
David C. Dowell

gradient acceleration) remaining below 0.1 hPa (0.01 m 2 s −2 ) across the Dumas supercell. REFERENCES Adlerman , E. J. , 2003 : Numerical simulations of cyclic storm behavior: Mesocyclogenesis and tornadogenesis. Ph.D. thesis, University of Oklahoma, 217 pp. [Available from School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, Norman, OK 73072.] Adlerman , E. J. , K. K. Droegemeier , and R. Davies-Jones , 1999 : A numerical simulation of cyclic

Full access
Chad M. Shafer
,
Andrew E. Mercer
,
Charles A. Doswell III
,
Michael B. Richman
, and
Lance M. Leslie

Abstract

Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

Full access
Andrew E. Mercer
,
Chad M. Shafer
,
Charles A. Doswell III
,
Lance M. Leslie
, and
Michael B. Richman

Abstract

Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

Full access
James B. Elsner
,
Laura E. Michaels
,
Kelsey N. Scheitlin
, and
Ian J. Elsner

Abstract

Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.

Full access
Vincent T. Wood
and
Rodger A. Brown

Abstract

A tornadic vortex signature (TVS) is a degraded Doppler velocity signature that occurs when the tangential velocity core region of a tornado is smaller than the effective beamwidth of a sampling Doppler radar. Early Doppler radar simulations, which used a uniform reflectivity distribution across an idealized Rankine vortex, showed that the extreme Doppler velocity peaks of a TVS profile are separated by approximately one beamwidth. The simulations also indicated that neither the size nor the strength of the tornado is recoverable from a TVS. The current study was undertaken to investigate how the TVS might change if vortices having more realistic tangential velocity profiles were considered. The one-celled (axial updraft only) Burgers–Rott vortex model and the two-celled (annular updraft with axial downdraft) Sullivan vortex model were selected. Results of the simulations show that the TVS peaks still are separated by approximately one beamwidth—signifying that the TVS not only is unaffected by the size or strength of a tornado but also is unaffected by whether the tornado structure consists of one or two cells.

Full access