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Adam J. Clark
,
Andrew MacKenzie
,
Amy McGovern
,
Valliappa Lakshmanan
, and
Rodger A. Brown

Abstract

Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.

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David John Gagne II
,
Amy McGovern
,
Jeffrey B. Basara
, and
Rodger A. Brown

Abstract

Oklahoma Mesonet surface data and North American Regional Reanalysis data were integrated with the tracks of over 900 tornadic and nontornadic supercell thunderstorms in Oklahoma from 1994 to 2003 to observe the evolution of near-storm environments with data currently available to operational forecasters. These data are used to train a complex data-mining algorithm that can analyze the variability of meteorological data in both space and time and produce a probabilistic prediction of tornadogenesis given variables describing the near-storm environment. The algorithm was assessed for utility in four ways. First, its probability forecasts were scored. The algorithm did produce some useful skill in discriminating between tornadic and nontornadic supercells as well as in producing reliable probabilities. Second, its selection of relevant attributes was assessed for physical significance. Surface thermodynamic parameters, instability, and bulk wind shear were among the most significant attributes. Third, the algorithm’s skill was compared with the skill of single variables commonly used for tornado prediction. The algorithm did noticeably outperform all of the single variables, including composite parameters. Fourth, the situational variations of the predictions from the algorithm were shown in case studies. They revealed instances both in which the algorithm excelled and in which the algorithm was limited.

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Jessica L. Proud
,
Kelvin K. Droegemeier
,
Vincent T. Wood
, and
Rodger A. Brown

Abstract

Increasing tornado and severe storm warning lead time (lead time is defined here as the elapsed time between the issuance of a watch or warning and the time at which the anticipated weather event first impacts the specified region) through the use of radar observations has long been a challenge for researchers and operational forecasters. To improve lead time and the probability of detecting tornadoes while decreasing the false alarm ratio, a greater understanding, obtained in part by more complete observations, is needed about the region of storms within which tornadoes form and persist. Driven in large part by this need, but also by the goal of using numerical models to explicitly predict intense local weather such as thunderstorms, the National Science Foundation established, in fall 2003, the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). CASA is developing a revolutionary new paradigm of using a network of small, closely spaced, inexpensive, low-power dual-polarization Doppler weather radars to overcome the inability of widely spaced, high-power radars to sample large regions of the lower atmosphere owing to the curvature of earth given that zero or negative beam elevation angles are not allowed. Also, current radar technology operates mostly independently of the weather and end-user needs, thus producing valuable information on storms as a whole but not focused on any specific phenomenon or need. Conversely, CASA utilizes a dynamically adaptive sensing paradigm to identify, and optimally sample, multiple targets based upon their observed characteristics in order to meet a variety of often competing end-user needs.

The goal of this study is to evaluate a variety of adaptive sampling strategies for CASA radars to assess their effectiveness in identifying intense low-altitude vortices. Such identification, for the purposes of this study, is defined as achieving a best fit of simulated observations to an analytic model of a tornado or mesocyclone. Several parameters are varied in this study including the size of the vortex, azimuthal sampling interval, distance of the vortex from the radar, and radar beamwidth.

Results show that, in the case of small vortices, adaptively decreasing the azimuthal sampling interval (i.e., overlapping beams) is beneficial in comparison to conventional azimuthal sampling that is approximately equal to the beamwidth. However, the benefit is limited to factors of 2 in overlapping. When simulating the performance of a CASA radar in comparison to that of a Weather Surveillance Radar-1988 Doppler (WSR-88D) at close range, with both operating in the conventional nonoverlapping mode, the WSR-88D (with a beamwidth about half that of a CASA radar) performs better. However, when overlapping is applied to the CASA radar, for which little additional processing time is required, the results are comparable. In effect, the sampling resolution of a radar can be increased simply by decreasing the azimuthal sampling interval as opposed to installing a larger antenna.

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Rodger A. Brown
,
Donald W. Burgess
,
John K. Carter
,
Leslie R. Lemon
, and
Dale Sirmans

Some results of the first 10 cm dual-Doppler radar measurements in a tornadic storm are presented. A mesoscale cyclonic circulation confirms proposed single Doppler vortex signature and indicates that the curved reflectivity hook echo is around the periphery of the circulation. The interpolated tornado position is within the mesocyclone where high-variance Doppler velocity spectra suggest strong velocity gradients.

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Leslie R. Lemon
,
Ralph J. Donaldson Jr.
,
Donald W. Burgess
, and
Rodger A. Brown

Significant advances in single-Doppler radar application to severe storm study and identification have been made since 1965. Mesocyclones have been detected by Doppler radar and found to precede severe weather occurrence by several tens of minutes. A typical mesocyclone evolution leading to tornado development has also been documented. The tornado vortex itself has a revealing signature in Doppler radar data, the tornadic vortex signature (TVS). Statistics of both mesocyclone and TVS association with confirmed severe weather are presented in this paper. Doppler radar provides the potential for improving severe thunderstorm warnings. Experiments are underway to test the operational use of this new tool in storm warning and flight advisory services.

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Rodger A. Brown
,
William C. Bumgarner
,
Kenneth C. Crawford
, and
Dale Sirmans

Single Doppler radar measurements were made in a squall line that formed in southern Kansas during the afternoon of 2 June 1971 and moved south-southeastward through central Oklahoma. During the period of data collection, a pronounced hook echo, having at least one funnel cloud associated with it, developed. Preliminary analyses of these first Doppler velocity measurements within a radar hook echo in the tornado belt are presented.

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Rodger A. Brown
,
Vincent T. Wood
,
Randy M. Steadham
,
Robert R. Lee
,
Bradley A. Flickinger
, and
Dale Sirmans

Abstract

For the first time since the installation of the national network of Weather Surveillance Radar-1988 Doppler (WSR-88D), a new scanning strategy—Volume Coverage Pattern 12 (VCP 12)—has been added to the suite of scanning strategies. VCP 12 is a faster version of VCP 11 and has denser vertical sampling at lower elevation angles. This note discusses results of field tests in Oklahoma and Mississippi during 2001–03 that led to the decision to implement VCP 12. Output from meteorological algorithms for a test-bed radar using an experimental VCP were compared with output for a nearby operational WSR-88D using VCP 11 or 21. These comparisons were made for severe storms that were at comparable distances from both radars. Findings indicate that denser vertical sampling at lower elevation angles leads to earlier and longer algorithm identifications of storm cells and mesocyclones, especially those more distant from a radar.

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Rodger A. Brown
,
Bradley A. Flickinger
,
Eddie Forren
,
David M. Schultz
,
Dale Sirmans
,
Phillip L. Spencer
,
Vincent T. Wood
, and
Conrad L. Ziegler

Abstract

Doppler velocity and reflectivity measurements from Weather Surveillance Radar-1988 Doppler (WSR-88D) radars provide important input to forecasters as they prepare to issue short-term severe storm and tornado warnings. Current-resolution data collected by the radars have an azimuthal spacing of 1.0° and range spacing of 1.0 km for reflectivity and 0.25 km for Doppler velocity and spectrum width. To test the feasibility of improving data resolution, National Severe Storms Laboratory’s test bed WSR-88D (KOUN) collected data in severe thunderstorms using 0.5°-azimuthal spacing and 0.25-km-range spacing, resulting in eight times the resolution for reflectivity and twice the resolution for Doppler velocity and spectrum width. Displays of current-resolution WSR-88D Doppler velocity and reflectivity signatures in severe storms were compared with displays showing finer-resolution signatures. At all ranges, fine-resolution data provided better depiction of severe storm characteristics. Eighty-five percent of mean rotational velocities derived from fine-resolution mesocyclone signatures were stronger than velocities derived from current-resolution signatures. Likewise, about 85% of Doppler velocity differences across tornado and tornadic vortex signatures were stronger than values derived from current-resolution data. In addition, low-altitude boundaries were more readily detected using fine-resolution reflectivity data. At ranges greater than 100 km, fine-resolution reflectivity displays revealed severe storm signatures, such as bounded weak echo regions and hook echoes, which were not readily apparent on current-resolution displays. Thus, the primary advantage of fine-resolution measurements over current-resolution measurements is the ability to detect stronger reflectivity and Doppler velocity signatures at greater ranges from a WSR-88D.

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Bruce A. Boe
,
Jeffrey L. Stith
,
Paul L. Smith
,
John H. Hirsch
,
John H. Helsdon Jr.
,
Andrew G. Detwiler
,
Harold D. Orville
,
Brooks E. Mariner
,
Roger F. Reinking
,
Rebecca J. Meitín
, and
Rodger A. Brown

The North Dakota Thunderstorm Project was conducted in the Bismarck, North Dakota, area from 12 June through 22 July 1989. The project deployed Doppler radars, cloud physics aircraft, and supporting instrumentation to study a variety of aspects of convective clouds. These included transport and dispersion; entrainment; cloud-ice initiation and evolution; storm structure, dynamics, and kinematics; atmospheric chemistry; and electrification.

Of primary interest were tracer experiments that identified and tracked specific regions within evolving clouds as a means of investigating the transport, dispersion, and activation of ice-nucleating agents as well as studying basic transport and entrainment processes. Tracers included sulfur hexafluoride (SF6), carbon monoxide, ozone, radar chaff, and silver iodide.

Doppler radars were used to perform studies of all scales of convection, from first-echo cases to a mesoscale convective system. An especially interesting dual-Doppler study of two splitting thunderstorms has resulted.

The objectives of the various project experiments and the specific facilities employed are described. Project highlights and some preliminary results are also presented.

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