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2001 Student Research


Severe Weather Associated with a Bow Echo and a Mesolow Complex

student: Diana Blahyj, State University of New York - Brockport
mentor: Dave Imy, Storm Prediction Center

abstract
Fast moving bow echoes typically produce a significant amount of wind damage, especially along the bowing portion of the line. On 12 March 2001, a line of severe thunderstorms with embedded bow echoes and mesolows moved rapidly through the southeastern United States. This bow echo produced wind damage along the bowing portion of the line early in the day, however, a majority of the severe weather events occurred with mesolows/mesocyclones located within the line after 1500 UTC. This paper focuses on the evolution of the mesolows and severe weather that occurred on this particular day.

 


The Relationship Of Large-Scale Surface Boundaries To Convective Initiation And Strong And Violent Tornado Occurrences

student: David Bolen - University of Northern Colorado
mentors: Bob Johns and John Hart, Storm Prediction Center

abstract
This preliminary study examines the occurrence of strong and violent tornadoes to determine the relationship between convective initiation, tornado occurrence, and boundary location. Tornadoes were gathered from events occurring during the warm (Jun.-Aug.) and cool (Jan.-Mar.) season portions of 1991, as well as a spring transition season major outbreak on 26 April. The majority of strong and violent tornado episodes occurred on or near preexisting surface boundaries. Isolated events (1-2 tornadoes) had the highest association with preexisting surface boundaries, while outbreak events (> 10 tornadoes) varied widely been episodes. This was especially true when comparing the outbreak of 26 April with the other outbreaks of the period.
Boundaries also served as favored spots for convective initiation, but the convection did not necessarily stay on the initiation boundaries until the tornado occurred. In fact, in 70% of the cases, the convection moved off of the boundary in which it initiated, and tornado(es) occurred on different boundaries or in different sectors. In the majority of these cases, the tornado(es) occurred on large-scale outflow boundaries generated by the convection or convective cluster. Some implications of future work are also discussed.

 


A New Perspective of the Climatology of Tornadoes

Student: Sara Bruening, Univerisity of Wisconsin at Milwaukee
Mentors: Mike Kay, Storm Prediction Center and Harold Brooks, National Severe Storms Laboratory

abstract
The climatology of tornadoes is considered using only the most fundamental aspects of the historical tornado record: the daily number of tornadoes and the annual number of tornadoes for the period 1955-1999. This study attempts to define what is a normal tornado year given the fact that the raw number of tornadoes has nearly doubled since 1955. Two methods are employed to determine a normal year: the first is to remove any secular trends from the observed data while the second uses a rudimentary numerical model to simulate 10,000 years of daily tornado activity. Once a normal year has been defined, climatological aspects of any year such as departures from normal can be computed. Results suggest that data through the end of April are required in order to make judgments about the outcome of the tornado year.

 


Radar Signatures Associated with Atypical Tornado-Producing Thunderstorms in Oklahoma

Student: Kristen Delack, Pennsylvania State University
Mentors: Dave Andra, Mike Foster, Dan Miller, National Weather Service Forecast Office

abstract
During the early morning hours of 11 April 2001 a mini outbreak of tornadoes occurred across central and southern Oklahoma. The storms that affected the area were not as strong or as organized as traditional tornadic supercell thunderstorms, nor were they low-topped like tornadic mini-supercell thunderstorms. Most of the tornadoes produced that morning, however, were rated F1 or F2 on the Fujita scale and were responsible for one fatality, five injuries, and over $3.1 million in damage. This case and two similar cases were examined to determine radar characteristics of these atypical tornadic storms. Analysis of Doppler radar-retrieved data, such as mesocyclone rotational velocity, mesocyclone diameter, height of maximum rotational velocity, and height of storm top was performed. Results indicated that these storms do not fit the conceptual model for classic, LP, HP, or mini-supercells. Rather, they reflected characteristics of both mini- and traditional supercells. The strength and diameter of the mesocyclones were similar to those of mini-supercells, however, the storm top heights were substantially higher than those of mini-supercells. The height of maximum rotational velocity was much lower than that of both mini- and traditional supercells. Although this is a limited data set, the conclusion can be made that these storms represent another region of the tornadic storm spectrum that is not well documented.

 


A Relationship Between Surface Equivalent Potential Temperature and Dominant Lightning Polarity

Student: Nettie Lake, Lyndon State College
Mentor: Don MacGorman, National Severe Storms Laboratory

abstract
Several studies have indicated that the dominant polarity of a thunderstorm may predict the severity of the thunderstorm. This study examines the differences in the location of thunderstorms dominated by positive cloud-to-ground lightning (initially positive storms) and of thunderstorms dominated by negative cloud-to-ground flashes (negative storms) relative to a surface equivalent potential temperature (theta-e) ridge. Previous studies suggest that a surface theta-e maximum separates initially positive storms on the upstream side of the ridge from negative storms on the downstream side. This study found that most initially positive storms form on the side of increasing theta-e relative to storm motion. Initially positive storms will usually cross the ridge axis and become dominated by negative cloud-to ground flashes. Other initially positive storms move adjacent to the ridge axis and remain positive. Some initially positive storms do not form near a theta-e ridge. Negative storms were thought to form downstream of a theta-e maximum, but this study found otherwise, as only 17% of negative storms examined formed downstream of the maximum. Nearly equal amounts of negative storms cross the axis, move adjacent to it, or do neither. Very few negative storms reversed dominant polarity, and if they did, it was during the final stages of the storms' duration.

 


Developing a Statistical Climatology of Storm Cell Characteristics

Student: Adam Lopes, Pennsylvania State University
Mentor: Kelvin Droegemeier, Center for Analysis and Prediction of Storms

abstract
The establishment of a statistical climatology of storm cell characteristics has long been a goal of the atmospheric research community. Yet, the large amounts of data necessary for such a project, coupled with an incomplete radar Level II data archive, made this sort of endeavor impossible. In 1998, however, the Center for Analysis and Prediction of Storms at the University of Oklahoma began the Collaborative Radar Acquisition Field Test (or Project CRAFT) as an effort to equip WSR-88D radars with the capability to compress and transmit Level II data over the Internet in real time. For 2 years the CRAFT radars have been continuously transmitting data, assembling a Level II data archive that is nearly 100% complete. The success of Project CRAFT has presented the enabling technology to begin a statistical climatology of storm cell characteristics. Therefore, this pilot study was done in order to identify several methods through which a statistical climatology can be constructed. Storm events were selectively chosen for this study. After the Level II radar data from Dallas/Ft. Worth radar (KFWS) was collected for each event and run through the Storm Cell Identification and Tracking (SCIT) Algorithm, the data was analyzed several different ways. Analysis techniques include developing frequency distributions of cell characteristics, examining relationships among the cell characteristics, and plotting the cell characteristics spatially using GIS software. Spatial plots were then related to the locations of Dallas/Ft. Worth Airport and major airways into and out of this airport. These analysis techniques proved to be beneficial, as several conclusions were reached that may, upon the compilation of years of radar data, lead to the development of a credible statistical climatology of storm cell characteristics. Furthermore, the results presented from this small data set show great promise for the potential utility of such a climatology for air traffic considerations at large airports.

 


Are Biological Hotspots Dependent on Climate?

Student: Mario Lopez, University of Texas Pan American
Mentor: Ken Howard, National Severe Storm Laboratory

abstract
Biological hotspots are small geographical areas that contain much of Earth's biodiversity. Humans significantly threaten these areas and the species that reside in them with destruction. Moreover, because these biological hotspots occur in such small areas of the Earth, it is necessary to study whether a relationship exists between climates and the biological hotspots. Climate change could affect biological hotspots if such a relationship exists. This paper seeks to demonstrate that a direct relationship exists between climate regimes and biological hotspots.

 


The Effects of an Ice Parameterization Scheme on Ensemble Cloud Model Storm Lifetimes

Student: Alison Marr, Murray State
Mentor: Kim Elmore, National Severe Storms Laboratory

abstract
To date, ensemble cloud modeling has mainly been used as a research tool. An ensemble cloud model that uses the Kessler precipitation parameterization has been used to generate forecasts of storm lifetimes. This project examines how an ice parameterization scheme changes the ensemble storm lifetimes. For each of the six cases, the storm lifetimes from the model without ice and model with ice are compared to the observed storm lifetimes to determine if adding ice statistically improves the forecast storm lifetimes. Quantitatively, only one case shows improvement when ice is added to the model. However, qualitatively, a majority of the cases improve when ice is included.

 


A Statistical Model for Significant Tornado Hazards in the United States

Student: Cathryn Meyer, Boston College
Mentor: Harold Brooks, National Severe Storms Laboratory

abstract
A statistically based model of the hazard associated with significant tornadoes (F2 or greater on the Fujita scale) based on the reports recorded from 1921-1995 in the United States is developed. The model consists of four components. The first is a probability that at least one tornado will occur at a grid point (grid spacing is approximately 80 km on a side) on any day of the year at any location in the United States. The second component is a model of the number of tornadoes reported per day in a single grid box, given that at least one tornado occurs. Third, the intensity of each tornado on the Fujita scale is determined, based on the historical distribution of intensity. Finally, the path length and width of the tornado are modeled using Weibull distributions for each value of the F-scale. The model has been run for 30,000 years, with almost 4,000,000 tornadoes produced in order to develop reliable statistics. The peak areal coverage of tornadoes (at a grid point in southern Oklahoma) is about 3% of a grid box per century, implying a return period for strong and violent tornadoes at any point in that grid box of about 3000 years. The model also shows the dangers of using a short period of record to estimate tornado hazards. Differences in 15-year periods of 50% in mean tornado occurrence are observed in the model, even without changing any of the parameters. This result has significance for the assessment of risk using the 'raw' observations and for detection of changes in the climatology of tornado occurrence.

 


Procedure for Refurbishing Balloon-Borne Electric Field Meters

Student; Jason Tomlison, Valparaiso University
Mentor: Dave Rust, National Severe Storms Laboratory

abstract