DOCSLIB.ORG

Topography and Land-Cover Effects on Tornado Intensity Using Rapid-Scan Mobile

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Topography and Land-Cover Effects on Tornado Intensity using Rapid-Scan Mobile Radar Observations and Geographic Information Systems A thesis presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Master of Science Nathaniel L. McGinnis December 2016 © 2016 Nathaniel L. McGinnis. All Rights Reserved. 2 This thesis titled Topography and Land-Cover Effects on Tornado Intensity using Rapid-Scan Mobile Radar Observations and Geographic Information Systems by NATHANIEL L. MCGINNIS has been approved for the Department of Geography and the College of Arts and Sciences by Jana L. Houser Assistant Professor of Geography Robert Frank Dean, College of Arts and Sciences 3 ABSTRACT MCGINNIS, NATHANIEL L., M.S., December 2016, Geography Topography and Land-Cover Effects on Tornado Intensity using Rapid-Scan Mobile Radar Observations and Geographic Information Systems Director of Thesis: Jana L. Houser High spatio-temporal datasets collected by two rapid-scan, mobile, Doppler radars (the University of Oklahoma’s rapid-scan, X-band, polarimetric (RaXPol) radar and the Naval Post-Graduate School’s Mobile Weather Radar 2005 X-Band Phased Array (MWR-05XP)) are used to investigate the relationships between tornado intensity and land cover type. Through the application of Geographic Information System techniques, elevation, slope, and aspect values are derived using the United States Geological Survey’s Digital Elevation Model. Additionally, surface roughness values are extracted using land-cover data from the USGS National Land-Cover Database and surface roughness values from the Environmental Protection Agency AERSURFACE User’s Guide. The extracted topographic and surface roughness values are then compared to the intensity values (∆Vmax) obtained through radar analysis. Linear correlations, comparison of means, and multiple linear regression techniques are used to test the significance of the data in order to determine the possible relationships between tornado intensity and topography/land-cover. While significant statistical relationships are found using these techniques, the relationships do not favor a specific direction and often reversed between cases. However, based upon the results from the multiple linear regression it is hypothesized that the radar beam height was a strong predictor of tornado intensity for 4 these particular cases, which implies that over all of the topographic and land-cover variables, the tornado intensity observed was significantly influenced by the location of the radar’s lowest-level beam height. Nevertheless, several unique topography/land-cover features did appear to affect tornado intensity, encouraging the continued investigation of the potential relationships, despite the contradicting statistical relationships found here. 5 ACKNOWLEDGMENTS Over the last two years, I have had the greatest privilege serving the Scalia Lab as Associate Director while completing my master’s degree. The journey was long, and there are many people that assisted me along the way. Firstly, to Dr. Ryan Fogt, thank you for allowing me the opportunity to serve the Scalia Lab. It truly was a great honor to carry that role, and I will never forget the many things holding that position as taught me. Additionally, your passion as an instructor and an advisor creates an environment where the program can thrive. I look forward to seeing the future growth of the program! To Dr. James Lein, thank you for your challenging insight and sincere investment into the furthering of my education. Your classes encouraged creativity which allowed me to develop and strengthen skills that will contribute to my future career. To Dr. Jana Houser, your expertise and passion for severe weather motivates every student you come into contact with. I am extremely grateful for the opportunities you provided to me over the last two years for which many had been lifelong dreams. I would also like to thank you for your inspiration regarding this thesis. As an advisor, you were always there to provide knowledge and ideas. I appreciate the many hours you spent assisting me with this project. It has and will continue to be an honor working with you. To my friends, Doug, Chad, Megan, and Hallie, the laughter during all of our adventures provided an escape from the stresses of graduate school. I can’t imagine what the journey would have been like without you all. 6 To my family, your love and encouragement pushed me through one of the biggest hurdles in my life. Through my moments of doubt, your words and prayers inspired me to continue on. Lastly, to my girlfriend Toni, these simple words do not cover the patience you graced me with over the last two years. Your love has been steadfast through all of my personal endeavors, and I can’t thank you enough. 7 TABLE OF CONTENTS Page Abstract……………………………………………………………………………………3 Acknowledgments…………………………………………………………………………5 List of Tables……………………………………………………………………………...9 List of Figures……………………………………………………………………………14 Chapter 1: Introduction and Motivation…………………………………………………19 1.1 Introduction…………………………………………………………..19 1.2 Motivation……………………………………………………………21 Chapter 2: Literature Review…………………………………………………………….24 2.1 Tornado Structure……………………………………………………24 2.2 Elevation and Land-Cover Effects…………………………………...33 2.2.1 Elevation…………………………………………………...34 2.2.2 Land-Cover………………………………………………...40 2.3 Storm Mechanisms…………………………………………………..45 Chapter 3: Instrumentation and Methodology…………………………………………...51 3.1 Instrumentation………………………………………………………51 3.2 Methodology…………………………………………………………54 3.2.1 Radar Analysis……………………………………………..54 3.2.2 GIS Analysis……………………………………………….56 3.2.3 Statistical Analysis…………………………………………64 3.3 Case Studies………………………………………………………….69 3.3.1 Goshen County, Wyoming- 5 June 2009…………………..69 3.3.2 Lookeba, Oklahoma- 24 May 2011………………………..71 3.3.3 El Reno/Piedmont, Oklahoma- 24 May 2011……………...73 3.3.4 Carney, Oklahoma- 19 May 2013………………………….78 3.3.5 Shawnee, Oklahoma- 19 May 2013………………………..80 3.4 Instrumentation and Methodology Limitations……………………...83 Chapter 4: Results………………………………………………………………………..89 8 4.1 Individual Cases……………………………………………………...89 4.1.1 Goshen County, Wyoming- 5 June 2009…………………..89 4.1.2 Lookeba, Oklahoma- 24 May 2011………………………103 4.1.3 El Reno/Piedmont, Oklahoma- 24 May 2011…………….114 4.1.4 Carney, Oklahoma- 19 May 2013………………………...157 4.1.5 Shawnee, Oklahoma- 19 May 2013………………………165 4.2 Cumulative Review…………………………………………………181 Chapter 5: Discussion…………………………………………………………………..191 Chapter 6: Conclusion…………………………………………………………………..199 References………………………………………………………………………………205 9 LIST OF TABLES Page Table 3.1: Basic information regarding the differences between the WSR-88D (Crum and Alberty 1993) and the two mobile radars, RaXPol (Pazmany et al. 2013) and MWR- 05XP (Bluestein et al. 2010). ............................................................................................ 51 Table 3.2: Surface roughness lengths from EPA’s AERSURFACE User’s Guide organized according to the 2011 land-cover data from NLCD. ........................................ 59 Table 3.3: Information summary regarding the time periods analyzed for the Goshen tornado. Tornadogenesis or Tornado Dissipation categories with N/A indicate that those processes were not associated with those periods............................................................. 71 Table 3.4: As in Table 3.3, but the Lookeba tornado. ...................................................... 73 Table 3.5: As in Table 3.3, but for the data collected by RaXPol during the El Reno tornado. ............................................................................................................................. 76 Table 3.6: As in Table 3.3, but for the data collected by the MWR-05XP during the El Reno tornado. .................................................................................................................... 78 Table 3.7: As in Table 3.3, but for the Carney tornado. ................................................... 80 Table 3.8: As in Table 3.3, but for the Shawnee tornado. ................................................ 83 Table 4.1: A summary of basic information for the second period of the Goshen County, WY case.. .......................................................................................................................... 90 Table 4.2: A summary of correlations and the associated probabilities for the topographic parameter, elevation, slope, and aspect. Bolded values are significant (p < 0.05 for a two- tailed t-test). ...................................................................................................................... 93 Table 4.3: Unstandardized coefficient summary from the multiple linear regression output containing the coefficient value, standard error, t-value, and probability for each variable used in test 1 (all predictors available are used). Probability values that are significant at the 95% confidence interval (p < 0.05) are bolded. .................................... 97 Table 4.4: As in test 1, but excluding beam height (test 2). ............................................. 97 Table 4.5: As in Table 4.1, but for the third observational period. ................................... 98 10 Table 4.6: As in Table 4.2, but for the second period of the Goshen case. .................... 101 Table 4.7: As in Table 4.3, but for time period 3............................................................ 103 Table 4.8: As in Table 4.4
Recommended publications
  • The Fujita Scale Is Used to Rate the Intensity of a Tornado by Examining the Damage Caused by the Tornado After It Has Passed Over a Man-Made Structure

    The Fujita Scale Is Used to Rate the Intensity of a Tornado by Examining the Damage Caused by the Tornado After It Has Passed Over a Man-Made Structure

    The Fujita Scale is used to rate the intensity of a tornado by examining the damage caused by the tornado after it has passed over a man-made structure. The "Percentage of All Tornadoes 1950- 1994" pie chart reveals that the vast majority of tornadoes are either weak or do damage that can only be attributed to a weak tornado. Only a small percentage of tornadoes can be correctly classed as violent. Such a chart became possible only after the acceptance of the Fujita Scale as the official classification system for tornado damage. It is quite possible that an even higher percentage of all tornadoes are weak. Each year the National Weather Service documents about 1000 tornado touchdowns in the United States. There is evidence that 1000 or more additional weak tornadoes may occur each year and go completely undocumented. The "Percentage of Tornado-Related Deaths 1950-1994" pie chart shows that while violent tornadoes are few in number, they cause a very high percentage of tornado-related deaths. The Tornado Project has analyzed data prior to 1950, and found that the percentage of deaths from violent tornadoes was even greater in the past. This is because the death tolls prior to the introduction of the forecasting/awareness programs were enormous: 695 dead(Missouri-Illinois-Indiana, March 18, 1925); 317 dead(Natchez, Mississippi, May 7, 1840);.255 dead(St. Louis, Missouri and East St. Louis, Illinois, May 27, 1896); 216 dead(Tupelo, Mississippi, April 5, 1936); 203 dead(Gainesville, GA, April 6, 1936). In more recent times, no single tornado has killed more than 50 people since 1971.
  • What Are We Doing with (Or To) the F-Scale?

    What Are We Doing with (Or To) the F-Scale?

    5.6 What Are We Doing with (or to) the F-Scale? Daniel McCarthy, Joseph Schaefer and Roger Edwards NOAA/NWS Storm Prediction Center Norman, OK 1. Introduction Dr. T. Theodore Fujita developed the F- Scale, or Fujita Scale, in 1971 to provide a way to compare mesoscale windstorms by estimating the wind speed in hurricanes or tornadoes through an evaluation of the observed damage (Fujita 1971). Fujita grouped wind damage into six categories of increasing devastation (F0 through F5). Then for each damage class, he estimated the wind speed range capable of causing the damage. When deriving the scale, Fujita cunningly bridged the speeds between the Beaufort Scale (Huler 2005) used to estimate wind speeds through hurricane intensity and the Mach scale for near sonic speed winds. Fujita developed the following equation to estimate the wind speed associated with the damage produced by a tornado: Figure 1: Fujita's plot of how the F-Scale V = 14.1(F+2)3/2 connects with the Beaufort Scale and Mach number. From Fujita’s SMRP No. 91, 1971. where V is the speed in miles per hour, and F is the F-category of the damage. This Amazingly, the University of Oklahoma equation led to the graph devised by Fujita Doppler-On-Wheels measured up to 318 in Figure 1. mph flow some tens of meters above the ground in this tornado (Burgess et. al, 2002). Fujita and his staff used this scale to map out and analyze 148 tornadoes in the Super 2. Early Applications Tornado Outbreak of 3-4 April 1974.
  • Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions Christopher D

    Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions Christopher D

    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2012 Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions Christopher D. Karstens Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Atmospheric Sciences Commons, and the Meteorology Commons Recommended Citation Karstens, Christopher D., "Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions" (2012). Graduate Theses and Dissertations. 12778. https://lib.dr.iastate.edu/etd/12778 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Observations and laboratory simulations of tornadoes in complex topographical regions by Christopher Daniel Karstens A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Meteorology Program of Study Committee: William A. Gallus, Jr., Major Professor Partha P. Sarkar Bruce D. Lee Catherine A. Finley Raymond W. Arritt Xiaoqing Wu Iowa State University Ames, Iowa 2012 Copyright © Christopher Daniel Karstens, 2012. All rights reserved. ii TABLE OF CONTENTS LIST OF FIGURES ..............................................................................................
  • Template for Electronic Journal of Severe Storms Meteorology

    Template for Electronic Journal of Severe Storms Meteorology

    Lyza, A. W., A. W. Clayton, K. R. Knupp, E. Lenning, M. T. Friedlein, R. Castro, and E. S. Bentley, 2017: Analysis of mesovortex characteristics, behavior, and interactions during the second 30 June‒1 July 2014 midwestern derecho event. Electronic J. Severe Storms Meteor., 12 (2), 1–33. Analysis of Mesovortex Characteristics, Behavior, and Interactions during the Second 30 June‒1 July 2014 Midwestern Derecho Event ANTHONY W. LYZA, ADAM W. CLAYTON, AND KEVIN R. KNUPP Department of Atmospheric Science, Severe Weather Institute—Radar and Lightning Laboratories University of Alabama in Huntsville, Huntsville, Alabama ERIC LENNING, MATTHEW T. FRIEDLEIN, AND RICHARD CASTRO NOAA/National Weather Service, Romeoville, Illinois EVAN S. BENTLEY NOAA/National Weather Service, Portland, Oregon (Submitted 19 February 2017; in final form 25 August 2017) ABSTRACT A pair of intense, derecho-producing quasi-linear convective systems (QLCSs) impacted northern Illinois and northern Indiana during the evening hours of 30 June through the predawn hours of 1 July 2014. The second QLCS trailed the first one by only 250 km and approximately 3 h, yet produced 29 confirmed tornadoes and numerous areas of nontornadic wind damage estimated to be caused by 30‒40 m s‒1 flow. Much of the damage from the second QLCS was associated with a series of 38 mesovortices, with up to 15 mesovortices ongoing simultaneously. Many complex behaviors were documented in the mesovortices, including: a binary (Fujiwhara) interaction, the splitting of a large mesovortex in two followed by prolific tornado production, cyclic mesovortexgenesis in the remains of a large mesovortex, and a satellite interaction of three small mesovortices around a larger parent mesovortex.
  • Tornadoes & Downburst

    Tornadoes & Downburst

    TORNADOES & DOWNBURST TORNADOES • A devastating F5 tornado about 200 meters wide plows through Hesston, Kansas, on March 13, 1990, leaving almost 300 people homeless and 13 injured. • Total destruction caused by an F5 tornado that devastated parts of Oklahoma on May 3, 1999. • A tornado is a violently rotating (usually counterclockwise in the northern hemisphere) column of air descending from a thunderstorm and in contact with the ground. Although tornadoes are usually brief, lasting only a few minutes, they can sometimes last for more than an hour and travel several miles causing considerable damage. In a typical year about 1000 tornadoes will strike the United States. The peak of the tornado season is April through June and more tornadoes strike the central United States than any other place in the world. This area has been nicknamed "tornado alley." Most tornadoes are spawned from supercell thunderstorms. Supercell thunderstorms are characterized by a persistent rotating updraft and form in environments of strong vertical wind shear. Wind shear is the change in wind speed and/or direction with height. • Tornadoes are natures most destructive weather hazard. Annual Number of Tornadoes per State (upper number) • Tornado incidence by state. The upper figure shows the number of tornadoes reported by each state during a 25-year period. The lower figure is the average annual number of tornadoes per 10,000 square miles. The darker the shading, the greater the frequency of tornadoes. • Average number of tornadoes during each month in the United States. Fujita Scale F0-F1 Fujita scale is a measure of tornado intensity Winds 60 - 115 mph quantified through a subjective analysis of relating tornadic damage to wind speed.
  • 1 International Approaches to Tornado Damage and Intensity Classification International Association of Wind Engineers

    1 International Approaches to Tornado Damage and Intensity Classification International Association of Wind Engineers

    International Approaches to Tornado Damage and Intensity Classification International Association of Wind Engineers (IAWE), International Tornado Working Group 2017 June 6, DRAFT FINAL REPORT 1. Introduction Tornadoes are one of the most destructive natural Hazards on Earth, with occurrences Having been observed on every continent except Antarctica. It is difficult to determine worldwide occurrences, or even the fatalities or losses due to tornadoes, because of a lack of systematic observations and widely varying approacHes. In many jurisdictions, there is not any tracking of losses from severe storms, let alone the details pertaining to tornado intensity. Table 1 provides a summary estimate of tornado occurrence by continent, with details, wHere they are available, for countries or regions Having more than a few observations per year. Because of the lack of systematic identification of tornadoes, the entries in the Table are a mix of verified tornadoes, reports of tornadoes and climatological estimates. Nevertheless, on average, there appear to be more than 1800 tornadoes per year, worldwide, with about 70% of these occurring in North America. It is estimated that Europe is the second most active continent, with more than 240 per year, and Asia third, with more than 130 tornadoes per year on average. Since these numbers are based on observations, there could be a significant number of un-reported tornadoes in regions with low population density (CHeng et al., 2013), not to mention the lack of systematic analysis and reporting, or the complexity of identifying tornadoes that may occur in tropical cyclones. Table 1 also provides information on the approximate annual fatalities, althougH these data are unavailable in many jurisdictions and could be unreliable.
  • Damage Analysis of Three Long-Track Tornadoes Using High-Resolution Satellite Imagery

    Damage Analysis of Three Long-Track Tornadoes Using High-Resolution Satellite Imagery

    atmosphere Article Damage Analysis of Three Long-Track Tornadoes Using High-Resolution Satellite Imagery Daniel Burow * , Hannah V. Herrero and Kelsey N. Ellis Department of Geography, University of Tennessee, Knoxville, 1000 Phil Fulmer Way, Knoxville, TN 37920, USA; [email protected] (H.V.H.); [email protected] (K.N.E.) * Correspondence: [email protected] Received: 2 May 2020; Accepted: 8 June 2020; Published: 10 June 2020 Abstract: Remote sensing of tornado damage can provide valuable observations for post-event surveys and reconstructions. The tornadoes of 3 March 2019 in the southeastern United States are an ideal opportunity to relate high-resolution satellite imagery of damage with estimated wind speeds from post-event surveys, as well as with the Rankine vortex tornado wind field model. Of the spectral metrics tested, the strongest correlations with survey-estimated wind speeds are found using a Normalized Difference Vegetation Index (NDVI, used as a proxy for vegetation health) difference image and a principal components analysis emphasizing differences in red and blue band reflectance. NDVI-differenced values across the width of the EF-4 Beauregard-Smiths Station, Alabama, tornado path resemble the pattern of maximum ground-relative wind speeds across the width of the Rankine vortex model. Maximum damage sampled using these techniques occurred within 130 m of the tornado vortex center. The findings presented herein establish the utility of widely accessible Sentinel imagery, which is shown to have sufficient spatial resolution to make inferences about the intensity and dynamics of violent tornadoes occurring in vegetated areas. Keywords: tornadoes; tornado damage; remote sensing; Sentinel-2; NDVI; PCA; Rankine vortex 1.
  • Tornadoes Tornadoes

    Tornadoes Tornadoes

    Tornadoes Tornadoes A tornado is a violently rotating column of air in contact with the ground Tornadogenesis is the formation of a tornado A visible condensation funnel is NOT necessary to have a tornado However, just a funnel without a circulation in contact with the ground is NOT a tornado Tornadoes may have wind speeds between 40 and 300+ m.p.h! Photo: D. Zaras, NOAA On a local scale, the tornado is the most intense of Dr. Christopher M. Godfrey all atmospheric circulations University of North Carolina at Asheville ATMS 179 – Fall 2013 ATMS 179 – Fall 2013 Tornado Alley When do tornadoes occur? Mainly during spring (April, May, June) in the U.S. Tornadoes can occur year-round! 75% of all tornadoes occur in the U.S. Note: This is the total number of tornadoes in Most tornadoes occur in tornado alley (central Texas a multi-year study. Do I know which years? No, to Nebraska) but you get the idea. Top number: # tornadoes reported by each state in a 25-year period Bottom number: Average annual # tornadoes per 10,000 square miles ATMS 179 – Fall 2013 ATMS 179 – Fall 2013 Tornado Frequency by Time of Day The Fujita Scale (Used prior to 2007) Most tornadoes occur from late afternoon through Relates tornado intensity indirectly to structural early evening (1−8 p.m.) and/or vegetative damage ATMS 179 – Fall 2013 ATMS 179 – Fall 2013 1 Tornado Damage The Enhanced Fujita Scale EF-Scale in use by the NWS starting February 1, 2007 More complex than F-Scale Photo: Jim LaDue ATMS 179 – Fall 2013 ATMS 179 – Fall 2013 Why the NWS created the EF-Scale EF-Scale Damage Indicators (DIs) Need more damage indicators The framed house is one of only 28 DIs identified initially To recalibrate winds a few FF--scalescale damage indicators.
  • Service Assessment

    Service Assessment

    Service Assessment Record Tornado Outbreaks of May 4-10, 2003 U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service Silver Spring, Maryland Cover Photographs: Left: F4 tornado near Girard, Kansas, May 4, 2003. (Chuck Robertson) Right: The tornado that moved through Girard later destroyed this home in Liberal, Missouri. The gun safe in the center of the basement was used as shelter by the residents of the home. None of the residents were injured. (Ken Harding, NOAA, NWS Aberdeen, South Dakota) Service Assessment Record Tornado Outbreaks of May 4-10, 2003 December 2003 U.S. DEPARTMENT OF COMMERCE Donald L. Evans, Secretary National Oceanic and Atmospheric Administration Vice Admiral Conrad C. Lautenbacher, Jr., Administrator National Weather Service John J. Kelly, Jr., Assistant Administrator Preface During the period May 4-10, 2003, an unprecedented number of tornadoes affected the central and southern United States. During this period, 393 tornadoes occurred across the central and southern U.S. resulting in 39 deaths across 4 states. Six of these tornadoes were classified as violent (F4) on the Fujita Tornado Intensity Scale. Due to the magnitude of this event, a service assessment team was formed to examine the warning and forecast services provided to emergency managers (EMs), government agencies and the public. Service assessments are used to improve techniques of National Weather Service products and services to the American public through the recommendations in the report. John J. Kelly,
  • On the Implementation of the Enhanced Fujita Scale in the USA

    On the Implementation of the Enhanced Fujita Scale in the USA

    Atmospheric Research 93 (2009) 554–563 Contents lists available at ScienceDirect Atmospheric Research journal homepage: www.elsevier.com/locate/atmos On the implementation of the enhanced Fujita scale in the USA Charles A. Doswell III a,⁎, Harold E. Brooks b, Nikolai Dotzek c,d a Cooperative Institute for Mesoscale Meteorological Studies, National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072, USA b NOAA/National Severe Storms Laboratory, National Weather Center, 120 David L. Boren Blvd., Norman, OK 73072, USA c Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, 82234 Wessling, Germany d European Severe Storms Laboratory (ESSL), Münchner Str. 20, 82234 Wessling, Germany article info abstract Article history: The history of tornado intensity rating in the United States of America (USA), pioneered by Received 1 December 2007 T. Fujita, is reviewed, showing that non-meteorological changes in the climatology of the Received in revised form 5 November 2008 tornado intensity ratings are likely, raising questions about the temporal (and spatial) Accepted 14 November 2008 consistency of the ratings. Although the Fujita scale (F-scale) originally was formulated as a peak wind speed scale for tornadoes, it necessarily has been implemented using damage to Keywords: estimate the wind speed. Complexities of the damage-wind speed relationship are discussed. Tornado Recently, the Fujita scale has been replaced in the USA as the official system for rating tornado F-scale intensity by the so-called Enhanced Fujita scale (EF-scale). Several features of the new rating EF-scale Intensity distribution system are reviewed and discussed in the context of a proposed set of desirable features of a tornado intensity rating system.
  • Tornado Intensity Prediction Based on Environment Elements at Tornado Events Starting Points

    Tornado Intensity Prediction Based on Environment Elements at Tornado Events Starting Points

    TORNADO INTENSITY PREDICTION BASED ON ENVIRONMENT ELEMENTS AT TORNADO EVENTS STARTING POINTS A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science by XINWEI DU Dr. Grant Scott, Thesis Supervisor May 2016 The undersigned, appointed by the Dean of the Graduate School, have examined the dissertation entitled: Tornado Intensity Prediction Based on Environment Elements At Tornado Events Starting Points Xinwei Du, a candidate for the degree of Master of Science and hereby certify that, in their opinion, it is worthy of acceptance. Dr. Grant Scott Dr. Jianlin Cheng Dr. Curt H. Davis ACKNOWLEDGMENTS I would first like to express my sincere gratitude to my advisor Dr. Grant Scott for the continuous support of my Master study and research. His guidance helped me in all the time of research and writing of this thesis. Whatever I am getting in trouble with the technique problem or can not express the idea accurately in my thesis, he is always there to provide help. And I would like to thanks the rest of my thesis committee members: Dr. Jianlin Cheng and Dr. Curt H.Davis, for their time, encouragement, and insightful com- ments. I thank my graduate program advisor: Jodie Lenser, thanks for her help during my whole study time. Last but not the least, I would like to thank my family: Jun Du and Xiaoxia Zhang for supporting me in my whole life. And my friends: Muxi Chen, Xiaohui Yang, and Rui Xie for encouragement and valuable suggestions they gave for my thesis.
  • Environments of Supercellular Satellite Tornadoes

    Environments of Supercellular Satellite Tornadoes

    P51 ENVIRONMENTS OF SUPERCELLULAR SATELLITE TORNADOES Roger Edwards1 and Andrew R. Dean Storm Prediction Center, Norman, OK 1. BACKGROUND suggested that as an area for further study. Summary updates to ST analyses from E14 will be offered in A satellite tornado (ST, Edwards 2014; hereafter section 2. In section 3, we follow up by applying an E14) is defined as a discrete tornadic vortex occurring objectively analyzed, storm-environment dataset to a under all of these conditions: filtered ST case list, and compare results with a Within a supercell (even if genesis occurs outside broader national dataset of non-ST-producing a mesocyclone), thus precluding nonsupercellular tornadic supercells spanning a similar timespan. tornadoes (e.g., Wakimoto and Wilson 1989); Resulting conclusions and discussion appear in Adjacent to a longer-lasting and/or larger, section 4. mesocyclonic, main tornado (hereafter, MT) and within the MT lifespan; 2. TORNADO DATA and ANALYSES Translating around the MT for at least part of its own lifespan, in the direction matching the MT’s E14 documented 51 STs, starting with two rotational sense—thus having a common accompanying the “Tri-State” tornado of 18 March physical, mesocyclonic influence; 1925 (Johns et al. 2013), continuing through 6 June Documented as a distinct tornado, not developing 2018. All but seven cases occurred after 1992, as part of a multiple-vortex MT circulation, based indicating the influence of storm spotting and chasing, on evidence in photographs (Fig. 1), video, and associated photographic and video material, in mobile-radar data, and/or clear description in the documentation of STs.