Use of Dual-Polarization Signatures in Diagnosing Tornadic Potential
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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. -
Bulletin of the American Meteorological Society
AMERICAN METEOROLOGICAL SOCIETY Bulletin of the American Meteorological Society EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: 10.1175/BAMS-D-15-00301.1 The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. If you would like to cite this EOR in a separate work, please use the following full citation: Zhao, K., M. Wang, M. Xue, P. Fu, Z. Yang, X. Chen, Y. Zhang, W. Lee, F. Zhang, Q. Lin, and Z. Li, 2016: Doppler radar analysis of a tornadic miniature supercell during the Landfall of Typhoon Mujigae (2015) in South China. Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-15-00301.1, in press. © 2016 American Meteorological Society Manuscript Click here to download Manuscript (non-LaTeX) ZhaoEtal2016_Tornado_R3.docx 1 Doppler radar analysis of a tornadic miniature supercell during 2 the Landfall of Typhoon Mujigae (2015) in South China 3 4 Kun Zhao*1, Mingjun Wang1 ,Ming Xue1,2, Peiling Fu1, 5 Zhonglin Yang1 , Xiaomin Chen1 and Yi Zhang1 6 1Key Lab of Mesoscale Severe Weather/Ministry of Education -
Downloaded 10/07/21 04:40 AM UTC 2484 MONTHLY WEATHER REVIEW VOLUME 146
AUGUST 2018 B L U E S T E I N E T A L . 2483 The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013 HOWARD B. BLUESTEIN School of Meteorology, University of Oklahoma, Norman, Oklahoma KYLE J. THIEM National Weather Service, Peachtree City, Georgia JEFFREY C. SNYDER NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma JANA B. HOUSER Department of Geography, Ohio University, Athens, Ohio (Manuscript received 28 February 2018, in final form 17 May 2018) ABSTRACT This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for $15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right- forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers– Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind. -
Correlation of Tornado Intensity with Dual-Polarization Radar Information
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by KnowledgeBank at OSU Correlation of Tornado Intensity with Dual-Polarization Radar Information Undergraduate Research Thesis Presented in Partial Fulfillment of the Requirements for graduation “with honors research distinction in Atmospheric Sciences” in the undergraduate colleges of The Ohio State University by John Banghoff The Ohio State University December 2015 Project Advisor: Professor Jay S. Hobgood, Department of Geography i Table of Contents Section Page List of Tables iii List of Figures iv Abstract 1 I Introduction 2 II Literature Review 4 III Methods 8 IV Results 12 V Discussion 21 VI Acknowledgements 25 VII References 25 ii List of Tables Table Page 1 Breakdown of tornadoes investigated by EF-Scale 13 2 TDS and rotational velocity criteria for tornado warning issuance 22 iii List of Figures Figure Page 1 El Reno, OK tornado debris signature on 31 May 2013 9 2 Hattiesburg, MS (2/10/13) TDS demonstrating TDS height 11 3 Minimum correlation coefficient vs. maximum rated wind speed 13 4 Maximum TDS height vs. maximum rated wind speed 15 5 Maximum TDS diameter vs. tornado diameter 17 6 Moore, OK tornado on 20 May 2013 qualitative analysis 19 7 Time tendencies of TDS height and rotational velocity 20 iv ABSTRACT Analysis of National Weather Service radar data associated with tornadoes documented by the Storm Prediction Center through May 2014 indicates that 332 of all tornadoes and 126 significant tornadoes exhibited a polarimetric tornado debris signature (TDS). This study documented confirmed TDS events associated with significant tornadoes (EF2 or greater) from May 2010 through May 2014 and characterized multiple characteristics throughout the life cycle of each tornado. -
The Pennsylvania State University the Graduate School USING DUAL-POLARIZATION RADAR INFORMATION to INVESTIGATE CLEAR-AIR BOUNDAR
The Pennsylvania State University The Graduate School USING DUAL-POLARIZATION RADAR INFORMATION TO INVESTIGATE CLEAR-AIR BOUNDARY LAYER ATMOSPHERIC PHENOMENA A Thesis in Meteorology by John R. Banghoff c 2019 John R. Banghoff Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science May 2019 The thesis of John R. Banghoff was reviewed and approved∗ by the following: David J. Stensrud Professor of Meteorology Head of the Department of Meteorology Thesis Co-Advisor Matthew R. Kumjian Assistant Professor of Meteorology Thesis Co-Advisor George S. Young Professor of Meteorology Paul M. Markowski Professor of Meteorology Associate Head of Graduate Program ∗Signatures are on file in the Graduate School. Abstract Dual-polarization radar provides a wealth of new information about the type, size, and orientation of scat- terers in the atmosphere. This radar information has been interrogated for its applications to hazardous weather, but a wealth of clear-air radar data exists that is significantly underutilized. The ability of Na- tional Weather Service WSR-88D radars to detect insects and other biota within the convective boundary layer (CBL) facilitates estimation of boundary layer depth and characterization of horizontal convective rolls (HCRs). Bragg scatter signatures in dual-polarization radar observations, which are defined by low differen- tial reflectivity (ZDR) values, are used as a proxy for CBL depth in 2014 over Central Oklahoma using data from the Twin Lakes (KTLX) WSR-88D. The 243 ZDR Bragg scatter and upper air sounding CBL depth estimates collected during this year have a correlation of 0.90 and a RMSE of 254 m. -
Potential Tornado Warning Improvement Resulting from Utilization of the TDS in the Warning Decision Process
M. S. Van Den Broeke, 2017: Potential tornado warning improvement resulting from utilization of the TDS in the warning decision process. J. Operational Meteor., 5 (10), 121-133, doi: https://doi.org/10.15191/nwajom.2017.0510 Potential Tornado Warning Improvement Resulting from Utilization of the TDS in the Warning Decision Process MATTHEW S. VAN DEN BROEKE University of Nebraska-Lincoln, Lincoln, NE (Manuscript received 9 September 2016; review completed 15 May 2017) ABSTRACT Polarimetric tornadic debris signatures (TDSs) provide a means of confirming that a strong wind field has lofted debris to the altitude of the radar beam. They can increase confidence in an ongoing tornado, which may be noted in tornado warning text to increase the sense of urgency. They may also serve as indicators of weak tornadoes that may otherwise go unwarned. Polarimetric data have been available since 2012 on a large portion of the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network and since May 2013 for the entire network. It is anticipated that use of the polarimetric TDS in the tornado warning process should evolve through time. Thus, this paper presents an overview of how the TDS was used in the warning process during the 16 months when widespread polarimetric data were first becoming available (February 2012–May 2013). During this period, 22.5% of tornado warning texts for TDS-producing events mentioned the TDS. It is estimated that utilization of the TDS in the warning decision process could result in ~45 previously unwarned tornadoes being warned annually, and in ~65 tornado warnings being issued with a less negative lead time. -
Correlation of Tornado Intensity with Dual-Polarization Radar Information
Correlation of Tornado Intensity with Dual-Polarization Radar Information Undergraduate Research Thesis Presented in Partial Fulfillment of the Requirements for graduation “with honors research distinction in Atmospheric Sciences” in the undergraduate colleges of The Ohio State University by John Banghoff The Ohio State University December 2015 Project Advisor: Professor Jay S. Hobgood, Department of Geography i Table of Contents Section Page List of Tables iii List of Figures iv Abstract 1 I Introduction 2 II Literature Review 4 III Methods 8 IV Results 12 V Discussion 21 VI Acknowledgements 25 VII References 25 ii List of Tables Table Page 1 Breakdown of tornadoes investigated by EF-Scale 13 2 TDS and rotational velocity criteria for tornado warning issuance 22 iii List of Figures Figure Page 1 El Reno, OK tornado debris signature on 31 May 2013 9 2 Hattiesburg, MS (2/10/13) TDS demonstrating TDS height 11 3 Minimum correlation coefficient vs. maximum rated wind speed 13 4 Maximum TDS height vs. maximum rated wind speed 15 5 Maximum TDS diameter vs. tornado diameter 17 6 Moore, OK tornado on 20 May 2013 qualitative analysis 19 7 Time tendencies of TDS height and rotational velocity 20 iv ABSTRACT Analysis of National Weather Service radar data associated with tornadoes documented by the Storm Prediction Center through May 2014 indicates that 332 of all tornadoes and 126 significant tornadoes exhibited a polarimetric tornado debris signature (TDS). This study documented confirmed TDS events associated with significant tornadoes (EF2 or greater) from May 2010 through May 2014 and characterized multiple characteristics throughout the life cycle of each tornado. -
7B.4. Supercell Polarimetric Signatures at X-Band: Data from Vortex2
7B.4. SUPERCELL POLARIMETRIC SIGNATURES AT X-BAND: DATA FROM VORTEX2 Christopher M. Schwarz* and Donald W. Burgess Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), The University of Oklahoma, Norman, OK 1. INTRODUCTION km)). The radar provided dual-pol observations and radial Most dual-polarimetric (dual-pol) studies have been velocities for dual-Doppler in association with other performed at S- and C-bands (e.g., Kumjian and mesocyclone-scale radars (two Doppler on Wheels: Ryzhkov 2008, Romine et al. 2008, Ryzhkov et al. (DOW6 and DOW7) and UMass X-Pol (UMXP)). Table 1 2005a, Ryzhkov et al. 2002, Van Den Broeke 2008), but details NOXP specs for 2009 and 2010. The advantage relatively few studies have been made at X-band. of this mobile radar is high spatial resolution and fast Certain signatures have been found to exist in supercell updates. During all three days analyzed in this paper, thunderstorms. These signatures include the tornado NOXP performed two-minute time syncs. The scanning debris signature (TDS; Ryzhkov et al. 2002, 2005a), the strategies are listed in Table 1. low-level ZDR arc (e.g., Kumjian and Ryzhkov 2008, 2009; Snyder 2008), ZDR and KDP columns (e.g., Caylor Name NOXP and Illingworth 1987; Hubbert et al. 1998; Loney et al. Type X-Band (!"3.21 cm) 2002; Kumjian and Ryzhkov 2008), midlevel ZDR and Frequency 9410 MHz 3-dB Beamwidth !HV rings (Kumjian and Ryzhkov 2008; Kumjian 2008), 1.0° updraft signature (Kumjian and Ryzhkov 2008), and hail Effective Beamwidth ~1.45° signatures (Heinselman and Ryzhkov 2006). Each Azimuthal Sampling 0.5° signature reveals a distinguishing distribution of Rate -1 hydrometeors. -
Acronyms and Abbreviations
ACRONYMS AND ABBREVIATIONS AGL - Above Ground Level AP - Anomalous Propagation ARL -Above Radar Level AVSET - Automated Volume Scan Evaluation and Termination AWIPS - Advanced Weather Interactive Processing System BE – Book End as in Book End Vortex BWER - Bounded Weak Echo Region CAPE - Conditional Available Potential Energy CIN – Convective Inhibition CC – Correlation Coefficient Dual Pol Product CR -Composite Reflectivity Product CWA -County Warning Area DP – Dual Pol dBZ - Radar Reflectivity Factor DCAPE - Downdraft Conditional Available Potential Energy DCZ - Deep Convergence Zone EL – Equilibrium Level ET -Echo Tops Product ETC - Extratropical Cyclone FAR - False Alarm Ratio FFD – Forward Flank Downdraft FFG – Flash Flood Guidance GTG – Gate-To-Gate HP - High Precipitation supercell (storm) HDA -Hail Detection Algorithm LCL -Lifting Condensation Level LEWP -Line Echo Wave Pattern LP - Low Precipitation Supercell (storm) MARC - Mid Altitude Radial Convergence MCC - Mesoscale Convective Complex MCS - Mesoscale Convective System MCV – Mesoscale Convective Vortex MESO -Mesocyclone MEHS -Maximum Expected Hail Size OHP -One-Hour Rainfall Accumulation Product POSH -Probability of Severe Hail QLCS - Quasi Linear Convective Systems R - Rainfall Rate in Z-R relationship RF – Range Folding RFD - Rear Flank Downdraft RIJ - Rear Inflow Jet RIN – Rear Inflow Notch SAILS - Supplemental Adaptive Intra-Volume Low-Level Scan SCP – Supercell Composite Parameter SRH – Storm Relative Helicity SRM -Storm Relative Mean Radial Velocity STP –Significant -
Some Considerations for the Use of High-Resolution Mobile Radar Data in Tornado Intensity Determination
AUGUST 2014 S N Y D E R A N D B L U E S T E I N 799 Some Considerations for the Use of High-Resolution Mobile Radar Data in Tornado Intensity Determination JEFFREY C. SNYDER National Severe Storms Laboratory, Norman, Oklahoma HOWARD B. BLUESTEIN School of Meteorology, University of Oklahoma, Norman, Oklahoma (Manuscript received 26 February 2014, in final form 11 April 2014) ABSTRACT The increasing number of mobile Doppler radars used in field campaigns across the central United States has led to an increasing number of high-resolution radar datasets of strong tornadoes. There are more than a few instances in which the radar-measured radial velocities substantially exceed the estimated wind speeds associated with the enhanced Fujita (EF) scale rating assigned to a particular tornado. It is imperative, however, to understand what the radar data represent if one wants to compare radar observations to damage- based EF-scale estimates. A violent tornado observed by the rapid-scan, X-band, polarimetric mobile radar 2 (RaXPol) on 31 May 2013 contained radar-relative radial velocities exceeding 135 m s 1 in rural areas es- sentially devoid of structures from which damage ratings can be made. This case, along with others, serves as an excellent example of some of the complications that arise when comparing radar-estimated velocities with the criteria established in the EF scale. In addition, it is shown that data from polarimetric radars should reduce the variance of radar-relative radial velocity estimates within the debris field compared to data from single-polarization radars. -
User Guide Last Updated: October 19Th, 2017
User Guide Last Updated: October 19th, 2017 Table of Contents: Page 1: Overview of Map Controls A review of the controls that are displayed on the map Page 2: Side Menu Overview A review of the various options contained within the side menu Page 3-8: Radar Products Overview A review of all the radar products Storm Mapping provides and how to use them ***NOTE- THIS USER GUIDE WILL CONTINUE TO GROW, EXPECT MORE INFORMATION TO BE ADDED ON A CONSTANT BASIS**** If you have any questions that this guide does not provide, please feel free to email us at [email protected] Getting Started The controls between the desktop and mobile versions of Storm Mapping are the same. This user guide will show the controls of Storm Mapping using both the mobile and desktop versions. Map Controls Overview 1 Side Menu Overview 2 Radar Products Overview WSR-88D Radar Tower Status: WSR-88D Radar Tower Radar Tower Radar Tower Radar Tower Offline Selected & On Off for maintenance Terminal Doppler Weather Radar Status: Terminal Doppler Weather Radar Radar Tower Radar Tower Radar Tower Offline Selected & On Off for maintenance What’s the difference between a WSR-88D and a TDWR? A WSR-88D (Weather Surveillance Radar, 1988, Doppler) is the National Weather Service’s radar. There are about 158 currently in the U.S. This type of radar is also called NEXRAD (Next Generation Radar), because it is the first radar to be able to detect and measure both precipitation and velocity. This allows NWS and other meteorologists to detect things like tornadoes just by looking at the radar imagery. -
Comprehensive Emergency Management Plan
St. Johns County Florida Comprehensive Emergency Management Plan 2021 St. Johns County Comprehensive Emergency Management Plan 2021 CEMP DISTRIBUTION LIST Copy Number Organization Distributed CEMP 1 St. Johns County Division of Emergency Management (Master) 2 St. Johns County Administration 3 St. Johns County Board of County Commissioners 4 St. Johns County Clerk of the Court 5 St. Johns County Development Services 6 St. Johns County Fire / Rescue 7 St. Johns County Planning Department 8 St. Johns County School District 9 St. Johns County Sheriff’s Office 10 St. Johns County Supervisor of Elections 11 St. Johns County Tax Collectors Office 12 City of St. Augustine 13 City of St. Augustine Beach 14 Chamber of Commerce 15 St. Augustine Airport Authority 16 Amateur Radio Group 17 Clay County Emergency Management 18 Duval County Emergency Management 19 Flagler County Emergency Management 20 Putnam County Emergency Management 21 Florida Division of Emergency Management 22 Northeast Florida Regional Council St. Johns County Comprehensive Emergency Management Plan 2021 TABLE OF CONTENTS INTRO PAGES Plan Development and Maintenance ............................................................................................... 1 Resolution and Promulgation ........................................................................................................... 4 Signed Concurrence ........................................................................................................................ 6 Forward ...........................................................................................................................................