DOCSLIB.ORG

Tornado Debris Data April 2014

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Tornado Debris Data April 2014 Tornado Debris Data April 2014 WSI Corporation has recently developed a Tornado Debris Signature (TDS) algorithm that automatically detects the existence of debris lofted by tornadoes in real-time. The goal of this algorithm is to enable improved decision-making for deployment of insurance CAT team resources. The success of the TDS algorithm is dependent upon the incorporation of data from the new dual- polarization (dual-pol) capability of the National Weather Service (NWS) radar network. These dual- pol data complement the more traditional radar-based Tornado Vortex Signature (TVS) detections to provide a better assessment of whether debris is lofted from a tornado. About Dual-Polarization Radar Before the advent of dual-polarization (dual-pol) capabilities in recent years, the Weather Surveillance Radar – 1988 Doppler (WSR-88D) operated by the NWS was a non-polarimetric radar that only transmitted horizontally-polarized electromagnetic waves. This limited the measurement of refl ected energy to only the horizontal direction. Dual-pol radar sends simultaneous electromagnetic waves in both the horizontal and vertical directions. This enables the refl ected energy to have not only a horizontal measurement but also a vertical measurement. This two-dimensional representation of returning wave energy offers more detailed insight into the types and characteristics of the particles in the atmosphere (NSSL website). Example of conventional versus dual-pol radar. Image courtesy of NOAA. Tornado Debris Signature About Dual-Polarization Radar Continued... With the completion of the dual-pol WSR-88D upgrade, each of the radars now produce 14 new products that help to not only differentiate precipitation types and intensities but also non-meteorological particles. Some of the new radar products include Differential Refl ectivity (Zdr), Correlation Coeffi cient (CC), Specifi c Differential Phase (Kdp) and Hydrometeor Classifi cation Product (HC). Of these products, the CC is most important to the TDS algorithm because it refl ects the similarity between horizontal and vertical returns within a radar bin (Istok et al 2009). Since tornado debris is typically less uniform in shape than meteorological targets, CC can be a clue that a tornado is on the ground, provided a vortex signature is simultaneously present. Example of low correlation coeffi cient as a result of lofted debris from May 19, 2013 Shawnee, OK tornado. Image courtesy of NWS Tulsa. Tornado Debris Signature Characteristics of Tornado-Producing Storms Tornado-producing storms have several identifi able characteristics in radar data. None of these characteristics alone can demonstrate a tornado is present. When more than one is present, we can be more confi dent that a tornado is on the ground, lofting debris into the air. A weak echo region (WER) is an area of markedly lower refl ectivity, resulting from an increase in updraft strength (Glickman, 2000) that can develop during the growth phase of a tornadic thunderstorm. A WER may also be surrounded on the top and sides by an area of higher refl ectivity (Glockman, 2000), referred to as a bounded weak echo region (BWER). The BWER is suggestive of a strong updraft that is preventing precipitation from reaching the ground, generally associated with a mesocyclone that is capable of producing a tornado. Couplets in the velocity fi elds are another indication that a tornado is either occurring or imminent. A couplet occurs when signifi cant velocities towards and away from the radar are found in close proximity to one another. A couplet detected at the lowest elevation angles of the radar volume is evidence of rotation close to the ground. These couplets seen in the radar velocity data are the primary triggers for a TVS. A hook echo is also a good indication that a tornado is either occurring or imminent. A hook echo is caused by the precipitation being pulled into the mesocyclone (Glickman, 2000). This typically shallow hook-shaped area of refl ectivity is often associated with a tornado. Example of a hook echo (left) and velocity couplet (right) from May 20, 2013 Avant, OK tornado. Image courtesy of NWS Tulsa. A debris ball is an area of extremely high refl ectivity that is generally located near a hook echo. Large (compared to precipitation), irregularly-shaped non-meteorological debris is often lofted into the atmosphere during a tornado (Bodine et al, 2013). The characteristics of this debris ball are the basis for using the correlation coeffi cient in the calculation of the TDS. Tornado Debris Signature Raw Calculations The calculations for the TDS are based on the correlation coefficient and the corresponding reflectivity value at or near the location of a TVS. To have a TDS, a sufficiently low CC, sufficiently high reflectivity and a location within 130 km of a radar site are needed. TDS values can range from 0-10, but there are only a subset of discrete values that are produced in the current version of the algorithm (below). TDS Value Criteria 0.0 No data or location beyond range 1.0 Reflectivity of area below threshold 2.0 Reflectivity is above threshold, but no CC data available for the area 3.0 Reflectivity is above threshold, but CC is above threshold 7.0 Reflectivity is above threshold, but CC is near but slightly above threshold 10.0 Reflectivity is above threshold, CC is below threshold. Tornado debris likely A TDS detection is only triggered for a value of 10; lower non-zero values represent “failure modes”, or those TVS detections that do not have TDS detections associated with them. Tornado Debris Signature Examples A few recent examples of TDS detections are now described in more detail. Moore, OK On May 20, 2013, an EF5 tornado with estimated winds of 200-210 mph devastated the community of Moore, OK. The tornado developed at 2:45 PM CST, 4.4 miles west of Newcastle, OK. The tornado produced EF0 and EF1 damage for the fi rst 10 minutes. The fi rst TDS was detected at 2:55 PM CDT (below). During the 50 minutes the tornado was on the ground, it traveled 17 miles and had a maximum width of 1.3 miles. It destroyed homes and businesses in Moore and the surrounding communities (from NWS Norman). The tornado caused 24 fatalities. The evolution of the TDS during the event is shown below. The fi rst tornado debris signature is detected near Newcastle, OK at 2:55 CDT on 5/20/13. A tornado debris signature continues to be indicated near the end of the hook echo on the south side of Moore, OK at 3:15 CDT on 5/20/13. The tornado debris signature passes Moore, OK at 3:25 CDT on 5/20/13. Tornado Debris Signature Examples Continued... Cleburne, TX On May 15, 2013, an EF3 tornado with estimated winds of 140 mph hit the town of Cleburne, TX. The tornado developed in the late evening, 9 miles south-southwest of Cleburne. The tornado produced pockets of EF3 damage during the 8.5 miles it traveled, and had a maximum width of 1 mile. It caused signifi cant damage to dozens of homes in Cleburne (from NWS Dallas/Fort Worth). An image of the TDS during the event is shown below. The tornado debris signature near Cleburne, OK at 8:49 PM CDT on 5/15/13. Tornado Debris Signature Performance The TDS has shown to be extremely effective at detecting tornadoes on the ground. During a test in late winter and spring 2011-12, 18 tornadoes were confirmed by spotters within 130 km (81 miles) of WSR-88D radars that had completed the dual-pol upgrade. In all 18 cases, a TDS was detected during part of the lifespan of the tornado. The tornadoes ranged in strength from EF0 to EF4. During the test period, TDS signatures occurred in 47 WSR-88D volume scans. Confirmed tornadoes correlated with 45 (96%) of those volume scans with detections. The strength breakdown of the The breakdown of TDS detection 18 tornadoes is as follows: is as follows: EF0 3 # of TDS 47 EF1 2 # of TDS with a Tornado 45 EF2 5 # of False TDS 2 EF3 7 EF4 1 Shortcomings The data above clearly show that the TDS algorithm is quite useful in the detection of damaging tornadoes; however, there are a few shortcomings. The most obvious is that the storm needs to be sufficiently resolved by a WSR-88D dual-pol radar. Storms that are far from a radar site may not be well resolved due to the curvature of the Earth. Thus, the radar beam will intercept the storm at a higher altitude, making it more likely that the tornadic debris is underneath the beam. For this reason, the algorithm caps any potential debris signatures to within 130 km (or 81 miles) of the radar location. The second shortcoming is that a TDS relies on the detection of a TVS. While many tornadoes, especially strong, long-lasting ones, coincide with a TVS, weaker tornadoes have a lower tendency to trigger TVS. Finally, the TDS will not detect all tornadoes near the radar since many do not loft enough debris to trigger the algorithm, especially those tornadoes over open grasslands or other areas without significant population. Summary The Tornado Debris Signature algorithm represents an evolutionary step in real-time tornado detection. While there are the typical limitations that radar imposes -- such as the tornado’s distance from the radar and how high the debris is lofted -- it aids those who need to make quick resource allocation decisions with greater confidence than ever before. When users receive notification of a tornado debris signature, it is likely damaging weather is occurring close to or over the specified location and that action is more likely to be required.
Load more

Recommended publications
  • 23 Public Reaction to Impact Based Warnings During an Extreme Hail Event in Abilene, Texas
    23 PUBLIC REACTION TO IMPACT BASED WARNINGS DURING AN EXTREME HAIL EVENT IN ABILENE, TEXAS Mike Johnson, Joel Dunn, Hector Guerrero and Dr. Steve Lyons NOAA, National Weather Service, San Angelo, TX Dr. Laura Myers University of Alabama, Tuscaloosa, AL Dr. Vankita Brown NOAA/National Weather Service Headquarters, Silver Spring, MD 1. INTRODUCTION 3. HOW THE PUBLIC REACTED TO THE WARNINGS A powerful, supercell thunderstorm with hail up to the Of the 324 respondents, 86% were impacted by the size of softballs (>10 cm in diameter) and damaging extreme hail event. Listed below are highlighted winds impacted Abilene, Texas, during the Children's responses to some survey questions. Art and Literacy Festival and parade on June 12, 2014. It caused several minor injuries. This storm produced 3.1 Survey question # 3 widespread damage to vehicles, homes, and businesses, costing an estimated $400 million. More People rely on various sources of information when than 200 city vehicles sustained significant damage and making a decision to prepare for hazardous weather Abilene Fire Station #4 was rendered uninhabitable. events. Please indicate the sources that influenced your Giant hail of this magnitude is a rare phenomenon decisions on how to prepare BEFORE this severe (Blair, et al., 2011), but is responsible for a thunderstorm event occurred. disproportionate amount of damage. 1) Local television In support of a larger National Weather Service 2) Websites/social media (NWS) effort, the San Angelo Texas Weather Forecast 3) Wireless alerts/cell phones
    [Show full text]
  • From Improving Tornado Warnings: from Observation to Forecast
    Improving Tornado Warnings: from Observation to Forecast John T. Snow Regents’ Professor of Meteorology Dean Emeritus, College of Atmospheric and Geographic Sciences, The University of Oklahoma Major contributions from: Dr. Russel Schneider –NOAA Storm Prediction Center Dr. David Stensrud – NOAA National Severe Storms Laboratory Dr. Ming Xue –Center for Analysis and Prediction of Storms, University of Oklahoma Dr. Lou Wicker –NOAA National Severe Storms Laboratory Hazards Caucus Alliance Briefing Tornadoes: Understanding how they develop and providing early warning 10:30 am – 11:30 am, Wednesday, 21 July 2010 Senate Capitol Visitors Center 212 Each Year: ~1,500 tornadoes touch down in the United States, causing over 80 deaths, 100s of injuries, and an estimated $1.1 billion in damages Statistics from NOAA Storm Prediction Center Supercell –A long‐lived rotating thunderstorm the primary type of thunderstorm producing strong and violent tornadoes Present Warning System: Warn on Detection • A Warning is the culmination of information developed and distributed over the preceding days sequence of day‐by‐day forecasts identifies an area of high threat •On the day, storm spotters deployed; radars monitor formation, growth of thunderstorms • Appearance of distinct cloud or radar echo features tornado has formed or is about to do so Warning is generated, distributed Present Warning System: Warn on Detection Radar at 2100 CST Radar at 2130 CST with Warning Thunderstorms are monitored using radar A warning is issued based on the detected and
    [Show full text]
  • Evaluating Operational and Newly Developed Mesocyclone and Tornado Detection Algorithms for Quasi-Linear Convective Systems Thomas James Turnage
    Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2007 Evaluating Operational and Newly Developed Mesocyclone and Tornado Detection Algorithms for Quasi-Linear Convective Systems Thomas James Turnage Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES EVALUATING OPERATIONAL AND NEWLY DEVELOPED MESOCYCLONE AND TORNADO DETECTION ALGORITHMS FOR QUASI-LINEAR CONVECTIVE SYSTEMS By THOMAS JAMES TURNAGE A thesis submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Summer Semester, 2007 The members of the Committee approve the thesis of Thomas J. Turnage defended on April 5th, 2007. ____________________________________ Henry E. Fuelberg Professor Directing Thesis ____________________________________ Jon E. Ahlquist Committee Member ____________________________________ Paul H. Ruscher Committee Member ____________________________________ Andrew I. Watson Committee Member The Office of Graduate Studies has verified and approved the above named committee members. ii ACKNOWLEDGEMENTS I first want to thank the Lord. Without His blessings, none of this would have been possible. Next, I want to express my deepest gratitude and appreciation to my major professor Dr. Henry Fuelberg for working with me to complete this thesis in spite of rotating shift work at the National Weather Service (NWS) in Tallahassee and a subsequent move to Michigan. His high standards of integrity and academic excellence have been an inspiration to me. I also wish to thank the members of my thesis committee, Mr. Irv Watson of the NWS in Tallahassee, and Drs. Jon Ahlquist and Paul Ruscher for their advice and support.
    [Show full text]
  • Hurricane Response Annex Overview Introduction
    TABLE OF CONTENTS SECTION I – HURICANE RESPONSE ANNEX OVERVIEW ............................................................... 2 INTRODUCTION .................................................................................................................................................. 3 PLANNING ASSUMPTIONS ................................................................................................................................... 4 COMMUNITY IMPACTS ........................................................................................................................................ 4 SECTION II – CONCEPT OF OPERATIONS ........................................................................................ 5 DEFINING THE HAZARD ...................................................................................................................................... 5 Tropical Cyclones ..................................................................................................................................................... 5 TIMELINES ........................................................................................................................................................... 6 HURRICANE SEASON ...................................................................................................................................... 6 RESPONDER REENTRY HOUR ......................................................................................................................... 7 SECTION III – ROLES & RESPONSIBILITIES
    [Show full text]
  • Glossary Compiled with Use of Collier, C
    Glossary Compiled with use of Collier, C. (Ed.): Applications of Weather Radar Systems, 2nd Ed., John Wiley, Chichester 1996 Rinehard, R.E.: Radar of Meteorologists, 3rd Ed. Rinehart Publishing, Grand Forks, ND 1997 DoC/NOAA: Fed. Met. Handbook No. 11, Doppler Radar Meteorological Observations, Part A-D, DoC, Washington D.C. 1990-1992 ACU Antenna Control Unit. AID converter ADC. Analog-to-digitl;tl converter. The electronic device which converts the radar receiver analog (voltage) signal into a number (or count or quanta). ADAS ARPS Data Analysis System, where ARPS is Advanced Regional Prediction System. Aliasing The process by which frequencies too high to be analyzed with the given sampling interval appear at a frequency less than the Nyquist frequency. Analog Class of devices in which the output varies continuously as a function of the input. Analysis field Best estimate of the state of the atmosphere at a given time, used as the initial conditions for integrating an NWP model forward in time. Anomalous propagation AP. Anaprop, nonstandard atmospheric temperature or moisture gradients will cause all or part of the radar beam to propagate along a nonnormal path. If the beam is refracted downward (superrefraction) sufficiently, it will illuminate the ground and return signals to the radar from distances further than is normally associated with ground targets. 282 Glossary Antenna A transducer between electromagnetic waves radiated through space and electromagnetic waves contained by a transmission line. Antenna gain The measure of effectiveness of a directional antenna as compared to an isotropic radiator, maximum value is called antenna gain by convention.
    [Show full text]
  • Downloaded 09/30/21 06:43 PM UTC JUNE 1996 MONTEVERDI and JOHNSON 247
    246 WEATHER AND FORECASTING VOLUME 11 A Supercell Thunderstorm with Hook Echo in the San Joaquin Valley, California JOHN P. MONTEVERDI Department of Geosciences, San Francisco State University, San Francisco, California STEVE JOHNSON Association of Central California Weather Observers, Fresno, California (Manuscript received 30 January 1995, in ®nal form 9 February 1996) ABSTRACT This study documents a damaging supercell thunderstorm that occurred in California's San Joaquin Valley on 5 March 1994. The storm formed in a ``cold sector'' environment similar to that documented for several other recent Sacramento Valley severe thunderstorm events. Analyses of hourly subsynoptic surface and radar data suggested that two thunderstorms with divergent paths developed from an initial echo that had formed just east of the San Francisco Bay region. The southern storm became severe as it ingested warmer, moister boundary layer air in the south-central San Joaquin Valley. A well-developed hook echo with a 63-dBZ core was observed by a privately owned 5-cm radar as the storm passed through the Fresno area. Buoyancy parameters and ho- dograph characteristics were obtained both for estimated conditions for Fresno [on the basis of a modi®ed morning Oakland (OAK) sounding] and for the actual storm environment (on the basis of a radiosonde launched from Lemoore Naval Air Station at about the time of the storm's passage through the Fresno area). Both the estimated and actual hodographs essentially were straight and suggested storm splitting. Although the actual CAPE was similar to that which was estimated, the observed magnitude of the low-level shear was considerably greater than the estimate.
    [Show full text]
  • Marine Litter Legislation: a Toolkit for Policymakers
    Marine Litter Legislation: A Toolkit for Policymakers The views expressed in this publication are those of the authors and do not necessarily reflect the views of the United Nations Environment Programme. No use of this publication may be made for resale or any other commercial purpose whatsoever without prior permission in writing from the United Nations Environment Programme. Applications for such permission, with a statement of the purpose and extent of the reproduction, should be addressed to the Director, DCPI, UNEP, P.O. Box 30552, Nairobi, Kenya. Acknowledgments This report was developed by the Environmental Law Institute (ELI) for the United Nations Environment Programme (UNEP). It was researched, drafted, and produced by Carl Bruch, Kathryn Mengerink, Elana Harrison, Davonne Flanagan, Isabel Carey, Thomas Casey, Meggan Davis, Elizabeth Hessami, Joyce Lombardi, Norka Michel- en, Colin Parts, Lucas Rhodes, Nikita West, and Sofia Yazykova. Within UNEP, Heidi Savelli, Arnold Kreilhuber, and Petter Malvik oversaw the development of the report. The authors express their appreciation to the peer reviewers, including Catherine Ayres, Patricia Beneke, Angela Howe, Ileana Lopez, Lara Ognibene, David Vander Zwaag, and Judith Wehrli. Cover photo: Plastics floating in the ocean The views expressed in this report do not necessarily reflect those of the United Nations Environment Programme. © 2016. United Nations Environment Programme. Marine Litter Legislation: A Toolkit for Policymakers Contents Foreword ..................................................................................................
    [Show full text]
  • 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.
    [Show full text]
  • ESSENTIALS of METEOROLOGY (7Th Ed.) GLOSSARY
    ESSENTIALS OF METEOROLOGY (7th ed.) GLOSSARY Chapter 1 Aerosols Tiny suspended solid particles (dust, smoke, etc.) or liquid droplets that enter the atmosphere from either natural or human (anthropogenic) sources, such as the burning of fossil fuels. Sulfur-containing fossil fuels, such as coal, produce sulfate aerosols. Air density The ratio of the mass of a substance to the volume occupied by it. Air density is usually expressed as g/cm3 or kg/m3. Also See Density. Air pressure The pressure exerted by the mass of air above a given point, usually expressed in millibars (mb), inches of (atmospheric mercury (Hg) or in hectopascals (hPa). pressure) Atmosphere The envelope of gases that surround a planet and are held to it by the planet's gravitational attraction. The earth's atmosphere is mainly nitrogen and oxygen. Carbon dioxide (CO2) A colorless, odorless gas whose concentration is about 0.039 percent (390 ppm) in a volume of air near sea level. It is a selective absorber of infrared radiation and, consequently, it is important in the earth's atmospheric greenhouse effect. Solid CO2 is called dry ice. Climate The accumulation of daily and seasonal weather events over a long period of time. Front The transition zone between two distinct air masses. Hurricane A tropical cyclone having winds in excess of 64 knots (74 mi/hr). Ionosphere An electrified region of the upper atmosphere where fairly large concentrations of ions and free electrons exist. Lapse rate The rate at which an atmospheric variable (usually temperature) decreases with height. (See Environmental lapse rate.) Mesosphere The atmospheric layer between the stratosphere and the thermosphere.
    [Show full text]
  • 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
    [Show full text]
  • 2.3 an Analysis of Lightning Holes in a Dfw Supercell Storm Using Total Lightning and Radar Information
    2.3 AN ANALYSIS OF LIGHTNING HOLES IN A DFW SUPERCELL STORM USING TOTAL LIGHTNING AND RADAR INFORMATION Martin J. Murphy* and Nicholas W.S. Demetriades Vaisala Inc., Tucson, Arizona 1. INTRODUCTION simple source density-rate (e.g., number of sources/km2/min) with various grid sizes and A “lightning hole” is a small region within a temporal resolutions. An additional data storm that is relatively free of in-cloud lightning representation method may be obtained by first activity. This feature was first discovered in aggregating the sources according to which severe supercell storms in Oklahoma during branch and flash produced them and then MEaPRS in 1998 (MacGorman et al. 2002). The looking at the number of grid cells “touched” by feature was identified through high-resolution the flash. This concept, which we refer to as three-dimensional observations of total (in-cloud “flash extent density” (see also Lojou and and cloud-to-ground (CG)) lightning activity by Cummins, this conference), is illustrated in the Lightning Mapping Array (LMA; Rison et al. Figure 1. The figure shows the branches of a 1999). It is generally thought that lightning holes flash. At each branch division, as well as at are to lightning information what the bounded each bend or kink in any single branch, a VHF weak echo region (BWER) is to radar data. That source was observed. Rather than counting all is, lightning holes are affiliated with the of the sources within each grid square to extremely strong updrafts unique to severe produce source density, we only count each storms.
    [Show full text]
  • Waste Transfer Stations: a Manual for Decision-Making Acknowledgments
    Waste Transfer Stations: A Manual for Decision-Making Acknowledgments he Office of Solid Waste (OSW) would like to acknowledge and thank the members of the Solid Waste Association of North America Focus Group and the National Environmental Justice Advisory Council Waste Transfer Station Working Group for reviewing and providing comments on this draft document. We would also like to thank Keith Gordon of Weaver Boos & Gordon, Inc., for providing a technical Treview and donating several of the photographs included in this document. Acknowledgements i Contents Acknowledgments. i Introduction . 1 What Are Waste Transfer Stations?. 1 Why Are Waste Transfer Stations Needed?. 2 Why Use Waste Transfer Stations? . 3 Is a Transfer Station Right for Your Community? . 4 Planning and Siting a Transfer Station. 7 Types of Waste Accepted . 7 Unacceptable Wastes . 7 Public Versus Commercial Use . 8 Determining Transfer Station Size and Capacity . 8 Number and Sizing of Transfer Stations . 10 Future Expansion . 11 Site Selection . 11 Environmental Justice Considerations . 11 The Siting Process and Public Involvement . 11 Siting Criteria. 14 Exclusionary Siting Criteria . 14 Technical Siting Criteria. 15 Developing Community-Specific Criteria . 17 Applying the Committee’s Criteria . 18 Host Community Agreements. 18 Transfer Station Design and Operation . 21 Transfer Station Design . 21 How Will the Transfer Station Be Used? . 21 Site Design Plan . 21 Main Transfer Area Design. 22 Types of Vehicles That Use a Transfer Station . 23 Transfer Technology . 25 Transfer Station Operations. 27 Operations and Maintenance Plans. 27 Facility Operating Hours . 32 Interacting With the Public . 33 Waste Screening . 33 Emergency Situations . 34 Recordkeeping. 35 Environmental Issues.
    [Show full text]