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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 -
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. -
Mt417 – Week 10
Mt417 – Week 10 Use of radar for severe weather forecasting Single Cell Storms (pulse severe) • Because the severe weather happens so quickly, these are hard to warn for using radar • Main Radar Signatures: i) Maximum reflectivity core developing at higher levels than other storms ii) Maximum top and maximum reflectivity co- located iii) Rapidly descending core iv) pure divergence or convergence in velocity data Severe cell has its max reflectivity core higher up Z With a descending reflectivity core, you’d see the reds quickly heading down toward the ground with each new scan (typically around 5 minutes apart) Small Scale Winds - Divergence/Convergence - Divergent Signature Often seen at storm top level or near the Note the position of the radar relative to the ground at close velocity signatures. This range to a pulse type is critical for proper storm interpretation of the small scale velocity data. Convergence would show colors reversed Multicells (especially QLCSs– quasi-linear convective systems) • Main Radar Signatures i) Weak echo region (WER) or overhang on inflow side with highest top for the multicell cluster over this area (implies very strong updraft) ii) Strong convergence couplet near inflow boundary Weak Echo Region (left: NWS Western Region; right: NWS JETSTREAM) • Associated with the updraft of a supercell thunderstorm • Strong rising motion with the updraft results in precipitation/ hail echoes being shifted upward • Can be viewed on one radar surface (left) or in the vertical (schematic at right) Crude schematic of -
Variable Capacitors in RF Circuits
Source: Secrets of RF Circuit Design 1 CHAPTER Introduction to RF electronics Radio-frequency (RF) electronics differ from other electronics because the higher frequencies make some circuit operation a little hard to understand. Stray capacitance and stray inductance afflict these circuits. Stray capacitance is the capacitance that exists between conductors of the circuit, between conductors or components and ground, or between components. Stray inductance is the normal in- ductance of the conductors that connect components, as well as internal component inductances. These stray parameters are not usually important at dc and low ac frequencies, but as the frequency increases, they become a much larger proportion of the total. In some older very high frequency (VHF) TV tuners and VHF communi- cations receiver front ends, the stray capacitances were sufficiently large to tune the circuits, so no actual discrete tuning capacitors were needed. Also, skin effect exists at RF. The term skin effect refers to the fact that ac flows only on the outside portion of the conductor, while dc flows through the entire con- ductor. As frequency increases, skin effect produces a smaller zone of conduction and a correspondingly higher value of ac resistance compared with dc resistance. Another problem with RF circuits is that the signals find it easier to radiate both from the circuit and within the circuit. Thus, coupling effects between elements of the circuit, between the circuit and its environment, and from the environment to the circuit become a lot more critical at RF. Interference and other strange effects are found at RF that are missing in dc circuits and are negligible in most low- frequency ac circuits. -
12.9 Experimental High-Resolution Wsr-88D Measurements in Severe Storms
12.9 EXPERIMENTAL HIGH-RESOLUTION WSR-88D MEASUREMENTS IN SEVERE STORMS Rodger Brown1, Bradley Flickinger1,2, Eddie Forren1,2, David Schultz1,2, Phillip Spencer1,2, Vincent Wood1, and Conrad Ziegler1 1 NOAA, National Severe Storms Laboratory, Norman, OK 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 1. INTRODUCTION resolution to current-resolution measurements in mesocyclones, we present some of the simulation Through use of Doppler velocity simulations, results of Wood et al. (2001). A comparison of Wood et al. (2001) and Brown et al. (2002) have simulated Doppler radar scans across a shown that stronger Doppler velocity signatures of mesocyclone for 1.0o and 0.5o azimuthal sampling mesocyclones and tornadoes, respectively, can is shown in Fig. 1. The curves with dots along be obtained when WSR-88D (Weather Surveil- them represent the curves along which Doppler lance Radar–1988 Doppler) measurements are velocity measurements are made. There are two made at azimuthal sampling intervals of 0.5o factors illustrated in Fig. 1 that contribute to instead of the current intervals of 1.0o. In addition, stronger mesocyclone signatures with 0.5o for signatures exceeding a particular Doppler azimuthal sampling. First, the peaks of the meas- velocity threshold, the signatures are detectable urement curve in Fig. 1b are closer to the pointed 50% farther in range using 0.5o azimuthal peaks of the “true” curve being sampled by the sampling. radar than in Fig. 1a. The peaks are closer To help substantiate the simulation results, because the radar beam has to scan only half the the National Severe Storms Laboratory’s (NSSL) distance in azimuth for 0.5o sampling and testbed WSR-88D radar (KOUN) collected high- therefore there is less smearing of the true curve. -
Fuzzy Rule–Based Approach for Detection of Bounded Weak-Echo Regions in Radar Images
1304 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 45 Fuzzy Rule–Based Approach for Detection of Bounded Weak-Echo Regions in Radar Images NIKHIL R. PAL Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta, India ACHINTYA K. MANDAL Gitanjali Net, Visva-Bharati University, Santiniketan, India SRIMANTA PAL AND JYOTIRMAY DAS Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta, India V. LAKSHMANAN National Severe Storms Laboratory, University of Oklahoma, Norman, Oklahoma (Manuscript received 4 February 2005, in final form 27 February 2006) ABSTRACT A method for the detection of a bounded weak-echo region (BWER) within a storm structure that can help in the prediction of severe weather phenomena is presented. A fuzzy rule–based approach that takes care of the various uncertainties associated with a radar image containing a BWER has been adopted. The proposed technique automatically finds some interpretable (fuzzy) rules for classification of radar data related to BWER. The radar images are preprocessed to find subregions (or segments) that are suspected candidates for BWERs. Each such segment is classified into one of three possible cases: strong BWER, marginal BWER, or no BWER. In this regard, spatial properties of the data are being explored. The method has been tested on a large volume of data that are different from the training set, and the performance is found to be very satisfactory. It is also demonstrated that an interpretation of the linguistic rules extracted by the system described herein can provide important characteristics about the underlying process. 1. Introduction interaction between the updrafts and the vertically sheared environment strongly controls the degree of Supercell thunderstorms are perhaps the most vio- the organization of a storm and the severity of convec- lent of all thunderstorm types and are capable of pro- tion. -
Agilent 89441A Vector Signal Analyzer Dc to 2.65 Ghz Data Sheet
Agilent 89441A Vector Signal Analyzer dc to 2.65 GHz Data Sheet Definitions Introduction Analog demodulation mode = Measurements with AM, Specifications describe warranted performance PM, and FM demodulation capabilities. over the temperature range of 0° to 45°C (except where noted) and include a 30-minute warm-up Baseband = dc to 10 MHz measurements. from ambient conditions, automatic calibrations enabled, auto-zero on, time domain calibration off, Baseband time = Time-domain measurements selected and anti-alias filter in, unless noted otherwise. by setting start frequency to exactly 0 Hz or choos- Supplemental characteristics identified as “typical” ing full span in 0 to 10 MHz measurements. or “characteristic” provide useful information by giving non-warranted performance parameters. dBc = dB relative to input signal level. Typical performance is applicable from 20° to 30°C. When enabled, automatic calibrations are periodi- dBfs = dB relative to full scale amplitude range cally performed to compensate for the effects of setting. Full scale is approximately 2 dB below temperature and time sensitivities. During the cali- ADC overload. bration, no signals > 0 dBm should be connected to the front panel inputs. FS or fs = Full scale; synonymous with amplitude range or input range. Feature summary RBW = Resolution bandwidth. Frequency dc to 2.650 GHz RF = 2 MHz to 2.65 GHz measurements. 51 to 3201 points Center frequency signal-tracking Scalar mode = Measurements with only frequency- Instrument modes domain analysis available. Frequency spans up to Scalar (frequency-domain only) 2648 MHz. Vector (amplitude and phase information in frequency and time domain and also SNR = Signal to noise ratio. -
Supercell Radar Signatures
Supercell Radar Signatures OBJECTIVES 1. The student will understand how to use WxScope to access current and archived radar data. 2. The student will understand the reflectivity values frequently associated with hail and heavy rain. 3. The student will understand reflectivity patterns associated with hook echoes and tornadic circulations. 4. The student will understand the fundamentals of supercell storm motion. 5. Given a series of Doppler images from a supercell, the student will be able to identify the hook echo, presence of hail, heavy rain, and tornadic circulation. 6. The student will be able to predict storm locations relative to current radar images. 7. The student will be able to compare predictions to actual storm locations to determine if the storm moved as expected, moved right or left of predicted path. PREREQUISITES MATERIALS Basic understanding of WeatherScope. Computer Completion of Understanding Weather Radar WxScope software lecture in the OCS EarthStorm series Archived radar data VOCABULARY Hook echo: A pendant-shaped echo usually toward the right rear of the echo on a radar screen that indicates the presence of a mesocyclone and possible presence of a tornado. Radar: An electronic instrument used to detect objects (such as precipitation) by their ability to reflect and scatter microwaves back to a receiver. Reflectivity: A measure of the fraction of radiation reflected by a given surface; defined as a ratio of the radiant energy reflected to the total that is incident upon a surface. Generally “reflectivity” is used in place of the phrase “radar reflectivity factor” or “equivalent radar reflectivity factor.” Supercell: A large, long-lived (up to several hours) cell consisting of one quasi-steady updraft- downdraft couplet that is generally capable or producing the most sever weather (tornadoes, high winds, and giant hail). -
Convective Storm Structure and Evolution Presented by the Warning Decision Training Branch
Distance Learning Operations Course IC 5.7: Convective Storm Structure and Evolution Presented by the Warning Decision Training Branch Version: 0308.2 Distance Learning Operations Course Table of Contents Introduction ..................................................................................................... 1 Objectives ................................................................................................................................................ 3 Lesson 1....................................................................................................................................3 Lesson 2....................................................................................................................................3 Lesson 3....................................................................................................................................3 Lesson 1: Fundamental Relationships Between Shear and Instability on Convective Storm Structure and Type. ......................................................... 6 Objective 1 ............................................................................................................................................... 6 Effects of Shear .........................................................................................................................6 Objective 2 ............................................................................................................................................... 7 Objective 3 ............................................................................................................................................ -
Understanding Key Parameters for RF-Sampling Data Converters
White Paper: Zynq UltraScale+ RFSoCs WP509 (v1.0) February 20, 2019 Understanding Key Parameters for RF-Sampling Data Converters Xilinx® Zynq® UltraScale+™ RFSoCs provide a single device RF-to-output platform for the most demanding applications. Updated performance metrics more accurately present the direct-sampling RF capabilities of these devices. ABSTRACT In direct-sampling RF designs, data converters are typically characterized by the NSD, IM3, and ACLR parameters rather than by traditional metrics like SNR and ENOB. In software-defined radio and similar narrow-band use cases, it is more important to quantify the amount of data-converter noise falling into the band(s) of interest; legacy data conversion metrics are ill-suited to do this. This white paper first presents the mathematical relationships underlying traditional ADC parameters—SFDR, SNR, SNDR (SINAD), and ENOB—and illustrates why these metrics provide good characterization of data converters in wide-band applications such as superheterodyne receivers. It then delineates why these metrics are inappropriate for data converters that do not function over their full Nyquist bandwidth, as in direct RF sampling applications like SDR. Derivation and measurement of NSD, IM3, and ACLR are described in detail, including use of the Xilinx RF Data Converter Evaluation Tool in the measurement of RF data conversion parameters. © Copyright 2019 Xilinx, Inc. Xilinx, the Xilinx logo, Alveo, Artix, ISE, Kintex, Spartan, Versal, Virtex, Vivado, Zynq, and other designated brands included herein are trademarks of Xilinx in the United States and other countries. All other trademarks are the property of their respective owners. WP509 (v1.0) February 20, 2019 www.xilinx.com 1 Understanding Key Parameters for RF-Sampling Data Converters Introduction Analog data converters based on vacuum-tube technology were developed during World War II for message encryption systems. -
Doppler Radar Meteorological Observations
U.S. DEPARTMENT OF COMMERCE/ National Oceanic and Atmospheric Administration OFFICE OF THE FEDERAL COORDINATOR FOR METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH FEDERAL METEOROLOGICAL HANDBOOK NO. 11 DOPPLER RADAR METEOROLOGICAL OBSERVATIONS PART B DOPPLER RADAR THEORY AND METEOROLOGY FCM-H11B-2005 Washington, DC December 2005 THE FEDERAL COMMITTEE FOR METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH (FCMSSR) VADM CONRAD C. LAUTENBACHER, JR., USN (RET.) MR. RANDOLPH LYON Chairman, Department of Commerce Office of Management and Budget DR. SHARON HAYS (Acting) MR. CHARLES E. KEEGAN Office of Science and Technology Policy Department of Transportation DR. RAYMOND MOTHA MR. DAVID MAURSTAD (Acting) Department of Agriculture Federal Emergency Management Agency Department of Homeland Security BRIG GEN DAVID L. JOHNSON, USAF (RET.) Department of Commerce DR. MARY L. CLEAVE National Aeronautics and Space MR. ALAN SHAFFER Administration Department of Defense DR. MARGARET S. LEINEN DR. ARISTIDES PATRINOS National Science Foundation Department of Energy MR. PAUL MISENCIK DR. MAUREEN MCCARTHY National Transportation Safety Board Science and Technology Directorate Department of Homeland Security MR. JAMES WIGGINS U.S. Nuclear Regulatory Commission DR. MICHAEL SOUKUP Department of the Interior DR. LAWRENCE REITER Environmental Protection Agency MR. RALPH BRAIBANTI Department of State MR. SAMUEL P. WILLIAMSON Federal Coordinator MR. JAMES B. HARRISON, Executive Secretary Office of the Federal Coordinator for Meteorological Services and Supporting Research THE INTERDEPARTMENTAL COMMITTEE FOR METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH (ICMSSR) MR. SAMUEL P. WILLIAMSON, Chairman MS. LISA BEE Federal Coordinator Federal Aviation Administration Department of Transportation MR. THOMAS PUTERBAUGH Department of Agriculture DR. JONATHAN M. BERKSON United States Coast Guard MR. JOHN E. JONES, JR. Department of Homeland Security Department of Commerce MR. -
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