Severe Weather in the United States
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Severe Storms in the Midwest
Informational/Education Material 2006-06 Illinois State Water Survey SEVERE STORMS IN THE MIDWEST Stanley A. Changnon Kenneth E. Kunkel SEVERE STORMS IN THE MIDWEST By Stanley A. Changnon and Kenneth E. Kunkel Midwestern Regional Climate Center Illinois State Water Survey Champaign, IL Illinois State Water Survey Report I/EM 2006-06 i This report was printed on recycled and recyclable papers ii TABLE OF CONTENTS Abstract........................................................................................................................................... v Chapter 1. Introduction .................................................................................................................. 1 Chapter 2. Thunderstorms and Lightning ...................................................................................... 7 Introduction ........................................................................................................................ 7 Causes ................................................................................................................................. 8 Temporal and Spatial Distributions .................................................................................. 12 Impacts.............................................................................................................................. 13 Lightning........................................................................................................................... 14 References ....................................................................................................................... -
American Meteorological Society Revised Manuscript Click Here to Download Manuscript (Non-Latex): JAMC-D-14-0252 Revision3.Docx
AMERICAN METEOROLOGICAL SOCIETY Journal of Applied Meteorology and Climatology 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/JAMC-D-14-0252.1 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: Wang, Y., and B. Geerts, 2015: Vertical-plane dual-Doppler radar observations of cumulus toroidal circulations. J. Appl. Meteor. Climatol. doi:10.1175/JAMC-D-14- 0252.1, in press. © 2015 American Meteorological Society Revised manuscript Click here to download Manuscript (non-LaTeX): JAMC-D-14-0252_revision3.docx Vertical-plane dual-Doppler radar observations of cumulus toroidal circulations Yonggang Wang1, and Bart Geerts University of Wyoming Submitted to J. Appl. Meteor. Climat. October 2014 Revised version submitted in May 2015 1 Corresponding author address: Yonggang Wang, Department of Atmospheric Science, University of Wyoming, Laramie WY 82071, USA; email: [email protected] 1 Profiling dual-Doppler radar observations of cumulus toroidal circulations 2 3 Abstract 4 5 6 High-resolution vertical-plane dual-Doppler velocity data, collected by an airborne profiling 7 cloud radar in transects across non-precipitating orographic cumulus clouds, are used to examine 8 vortical circulations near cloud top. -
Äikesega) Kaasnevad Ohtlikud Ilmanähtused
TALLINNA TEHNIKAÜLIKOOL Eesti Mereakadeemia Merenduskeskus Veeteede lektoraat Raldo Täll RÜNKSAJUPILVEDEGA KAASNEVAD OHTLIKUD ILMANÄHTUSED LÄÄNEMEREL Lõputöö Juhendajad: Jüri Kamenik Lia Pahapill Tallinn 2016 SISUKORD SISUKORD ................................................................................................................................ 2 SÕNASTIK ................................................................................................................................ 4 SISSEJUHATUS ........................................................................................................................ 6 1. RÜNKSAJUPILVED JA ÄIKE ............................................................................................. 8 1.1. Äikese tekkimine ja areng ............................................................................................. 10 1.1.1. Äikese arengustaadiumid ........................................................................................ 11 1.2. Äikeste klassifikatsioon ................................................................................................. 14 1.2.1. Sünoptilise olukorra põhine liigitus ........................................................................ 14 1.2.2. Äikese seos tsüklonitega ......................................................................................... 15 1.2.3. Organiseerumispõhine liigitus ................................................................................ 16 2. RÜNKSAJUPILVEDEGA (ÄIKESEGA) KAASNEVAD OHTLIKUD ILMANÄHTUSED -
Session 8.Pdf
MARTIN SETVÁK [email protected] CZECH HYDROMETEOROLOGICAL INSTITUTE ČESKÝ HYDROMETEOROLOGICKÝ ÚSTAV http://www.chmi.cz http://www.setvak.cz Anticipated benefits of improved temporal and spatial resolution (with focus on deep convective clouds) EUMeTrain Event Week on MTG-I Satellite, 7 – 11 November 2016 Martin Setvák version: 2016-11-11 Introduction The most significant impact of improved spatial resolution and shorter scan interval: observations (detection, monitoring, nowcasting, …) and studies of short-lived and small scale features or phenomena e.g. fires, valley fog, shallow convection, and tops of deep convective clouds (storms) – namely their overshooting tops Martin Setvák Introduction The most significant impact of improved spatial resolution and shorter scan interval: observations (detection, monitoring, nowcasting, …) and studies of short-lived and small scale features or phenomena e.g. fires, valley fog, shallow convection, and tops of deep convective clouds (storms) – namely their overshooting tops geometrical properties and characteristics (visible and near-IR bands), cloud microphysics, cloud-top brightness temperature (BT) Martin Setvák Overshooting tops definition and appearance OVERSHOOTING TOP (anvil dome, penetrating top): A domelike protrusion above a cumulonimbus anvil, representing the intrusion of an updraft through its equilibrium level (level of neutral buoyancy). It is usually a transient feature because the rising parcel's momentum acquired during its buoyant ascent carries it past the point where it is -
Mesoscale Convective Systems
OCTOBER 2007 S T E I G E R E T A L . 3303 Total Lightning Signatures of Thunderstorm Intensity over North Texas. Part II: Mesoscale Convective Systems SCOTT M. STEIGER Department of Earth Sciences, State University of New York at Oswego, Oswego, New York RICHARD E. ORVILLE AND LAWRENCE D. CAREY Department of Atmospheric Sciences, Texas A&M University, College Station, Texas (Manuscript received 4 April 2006, in final form 25 January 2007) ABSTRACT Total lightning data from the Lightning Detection and Ranging (LDAR II) research network in addition to cloud-to-ground flash data from the National Lightning Detection Network (NLDN) and data from the Dallas–Fort Worth, Texas, Weather Surveillance Radar-1988 Doppler (WSR-88D) station (KFWS) were examined from individual cells within mesoscale convective systems that crossed the Dallas–Fort Worth region on 13 October 2001, 27 May 2002, and 16 June 2002. LDAR II source density contours were comma shaped, in association with severe wind events within mesoscale convective systems (MCSs) on 13 October 2001 and 27 May 2002. This signature is similar to the radar reflectivity bow echo. The source density comma shape was apparent 15 min prior to a severe wind report and lasted more than 20 min during the 13 October storm. Consistent relationships between severe straight-line winds, radar, and lightning storm cell characteristics (e.g., lightning heights) were not found for cells within MCSs as was the case for severe weather in supercells in Part I of this study. Cell interactions within MCSs are believed to weaken these relationships as reflectivity and lightning from nearby storms contaminate the cells of interest. -
Flash Flooding
NationalNational OceanicOceanic andand AtmosphericAtmospheric AdministrationAdministration (NOAA)(NOAA) NationalNational WeatherWeather ServiceService (NWS)(NWS) PresentsPresents SevereSevere WeatherWeather ObserverObserver andand SafetySafety TrainingTraining 20052005 Severe Weather Spotter Line 1-888-668-3344 Spotter Reports E-mail: www.crh.noaa.gov/espotter Homepage Address: www.crh.noaa.gov/iwx 2 GoalsGoals ofof thethe TrainingTraining You will learn: • Definitions of important weather terms and severe weather criteria • How thunderstorms develop and why some become severe • How to correctly identify cloud features that may or may not be associated with severe weather • What information the observer is to report and how to report it • Ways to receive weather information before and during severe weather events • Observer Safety! 3 WFOWFO NorthernNorthern IndianaIndiana (WFO(WFO IWX)IWX) CountyCounty WarningWarning andand ForecastForecast AreaArea (CWFA)(CWFA) Work with public, state and local officials Dedicated team of highly trained professionals 24 hours a day/7 days a week Prepare forecasts and warnings for 2.3 million people in 37 counties 4 SKYWARNSKYWARN (Severe(Severe Weather)Weather) ObserversObservers Why Are You Critical to NWS Operations? • Help overcome Doppler Radar limitations • Provide ground truth which can be correlated with radar signatures prior to, during, and after severe weather • Ground truth reports in warnings heighten public awareness and allow us to have confidence in our warning decisions 5 SKYWARNSKYWARN (Severe(Severe Weather)Weather) ObserversObservers Why Are You Critical to NWS Operations? • The NWS receives hundreds of reports of “False” or “Mis-Identified” funnel clouds and tornadoes each year • We strongly rely on the “2 out of 3” Rule before issuing a warning. Of the following, we like to have 2 out of 3 present before sending out a warning. -
Glossary of Severe Weather Terms
Glossary of Severe Weather Terms -A- Anvil The flat, spreading top of a cloud, often shaped like an anvil. Thunderstorm anvils may spread hundreds of miles downwind from the thunderstorm itself, and sometimes may spread upwind. Anvil Dome A large overshooting top or penetrating top. -B- Back-building Thunderstorm A thunderstorm in which new development takes place on the upwind side (usually the west or southwest side), such that the storm seems to remain stationary or propagate in a backward direction. Back-sheared Anvil [Slang], a thunderstorm anvil which spreads upwind, against the flow aloft. A back-sheared anvil often implies a very strong updraft and a high severe weather potential. Beaver ('s) Tail [Slang], a particular type of inflow band with a relatively broad, flat appearance suggestive of a beaver's tail. It is attached to a supercell's general updraft and is oriented roughly parallel to the pseudo-warm front, i.e., usually east to west or southeast to northwest. As with any inflow band, cloud elements move toward the updraft, i.e., toward the west or northwest. Its size and shape change as the strength of the inflow changes. Spotters should note the distinction between a beaver tail and a tail cloud. A "true" tail cloud typically is attached to the wall cloud and has a cloud base at about the same level as the wall cloud itself. A beaver tail, on the other hand, is not attached to the wall cloud and has a cloud base at about the same height as the updraft base (which by definition is higher than the wall cloud). -
Tornado Warnings: Delivery, Economics, & Public Perception
Tornado Warnings: Delivery, Economics, & Public Perception Bibliography Katie Rowley, Librarian, NOAA Central Library Trevor Riley, Head of Public Services, NOAA Central Library Christine Reed, Librarian, Oklahoma University/NOAA NCRL subject guide 2018-15 10.7289/V5/SG-NCRL-18-15 June 2018 U.S. Department of Commerce National Oceanic and Atmospheric Administration Office of Oceanic and Atmospheric Research NOAA Central Library – Silver Spring, Maryland Table of Contents Background & Scope ................................................................................................................................. 3 Sources Reviewed ..................................................................................................................................... 3 Section I: Economic Impact, Risk & Mitigation ......................................................................................... 4 Section II: Public Perception & Behavior ................................................................................................ 11 Section III: Tornado Identification & Technology ................................................................................... 30 Section IV: Warning Process, Development, & Delivery ......................................................................... 36 2 Background & Scope The Weather Research and Forecasting Innovation Act of 2017 requires the National Oceanic and Atmospheric Administration (NOAA) to prioritize weather research to improve weather data, modeling, computing, forecasts, -
Thunderstorm Anatomy and Dynamics
THUNDERSTORM ANATOMY AND DYNAMICS An Overview Prepared by LCDR Bill Nisley MR 3421 • Cloud Physics Naval Postgraduate School • Monterey, California [email protected] Photo credits: Thunderstorm Cell and Mammatus Clouds by: Michael Bath; Tornado by Daphne Zaras / NSSL; Supercell Thunderstorm by: AMOS; Wall Cloud by: Greg Michels 1. Introduction The purpose of this paper is to present a broad overview of the various cloud structures displayed during the life cycle of a thunderstorm and the atmospheric dynamics associated with each. Knowledge of atmospheric dynamics provides for a keener understanding of the physical processes related to the “why and how” certain cloud features form. Accordingly, observation of cloud features presents visual queuing of changes in the atmosphere. 2. Thunderstorm Formation and Stages of Development Thunderstorm development is dependent on three basic components: moisture, instability, and some form of lifting mechanism. 2.1 Moisture – As air near the surface is lifted higher in the atmosphere and cooled, available water vapor condenses into small water droplets which form clouds. As condensation of water vapor occurs, latent heat is released making the rising air warmer and less dense than its surroundings (figure 1). The added heat allows the air (parcel) to continue to rise and form an updraft within the developing cloud structure. 2.1.1 In general1, low level moisture increases instability simply by making more latent heat available to the lower atmosphere. Increasing mid level moisture can decrease instability in the atmosphere because moist air is less dense than dry air and therefor is unable to evaporate • Figure 1 – Positive buoyancy / instability as a result of precipitation and cloud droplets as condensation and release of latent heat. -
Satellite INVESTIGATION INTO the JUNE 10, 1984 THUNDERSTORM VINCENNES, INDIANA
National Weather Digest Satellite INVESTIGATION INTO THE JUNE 10, 1984 THUNDERSTORM VINCENNES, INDIANA by Mark S. Binkley (1) Department of Geography Indiana State University Terre Haute, Indiana 47809 ABSTRACT SYNOPTIC SETTING On June 10, 1984, a thunderstorm of unusual severity produced heavy precipitation in a small area As is evident in the 160 I GMT image, Vincennes centered on Vincennes, Indiana. Sequential analysis (in southwestern Indiana) has not e xperienced any of the development of the storm using satellite type of significant weathe r. However, several imagery and radar data indicates its origin as part of variables might suggest trouble in the near future. a weak cold front. The active storm cell resulted from the merging of two active cells. Full As is common in summer, the Azores-Bermuda explanation of the event draws upon examination of High has increased in strength and has begun to block prevailing synoptic conditions following the approaching frontal systems. This resulted in a weakening of a blocking High. prolonged period of dry weather in much of the eastern United States during the summer of I 98 If. Terre Haute, IN (55 miles north of Vincennes) went 21f days without precipitation (May 28 through June INTRODUCTION 21, including June 10). The State of Indiana on this date was between At midnight, Sunday, June 10, 1981f, the United the blocking Be rmuda High and the cold front States was bisected by a long, nar row cold front. advancing from the plains. Pressures had fallen This front extended from a Low in northern Wisconsin slightly during this period as the 1020.0 millibar line and stretched southwest to the Texas Panhandle. -
Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014
climate Article Life Cycle Characteristics of Warm-Season Severe Thunderstorms in Central United States from 2010 to 2014 Weibo Liu 1 and Xingong Li 2,* 1 Department of Geosciences, Florida Atlantic University, Boca Raton, FL 33431, USA; [email protected] 2 Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS 66045, USA * Correspondence: [email protected]; Tel.: +1-785-864-5545 Academic Editor: Christina Anagnostopoulou Received: 17 July 2016; Accepted: 6 September 2016; Published: 8 September 2016 Abstract: Weather monitoring systems, such as Doppler radars, collect a high volume of measurements with fine spatial and temporal resolutions that provide opportunities to study many convective weather events. This study examines the spatial and temporal characteristics of severe thunderstorm life cycles in central United States mainly covering Kansas, Oklahoma, and northern Texas during the warm seasons from 2010 to 2014. Thunderstorms are identified using radar reflectivity and cloud-to-ground lightning data and are tracked using a directed graph model that can represent the whole life cycle of a thunderstorm. Thunderstorms were stored in a GIS database with a number of additional thunderstorm attributes. Spatial and temporal characteristics of the thunderstorms were analyzed, including the yearly total number of thunderstorms, their monthly distribution, durations, initiation time, termination time, movement speed and direction, and the spatial distributions of thunderstorm tracks, initiations, and terminations. Results revealed that thunderstorms were most frequent across the eastern part of the study area, especially at the borders between Kansas, Missouri, Oklahoma, and Arkansas. Finally, thunderstorm occurrence is linked to land cover, including a comparison of thunderstorms between urban and surrounding rural areas. -
Nowcasting Applications
Nowcasting Applications African SWIFT Summer School Morné Gijben [email protected] Weather Research South African Weather Service Doc Ref no: RES-PPT-SWIFT-20190729-GIJ002-001.1 2020/01/10 1 What is the difference between nowcasting and forecasting? 2020/01/10 2 Definition of time ranges • Nowcasting: A description of current weather parameters and 0 to 2 hours’ description of forecast weather parameters • Very short-range weather forecasting: Up to 12 hours’ description of weather parameters • Short-range weather forecasting: Beyond 12 hours’ and up to 72 hours’ description of weather parameters • Medium-range weather forecasting: Beyond 72 hours’ and up to 240 hours’ description of weather parameters • Extended-range weather forecasting: Beyond 10 days’ and up to 30 days’ description of weather parameters. Usually averaged and expressed as a departure from climate values for that period 2020/01/10 3 Definition of time ranges • Long-range forecasting: From 30 days up to two years • Month forecast: Description of averaged weather parameters expressed as a departure (deviation, variation, anomaly) from climate values for that month at any lead-time • Seasonal forecast: Description of averaged weather parameters expressed as a departure from climate values for that season at any lead-time • Climate forecasting: Beyond two years • Climate variability prediction: Description of the expected climate parameters associated with the variation of interannual, decadal and multi-decadal climate anomalies • Climate prediction: Description