Visualization of Tornado Data
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Wind Speed-Damage Correlation in Hurricane Katrina
JP 1.36 WIND SPEED-DAMAGE CORRELATION IN HURRICANE KATRINA Timothy P. Marshall* Haag Engineering Co. Dallas, Texas 1. INTRODUCTION According to Knabb et al. (2006), Hurricane Katrina Mehta et al. (1983) and Kareem (1984) utilized the was the costliest hurricane disaster in the United States to concept of wind speed-damage correlation after date. The hurricane caused widespread devastation from Hurricanes Frederic and Alicia, respectively. In essence, Florida to Louisiana to Mississippi making a total of three each building acts like an anemometer that records the landfalls before dissipating over the Ohio River Valley. wind speed. A range of failure wind speeds can be The storm damaged or destroyed many properties, determined by analyzing building damage whereas especially near the coasts. undamaged buildings can provide upper bounds to the Since the hurricane, various agencies have conducted wind speeds. In 2006, WSEC developed a wind speed- building damage assessments to estimate the wind fields damage scale entitled the EF-scale, named after the late that occurred during the storm. The National Oceanic Dr. Ted Fujita. The author served on this committee. and Atmospheric Administration (NOAA, 2005a) Wind speed-damage correlation is useful especially conducted aerial and ground surveys and published a when few ground-based wind speed measurements are wind speed map. Likewise, the Federal Emergency available. Such was the case in Hurricane Katrina when Management Agency (FEMA, 2006) conducted a similar most of the automated stations failed before the eye study and produced another wind speed map. Both reached the coast. However, mobile towers were studies used a combination of wind speed-damage deployed by Texas Tech University (TTU) at Slidell, LA correlation, actual wind measurements, as well as and Bay St. -
Rotating Storms: Supercells, Tornadoes, and Tropical Storms
RotatingRotating Storms:Storms: SupercellsSupercells,, Tornadoes,Tornadoes, andand TropicalTropical StormsStorms AT 351 Lab 12 April 14, 2008 SupercellsSupercells ! Characterized by rotating updrafts (called a mesocyclone) ! Differ from multicell cluster because smaller updrafts merge into a main rotated updraft rather than developing separate and competing cells ! Can persist for 12 hours and travel hundreds of miles ! Forms in environments of strong winds aloft ! Winds veer with height from the surface ! Can be classified as either High Precipitation (HP) or Low Precipitation (LP) SupercellSupercell HPHP SupercellsSupercells •Dark rain and hail core •Prolific producers of lightning and flash floods LPLP SupercellsSupercells ! Typically associated with a dry line ! Typically small and lacking in rainfall TornadoesTornadoes ! Formation ! Life Cycle ! Definition ! Types ! Damage ! EF-scale FormationFormation ! We know relatively little about the formation of tornadoes, known as tornadogenesis ! However, we do know the basic steps along the way ! It is the details we are missing, but they are very crucial details VorticityVorticity ! Vorticity is the name we give to a natural spin in the air ! Caused mainly by wind shear ! Just like a pinwheel Non-SupercellNon-Supercell TornadoTornado FormationFormation ! Vertical wind shear crucial VorticityVorticity TiltingTilting ! After horizontal rotation is established, the storm’s updraft works to tilt it upright ! Now the storm has a vertically rotating component SuctionSuction VorticesVortices -
Ttu Fujita 000088.Pdf (5.624Mb)
An Interpretation of the Probable Cause of Chronic Intolerable Pain at the Base of my Feet Special Report Requested by Dr. Robert F. Abbey, Jr. Associate Director for Integration Ocean, Atmosphere, and Space S&T Department Office of Naval Research and Supported by ONR Grant N00014-91-J1136 and University of Chicago Account 6-31562 June 1997 Delayed Delivery Tetsuya Theodore Fujita, Director Wind Research Laboratory Department of Geophysical Sciences The University of Chicago 5734 S. Ellis Avenue Chicago, Illinois 60637 U. S.A. 1 Preface I have been feeling extremely sorry to the sponsors of my Wind Research Laboratory and to Dean Oxtoby of The Division of Physical Sciences for not achieving my promised research, as originally planned. As you know, I began working on the research being encouraged by the 17 January 1996 letter stating, in effect, that my blood sugar control is wonderful and my diabetes will not influence my health or longevity. The letter was used for my grant negotiation, in support of my health. Unexpectedly and coincidentally, seven months thereafter, on 17 August 1996, I began suffering from the chronic severe pain at the base of my feet, resulting in ambulatory difficulty. Furthermore, neither the cause of pain nor treatment method has been established. Now the intolerable pain is accompanied by my cold feet, feeling like cold blooded Homosapiens. I am, hereby submitting you "An Interpretation of the Probable Cause of the Pain" written to the best of my meteorology-oriented analyses, which could be different from the medical analyses by each doctor. 3 REFERENCES dd .... -
What Are We Doing with (Or To) the F-Scale?
5.6 What Are We Doing with (or to) the F-Scale? Daniel McCarthy, Joseph Schaefer and Roger Edwards NOAA/NWS Storm Prediction Center Norman, OK 1. Introduction Dr. T. Theodore Fujita developed the F- Scale, or Fujita Scale, in 1971 to provide a way to compare mesoscale windstorms by estimating the wind speed in hurricanes or tornadoes through an evaluation of the observed damage (Fujita 1971). Fujita grouped wind damage into six categories of increasing devastation (F0 through F5). Then for each damage class, he estimated the wind speed range capable of causing the damage. When deriving the scale, Fujita cunningly bridged the speeds between the Beaufort Scale (Huler 2005) used to estimate wind speeds through hurricane intensity and the Mach scale for near sonic speed winds. Fujita developed the following equation to estimate the wind speed associated with the damage produced by a tornado: Figure 1: Fujita's plot of how the F-Scale V = 14.1(F+2)3/2 connects with the Beaufort Scale and Mach number. From Fujita’s SMRP No. 91, 1971. where V is the speed in miles per hour, and F is the F-category of the damage. This Amazingly, the University of Oklahoma equation led to the graph devised by Fujita Doppler-On-Wheels measured up to 318 in Figure 1. mph flow some tens of meters above the ground in this tornado (Burgess et. al, 2002). Fujita and his staff used this scale to map out and analyze 148 tornadoes in the Super 2. Early Applications Tornado Outbreak of 3-4 April 1974. -
Storm Data and Unusual Weather Phenomena ....…….…....……………
MAY 2006 VOLUME 48 NUMBER 5 SSTORMTORM DDATAATA AND UNUSUAL WEATHER PHENOMENA WITH LATE REPORTS AND CORRECTIONS NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION noaa NATIONAL ENVIRONMENTAL SATELLITE, DATA AND INFORMATION SERVICE NATIONAL CLIMATIC DATA CENTER, ASHEVILLE, NC Cover: Baseball-to-softball sized hail fell from a supercell just east of Seminole in Gaines County, Texas on May 5, 2006. The supercell also produced 5 tornadoes (4 F0’s 1 F2). No deaths or injuries were reported due to the hail or tornadoes. (Photo courtesy: Matt Jacobs.) TABLE OF CONTENTS Page Outstanding Storm of the Month …..…………….….........……..…………..…….…..…..... 4 Storm Data and Unusual Weather Phenomena ....…….…....……………...........…............ 5 Additions/Corrections.......................................................................................................................... 406 Reference Notes .............……...........................……….........…..……........................................... 427 STORM DATA (ISSN 0039-1972) National Climatic Data Center Editor: William Angel Assistant Editors: Stuart Hinson and Rhonda Herndon STORM DATA is prepared, and distributed by the National Climatic Data Center (NCDC), National Environmental Satellite, Data and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA). The Storm Data and Unusual Weather Phenomena narratives and Hurricane/Tropical Storm summaries are prepared by the National Weather Service. Monthly and annual statistics and summaries of tornado and lightning events -
Probable Maximum Precipitation Estimates, United States East of the 105Th Meridian
HYDROMETEOROLOGICAL REPORT 'N0.53 L D ..... 'C..,p "\ ..u o tM/ 'A1 ws/t.d/M 'b !>"'"'"' , , 1 1 ~, 5 E.~..~t:-west J/.,e; h~ s,J~ ..s, .... ~ ,41'b ~·qto Seasonal Variation of 10-Square-Mile Probable Maximum Precipitation Estimates, United States East of the 105th Meridian ,_ U.S. DEPARTMENT OF COMMERCE, , -- NATIONAL OCEANIC AND ATMOS~HERIC ADMINISI'RATION U.S. NUCLEAR REGULATORY COMMISSION - , - Silver Spnng, Md , , Apn11980 U.S. Department of Commerce U.S. Nuclear Regulatory National Oceanic and Atmospheric Commission Administration NUREG/CR-1486 Hydrometeorological Report No. 53 SEASONAL VARIATION OF 10-SQUARE-MILE PROBABLE MAXIMUM PRECIPITATION ESTIMATES) UNITED STATES EAST OF THE 105TH MERIDIAN Prepared by Francis P. Ho and John T. Riedel Hydrometeorological Branch Office of Hydrology National Weather Service Washington, D.C. April 1980 TABLE OF CONTENTS Page Abstract. 1 1. Introduction . 1 1.1 Authorization. 1 1.2 Purpose. 1 1.3 Scope ••... 1 1.4 Definitions .. 2 1.5 Previous study 2 2. Basic data . 2 2.1 Background • • • • • . 2 2.2 Available station rainfall data •• 3 2.2.1 Storm rainfall • • • • • 3 2.2.2 Maximum 1-day or 24-hour values, each month ••••••• 3 2.2.3 Maximum 6-, 12- and 24-hr values, each month . 3 2.2.4 11aximum recorded rainfall at first-order stations. 3 2.2.5 Data tapes, selected stations. 3 2.2.6 Data tapes, 1948-73 •. 5 2.2.7 Canadian data. 5 3. Approach to PMP. • ••. 5 3.1 Summary. 5 3.2 Selected major storm values •. 5 3.3 Moisture maximization. -
The Population Bias of Severe Weather Reports West of the Continental Divide
THE POPULATION BIAS OF SEVERE WEATHER REPORTS WEST OF THE CONTINENTAL DIVIDE Paul Frisbie NOAAlNational Weather Service Weather Forecast Office Grand Junction, Colorado Abstract and had 'significant consequences. Lightning from thun derstorms that afternoon sparked the Marble Cone fire Many severe storms in the West may go unreported or (several miles south of Camp Pico Blanco) that burned unobserved, and therefore, the severe weather climatology 177,866 acres - the third largest wildfire in California's is not fully understood. However, as the population has history (California Department of Forestry and Fire increased in the West, so has the number of severe weath Protection 2004). er reports. To check if a population bias exists, correlation The aforementioned cases illustrate the challenges of coefficients are calculated with respect to population for establishing a relevant severe weather climatology for tornadic and nontornadic severe storm activity. The sam the western US. Since no one knows how many severe ple size for tornadic storms is too small to draw any con storms go unreported or unobserved, there is uncertainty clusion. To calculate the correlation coefficients for non regarding the number of severe storms that actually do tornadic severe storms, population and severe weather occur. People have been gradually moving into areas that data are used for every county in each western state were once unpopulated, and are now observing severe between the years 1986-1995 and 1996-2004. The calcu storms that would not have been observed beforehand. To lated correlation coefficients reveal that population and provide a better understanding of how often severe the number ofnontornadic severe storms are significantly weather occurs, trends for tornadic and nontornadic correlated which indicates that a population bias does severe storms will be examined. -
1 International Approaches to Tornado Damage and Intensity Classification International Association of Wind Engineers
International Approaches to Tornado Damage and Intensity Classification International Association of Wind Engineers (IAWE), International Tornado Working Group 2017 June 6, DRAFT FINAL REPORT 1. Introduction Tornadoes are one of the most destructive natural Hazards on Earth, with occurrences Having been observed on every continent except Antarctica. It is difficult to determine worldwide occurrences, or even the fatalities or losses due to tornadoes, because of a lack of systematic observations and widely varying approacHes. In many jurisdictions, there is not any tracking of losses from severe storms, let alone the details pertaining to tornado intensity. Table 1 provides a summary estimate of tornado occurrence by continent, with details, wHere they are available, for countries or regions Having more than a few observations per year. Because of the lack of systematic identification of tornadoes, the entries in the Table are a mix of verified tornadoes, reports of tornadoes and climatological estimates. Nevertheless, on average, there appear to be more than 1800 tornadoes per year, worldwide, with about 70% of these occurring in North America. It is estimated that Europe is the second most active continent, with more than 240 per year, and Asia third, with more than 130 tornadoes per year on average. Since these numbers are based on observations, there could be a significant number of un-reported tornadoes in regions with low population density (CHeng et al., 2013), not to mention the lack of systematic analysis and reporting, or the complexity of identifying tornadoes that may occur in tropical cyclones. Table 1 also provides information on the approximate annual fatalities, althougH these data are unavailable in many jurisdictions and could be unreliable. -
Storms Are Thunderstorms That Produce Tornadoes, Large Hail Or Are Accompanied by High Winds
From February 17 to 19, a severe storm blasted the Lebanese coast with 100- kilometer (60-mile) winds and dropped as much as 2 meters (7 feet) of snow on parts of the country, news sources said. Temperatures dropped to near freezing along the coast, while snowplows struggled to clear the main roadway between Beirut and Damascus. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite captured this natural-color image on February 20, 2012. Snow covers much of Lebanon, and extends across the border with Syria. Another expanse of snow occurs just north of the Syria-Jordan border. Snow in Lebanon is not uncommon, and the country is home to ski resorts. Still, this fierce storm may have been part of a larger pattern of cold weather in Europe and North Africa. References The Daily Star. (2012, February 18). Lebanon hit by extreme weather conditions. Accessed February 21, 2012. Naharnet. (2012, February 19). Storm subsides after coating Lebanon in snow. Accessed February 21, 2012. NASA image courtesy LANCE/EOSDIS MODIS Rapid Response Team at NASA GSFC. Caption by Michon Scott. Instrument: Terra - MODIS Flooding is the most common of all natural hazards. Each year, more deaths are caused by flooding than any other thunderstorm related hazard. We think this is because people tend to underestimate the force and power of water. Six inches of fast-moving water can knock you off your feet. Water 24 inches deep can carry away most automobiles. Nearly half of all flash flood deaths occur in automobiles as they are swept downstream. -
Ardly a Day Passes in Lubbock, Texas, With- out Strong Winds Racing Across the South Plains. with Gusts of up to 23 Miles Per Ho
BY LARISSA K. TRUE ardly a day passes in Lubbock, Texas, with- out strong winds racing across the South Plains. With gusts of up to 23 miles per hour on an average day, few Lubbockites have grown to appreciate the wind. Many complain, but few understand the phenomenon that has made West Texas unique for decades. In addition to being home to a more-than-typically breezy climate, the city of Lubbock and Texas Tech University’s Southwest Collection/Special Collections Library are the keepers of one of the finest and most sought after compilations of invaluable wind research documentation in the world. On May 20, 2005, the Southwest Collec- tion formally accepted Professor Tetsuya “Ted” Fujita’s meticulous and copious records of every major wind event that occurred from the end of World War II until his death in the late 1990s. 26 • SPRING 2006 vol. 1 No.1 • 27 The image on the previous page shows Fujita’s wind generator laboratory where he studied downbursts. The Southwest ujita’s impressive career began when he taught school in scale was not efficient. The new scale is an enhancement that will Collection/Special Collections Library is home to Ted Fujita’s compilation of Japan during World War II. Following the dropping of make the scale more reliable and consistent.” The EF scale was invaluable wind research, the largest atomic bombs on Hiroshima and Nagasaki in 1945 by developed by the Wind Science and Engineering Research Center such documentation in the world. Scenes below show the aftermath of the Lubbock the United States, the Japanese government dispatched (WISE) researchers along with an assembly of wind engineers, tornado, as well as data of the storm that Fujita to execute a survey and identify whether a weapon universities, private companies, government organizations and hit at 7 p.m. -
Storm Data Dataset Discovery Day
Storm Data Dataset Discovery Day Stuart Hinson Meteorologist National Climatic Data Center – Asheville, NC Climate Services Division NCDC – Dataset Discovery Day 1 Asheville, NC November 2012 National Climatic Data Center • Mission: NCDC’s mission is to manage the Nation's resource of global climatological in-situ and remotely sensed data and information to promote global environmental stewardship; to describe, monitor and assess the climate; and to support efforts to predict changes in the Earth's environment. This effort requires the acquisition, quality control, processing, summarization, dissemination, and preservation of a vast array of climatological data generated by the national and international meteorological services. NCDC – Dataset Discovery Day 2 Asheville, NC November 2012 History of Severe Weather Data • Severe weather data has been gathered since 1826 when observations were recorded in several texts. Some of these sources are listed below: • Meteorological Register 1826 – 1860 • Results of Meteorological Observations 1843 – 1859 • Report to the Chief Signal Officer 1870 – 1891 • Monthly Weather Review 1872 – 1892 • Reports to the Chief of the Weather Bureau 1893 – 1935 • US Meteorological Yearbook 1935 – 1945 • Climatological Daily National Summary 1950 – 1980 • Storm Data 1959 – Current – F8 Typed/Printed format 1959 – 1992 – WordPerfect V5.0 format 1993 – 1995 – Paradox V7.0 format 1996 – 09/2006 – Windows SQL Server 2003 10/2006 – Current NCDC – Dataset Discovery Day 3 Asheville, NC November 2012 The Storm Data Publication -
Extreme Storm Data Catalog Development
Report DSO-11-07 Extreme Storm Data Catalog Development Dam Safety Technology Development Program U.S. Department of the Interior Bureau of Reclamation Technical Service Center Denver, Colorado September 2011 Form Approved REPORT DOCUMENTATION PAGE OMB No. 0704-0188 The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Extreme Storm Data Catalog Development 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Victoria Sankovich 5e. TASK NUMBER R. Jason Caldwell 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) 11. SPONSOR/MONITOR'S REPORT NUMBER(S) 12.