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University of Oklahoma
UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE INVESTIGATION OF POLARIMETRIC MEASUREMENTS OF RAINFALL AT CLOSE AND DISTANT RANGES A DISSERTATION SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the degree of Doctor of Philosophy By SCOTT EDWARD GIANGRANDE Norman, Oklahoma 2007 UMI Number: 3291249 UMI Microform 3291249 Copyright 2008 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 INVESTIGATION OF POLARIMETRIC MEASUREMENTS OF RAINFALL AT CLOSE AND DISTANT RANGES A DISSERTATION APPROVED FOR THE SCHOOL OF METEOROLOGY BY ____________________________________ Dr. Michael Biggerstaff ____________________________________ Dr. Alexander Ryzhkov ____________________________________ Dr. Jerry Straka ____________________________________ Dr. Guifu Zhang ____________________________________ Dr. Mark Yeary © Copyright by SCOTT EDWARD GIANGRANDE 2007 All Rights Reserved. ACKNOWLEDGEMENTS I would like to extend my sincerest thanks to the numerous individuals who have helped me complete this work. To begin, this work would not have been possible without the guidance of my primary research advisor, Dr. Alexander Ryzhkov. His leadership and patience were instrumental throughout this process. I would also like to extend my gratitude to the other members of my committee: Drs. Michael Biggerstaff (Co-Chair and primary OU School of Meteorology advisor), Guifu Zhang, Jerry Straka, and Mark Yeary. The reviews performed by these individuals strengthened this work. I also thank the members of the Radar Research and Development Division (RRDD) at the National Severe Storms Laboratory (NSSL), which includes Drs. Douglas Forsyth, Dusan Zrnic, Dick Doviak, Allen Zahrai, Terry Schuur, Pam Heinselman, Valery Melnikov, Sebastian Torres, Pengfei Zhang and Svetlana Bachmann. -
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. -
2020 Infra Surface Weather Observations
Surface Weather Observations Comparison of Various Observing Systems Scott Landolt & Matthias Steiner National Center for Atmospheric Research [email protected] USHST Infrastructure Summit 12 – 13 March 2020 in Washington, DC © 2020 University Corporation for Atmospheric Research 1 Surface Stations & Reporting Frequency Station Type Frequency of Reports Automated Surface 5 minutes Observing System (ASOS) (limited access to 1 minute data) Automated Weather 20 minutes Observing System (AWOS) 15 minutes (standard), can be Road Weather Information more frequent but varies state to System (RWIS) state and even site to site 5 – 15 minutes, can vary from Mesonet station to station Iowa station network © 2020 University Corporation for Atmospheric Research 2 Reporting Variables Weather Variable ASOS AWOS RWIS Mesonet Temperature X X X X Relative X X X X Humidity/Dewpoint Wind Speed/Direction X X X X Barometric Pressure X X X X Ceiling Height X X X X Visibility X X X X Present Weather X X X X Precipitation X X X X Accumulation Road Condition X X X X X – All Stations Report X – Some Stations Report X – No Stations Report © 2020 University Corporation for Atmospheric Research 3 Station Siting Requirements Station Type Siting Areal Representativeness Automated Surface Miles (varies depending on Airport grounds, unobstructed Observing System (ASOS) local conditions & weather) Automated Weather Miles (varies depending on Airport grounds, unobstructed Observing System (AWOS) local conditions & weather) Next to roadways, can be in canyons, valleys, mountain -
Relative Forecast Impact from Aircraft, Profiler, Rawinsonde, VAD, GPS-PW, METAR and Mesonet Observations for Hourly Assimilation in the RUC
16.2 Relative forecast impact from aircraft, profiler, rawinsonde, VAD, GPS-PW, METAR and mesonet observations for hourly assimilation in the RUC Stan Benjamin, Brian D. Jamison, William R. Moninger, Barry Schwartz, and Thomas W. Schlatter NOAA Earth System Research Laboratory, Boulder, CO 1. Introduction A series of experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3h-12h duration) wind, temperature, and relative humidity forecasts at different vertical levels. This assessment of the value of 7 different observation data types (aircraft (AMDAR and TAMDAR), profiler, rawinsonde, VAD (velocity azimuth display) winds, GPS precipitable water, METAR, and mesonet) on short-range numerical forecasts was carried out for a 10-day period from November- December 2006. 2. Background Observation system experiments (OSEs) have been found very useful to determine the impact of particular observation types on operational NWP systems (e.g., Graham et al. 2000, Bouttier 2001, Zapotocny et al. 2002). This new study is unique in considering the effects of most of the currently assimilated high-frequency observing systems in a 1-h assimilation cycle. The previous observation impact experiments reported in Benjamin et al. (2004a) were primarily for wind profiler and only for effects on wind forecasts. This new impact study is much broader than that the previous study, now for more observation types, and for three forecast fields: wind, temperature, and moisture. Here, a set of observational sensitivity experiments (Table 1) were carried out for a recent winter period using 2007 versions of the Rapid Update Cycle assimilation system and forecast model. -
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. -
Weatherscope Weatherscope Application Information: Weatherscope Is a Stand-Alone Application That Makes Viewing Weather Data Easier
User Guide - Macintosh http://earthstorm.ocs.ou.edu WeatherScope WeatherScope Application Information: WeatherScope is a stand-alone application that makes viewing weather data easier. To run WeatherScope, Mac OS X version 10.3.7, a minimum of 512MB of RAM, and an accelerated graphics card with 32MB of VRAM are required. WeatherScope is distributed freely for noncommercial and educational use and can be used on both Apple Macintosh and Windows operating systems. How do I Download WeatherScope? To download the application, go to http://earthstorm.ocs.ou.edu, select Data, Software, Download, or go to http://www. ocs.ou.edu/software. There will be three options: WeatherBuddy, WeatherScope, and WxScope Plugin. You will want to choose WeatherScope. There are two options under the application: Macintosh or Windows. Choose Macintosh to download the application. The installation wizard will automatically save to your desktop. Go to your desktop and double click on the icon that says WeatherScope- x.x.x.pkg. Several dialog messages will appear. The fi rst message will inform you that you are about to install the application. The next message tells you about computer system requirements in order to download the application. The following message is the Software License Agreement. It is strongly suggested that you read this agreement. If you agree, click Agree. If you do not agree, click Disagree and the software will not be installed onto your computer. The next message asks you to select a destination drive (usually your hard drive). The setup will run and install the software on your computer. You may then press Close. -
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. -
Visualization of Tornado Data
Visualization of Tornado Data Anu Joy, Harshal Chheda, James Chy and Yuxing Sun Drexel University ABSTRACT— The aim of this project is to study the nature of tornadoes with the use of information visualization tools. The emphasis of this project is to study the tornadoes that change directions and their effects. For this report we used a subset of published storm data from the National Oceanographic and Atmospheric Administration (NOAA). The data set included many types of storms, but we focused upon just the storm data relating to tornadoes. Both Tableau and IBM Many Eyes were used as the information visualization tools to perform graphical analysis of the data. When visualizing the data, we focused on the BEGIN_AZIMUTH and the END_AZIMUTH parameters in the data, which reflect the change of tornado directions. We found that tornadoes do frequently change direction. Tornadoes that probably went straight seemed to have much less in injuries than ones, which probably changed course. We had difficulties using the visualization tools convey any aggregated information regarding directions in general because the direction was not presented as numeric values in NOAA data. During the preparation of visualizations, we found some skills to change and improve the presentation of data. improved in step with advances in technology, most prominently 1 INTRODUCTION with increasingly more sophisticated radar systems. Numerical simulations continue to improve with increased computer power, 1.1 Brief History of Tornado Research speed, and storage capabilities. Researchers continue to be fascinated by tornadoes and measure 1.2 Brief History of Research into Tornado Path and observe the paths tornadoes follow, the months they are Research occur, and regions in which are tornado prone. -
Storm Preparedness Kits –By Sparky Smith HELLO
www.mesonet.org Volume 5 — Issue 7 — July 2014 connection Storm Preparedness Kits –by Sparky Smith HELLO. MY NAME IS SPARKY SMITH, and I am an pieces everywhere, but never the complete thing. I compiled Eagle Scout. First I will talk a little bit about myself. Then I all the lists and added a few things I thought were needed will tell you about how I came up with the idea for my project, in every emergency. The best way to get your project and actually show you how to build your own. I will also talk approved is to make it relatively inexpensive with easy to about the constraints and guidelines that I had to deal with obtain materials, and a personal aspect to show that you while doing the project. actually put thought into it. I have always been interested in weather. I went to the very One of the first things I did was go to a graphics design store first Mesonet Weather Camp, where I learned more about and had a nice looking label made to put on the storm kits, what kind of disasters occur and what other dangers they so people would know it was more than a five gallon bucket. bring. A storm kit has to last at least 1 day because many Thankfully, most of the products were donated, including the other dangers can arise after storms. For instance, you label. I made 20 storm kits, which would have cost about could be trapped in your shelter by debris or even have $3000, but it ended up costing about $1000-$1200.