Guidelines for Nowcasting Techniques
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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 ....................................................................................................................... -
Fatalities Associated with the Severe Weather Conditions in the Czech Republic, 2000–2019
Nat. Hazards Earth Syst. Sci., 21, 1355–1382, 2021 https://doi.org/10.5194/nhess-21-1355-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Fatalities associated with the severe weather conditions in the Czech Republic, 2000–2019 Rudolf Brázdil1,2, Katerinaˇ Chromá2, Lukáš Dolák1,2, Jan Rehoˇ rˇ1,2, Ladislava Rezníˇ ckovᡠ1,2, Pavel Zahradnícekˇ 2,3, and Petr Dobrovolný1,2 1Institute of Geography, Masaryk University, Brno, Czech Republic 2Global Change Research Institute, Czech Academy of Sciences, Brno, Czech Republic 3Czech Hydrometeorological Institute, Brno, Czech Republic Correspondence: Rudolf Brázdil ([email protected]) Received: 12 January 2021 – Discussion started: 21 January 2021 Revised: 25 March 2021 – Accepted: 26 March 2021 – Published: 4 May 2021 Abstract. This paper presents an analysis of fatalities at- with fatal accidents as well as a decrease in their percent- tributable to weather conditions in the Czech Republic dur- age in annual numbers of fatalities. The discussion of results ing the 2000–2019 period. The database of fatalities de- includes the problems of data uncertainty, comparison of dif- ployed contains information extracted from Právo, a lead- ferent data sources, and the broader context. ing daily newspaper, and Novinky.cz, its internet equivalent, supplemented by a number of other documentary sources. The analysis is performed for floods, windstorms, convective storms, rain, snow, glaze ice, frost, heat, and fog. For each 1 Introduction of them, the associated fatalities are investigated in terms of annual frequencies, trends, annual variation, spatial distribu- Natural disasters are accompanied not only by extensive ma- tion, cause, type, place, and time as well as the sex, age, and terial damage but also by great loss of human life, facts easily behaviour of casualties. -
Shipbreaking Bulletin of Information and Analysis on Ship Demolition # 60, from April 1 to June 30, 2020
Shipbreaking Bulletin of information and analysis on ship demolition # 60, from April 1 to June 30, 2020 August 4, 2020 On the Don River (Russia), January 2019. © Nautic/Fleetphoto Maritime acts like a wizzard. Otherwise, how could a Renaissance, built in the ex Tchecoslovakia, committed to Tanzania, ambassador of the Italian and French culture, carrying carefully general cargo on the icy Russian waters, have ended up one year later, under the watch of an Ukrainian classification society, in a Turkish scrapyard to be recycled in saucepans or in containers ? Content Wanted 2 General cargo carrier 12 Car carrier 36 Another river barge on the sea bottom 4 Container ship 18 Dreger / stone carrier 39 The VLOCs' ex VLCCs Flop 5 Ro Ro 26 Offshore service vessel 40 The one that escaped scrapping 6 Heavy load carrier 27 Research vessel 42 Derelict ships (continued) 7 Oil tanker 28 The END: 44 2nd quarter 2020 overview 8 Gas carrier 30 Have your handkerchiefs ready! Ferry 10 Chemical tanker 31 Sources 55 Cruise ship 11 Bulker 32 Robin des Bois - 1 - Shipbreaking # 60 – August 2020 Despina Andrianna. © OD/MarineTraffic Received on June 29, 2020 from Hong Kong (...) Our firm, (...) provides senior secured loans to shipowners across the globe. We are writing to enquire about vessel details in your shipbreaking publication #58 available online: http://robindesbois.org/wp-content/uploads/shipbreaking58.pdf. In particular we had questions on two vessels: Despinna Adrianna (Page 41) · We understand it was renamed to ZARA and re-flagged to Comoros · According -
Snow Nowcasting Using a Real-Time Correlation of Radar Reflectivity
20 JOURNAL OF APPLIED METEOROLOGY VOLUME 42 Snow Nowcasting Using a Real-Time Correlation of Radar Re¯ectivity with Snow Gauge Accumulation ROY RASMUSSEN AND MICHAEL DIXON National Center for Atmospheric Research, Boulder, Colorado STEVE VASILOFF National Severe Storms Laboratory, Norman, Oklahoma FRANK HAGE,SHELLY KNIGHT,J.VIVEKANANDAN, AND MEI XU National Center for Atmospheric Research, Boulder, Colorado (Manuscript received 21 November 2001, in ®nal form 13 June 2002) ABSTRACT This paper describes and evaluates an algorithm for nowcasting snow water equivalent (SWE) at a point on the surface based on a real-time correlation of equivalent radar re¯ectivity (Ze) with snow gauge rate (S). It is shown from both theory and previous results that Ze±S relationships vary signi®cantly during a storm and from storm to storm, requiring a real-time correlation of Ze and S. A key element of the algorithm is taking into account snow drift and distance of the radar volume from the snow gauge. The algorithm was applied to a number of New York City snowstorms and was shown to have skill in nowcasting SWE out to at least 1 h when compared with persistence. The algorithm is currently being used in a real-time winter weather nowcasting system, called Weather Support to Deicing Decision Making (WSDDM), to improve decision making regarding the deicing of aircraft and runway clearing. The algorithm can also be used to provide a real-time Z±S relationship for Weather Surveillance Radar-1988 Doppler (WSR-88D) if a well-shielded snow gauge is available to measure real-time SWE rate and appropriate range corrections are made. -
The Lagrange Torando During Vortex2. Part Ii: Photogrammetry Analysis of the Tornado Combined with Dual-Doppler Radar Data
6.3 THE LAGRANGE TORANDO DURING VORTEX2. PART II: PHOTOGRAMMETRY ANALYSIS OF THE TORNADO COMBINED WITH DUAL-DOPPLER RADAR DATA Nolan T. Atkins*, Roger M. Wakimoto#, Anthony McGee*, Rachel Ducharme*, and Joshua Wurman+ *Lyndon State College #National Center for Atmospheric Research +Center for Severe Weather Research Lyndonville, VT 05851 Boulder, CO 80305 Boulder, CO 80305 1. INTRODUCTION studies, however, that have related the velocity and reflectivity features observed in the radar data to Over the years, mobile ground-based and air- the visual characteristics of the condensation fun- borne Doppler radars have collected high-resolu- nel, debris cloud, and attendant surface damage tion data within the hook region of supercell (e.g., Bluestein et al. 1993, 1197, 204, 2007a&b; thunderstorms (e.g., Bluestein et al. 1993, 1997, Wakimoto et al. 2003; Rasmussen and Straka 2004, 2007a&b; Wurman and Gill 2000; Alexander 2007). and Wurman 2005; Wurman et al. 2007b&c). This paper is the second in a series that pre- These studies have revealed details of the low- sents analyses of a tornado that formed near level winds in and around tornadoes along with LaGrange, WY on 5 June 2009 during the Verifica- radar reflectivity features such as weak echo holes tion on the Origins of Rotation in Tornadoes Exper- and multiple high-reflectivity rings. There are few iment (VORTEX 2). VORTEX 2 (Wurman et al. 5 June, 2009 KCYS 88D 2002 UTC 2102 UTC 2202 UTC dBZ - 0.5° 100 Chugwater 100 50 75 Chugwater 75 330° 25 Goshen Co. 25 km 300° 50 Goshen Co. 25 60° KCYS 30° 30° 50 80 270° 10 25 40 55 dBZ 70 -45 -30 -15 0 15 30 45 ms-1 Fig. -
Snow Modeling and Observations at NOAA's National Operational
Snow Modeling and Observations at NOAA’S National Operational Hydrologic Remote Sensing Center Thomas R. Carroll National Operational Hydrologic Remote Sensing Center, National Weather Service, National Oceanic and Atmospheric Administration Chanhassen, Minnesota, USA Abstract The National Oceanic and Atmospheric Administration’s (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC) routinely ingests all of the electronically available, real-time, ground-based, snow data; airborne snow water equivalent data; satellite areal extent of snow cover information; and numerical weather prediction (NWP) model forcings for the coterminous United States. The NWP model forcings are physically downscaled from their native 13 kilometer2 (km) spatial resolution to a 1 km2 resolution for the coterminous United States. The downscaled NWP forcings drive the NOHRSC Snow Model (NSM) that includes an energy-and-mass-balance snow accumulation and ablation model run at a 1 km2 spatial resolution and at a 1 hour temporal resolution for the country. The ground-based, airborne, and satellite snow observations are assimilated into the model state variables simulated by the NSM using a Newtonian nudging technique. The principle advantages of the assimilation technique are: (1) approximate balance is maintained in the NSM, (2) physical processes are easily accommodated in the model, and (3) asynoptic data are incorporated at the appropriate times. The NSM is reinitialized with the assimilated snow observations to generate a variety of snow products that combine to form NOAA’s NOHRSC National Snow Analyses (NSA). The NOHRSC NSA incorporate all of the information necessary and available to produce a “best estimate” of real-time snow cover conditions at 1 km2 spatial resolution and 1 hour temporal resolution for the country. -
Skip Talbot Photography by Jennifer Brindley
STORM SPOTTING Skip Talbot SECRETS Photography by Jennifer Brindley Ubl and others Topics • Supercell Visualization • Radar Presentation • Structure Identification • Storm Properties • Walk Through Disclaimers • Attend spotter training • Your safety is more important than spotting, photos, video, or tornado reports Supercell Visualization Lemon and Doswell 1979 Supercell Visualization Supercell Visualization Photo: Chris Gullikson Supercell Visualization Photo: Chris Gullikson Anvil Anvil Backshear Mammatus Cumulonimbus Flanking Line Cloud Base Striations Precipitation Wall Cloud Precipitation-free Base Supercell Visualization Radar Presentation Classic Hook Echo Radar Presentation Android / iOS Android Windows GrLevel3 / GrLevel2 Radar Presentation Classic Hook Echo Radar Presentation Radar Presentation Radar Presentation Radar Presentation Storm Spotting Zoo • Bear’s Cage • Whale’s Mouth • Beaver Tail • Horseshoe • Ghost Train Base (Updraft Base) (Rain Free Base or RFB) Base (Updraft Base) (Rain Free Base or RFB) Base (Updraft Base) (Rain Free Base or RFB) Base (Updraft Base) (Rain Free Base or RFB) Base (Updraft Base) (Rain Free Base or RFB) Base (Updraft Base) (Rain Free Base or RFB) Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe Horseshoe - Cyclical supercell with multiple tornadoes HorseshoeHorseshoe HorseshoeHorseshoe Horseshoe Horseshoe – Anticyclonic Funnel Horseshoe Horseshoe – Anticyclonic Funnel Horseshoe - No Wall Cloud Horseshoe - No Wall Cloud -
A Study on Snow Density Variations at Different Elevations
Brian Miretzky 1 A Study on Snow Density Variations at Different Elevations and the Related Consequences; Especially to Forecasting. Brian J. Miretzky Senior Undergrad at the University of Wisconsin- Madison ABSTRACT With observations becoming more common and forecasting becoming more accurate snow density research has increased. These new observations can be correlated with current knowledge to develop reasons for snow density variations. Currently there is still no specific consensus on what are the important characteristics in snow density variations. There are many points of general agreement such as that temperature and relative humidity are key factors. How to incorporate these into snowfall prediction is the key. Once there is more substantiated knowledge, more accurate forecasts can be made. Finally, the last in the chain of events is that society can plan accordingly to this new data. Better prediction means less work, less time, and less money spent. This study attempts to determine if elevation is an important characteristic in snow density variations. It also looks at computer models and how snow density is used in conjunction with these models to make snowfall projections. The results seem to show elevation is not an important characteristic. The results also show that model accuracy is more important than snow density variations when using models to make snowfall projections. 1. Introduction volume of the sample. Snow densities are typically on an order of 70 to 150 kg The density of snow is a m-3, but can be lower or higher in some very important topic in winter weather. cases. This is because the density of snow The fact that snow densities can helps determine characteristics of a vary makes them one of the most given snowfall. -
Mid-Latitude Dynamics and Atmospheric Rivers Session: Theory, Structure, Processes 1 Jason M
Mid-Latitude Dynamics and Atmospheric Rivers Session: Theory, Structure, Processes 1 Jason M. Cordeira Wednesday, 10 August 2016 Plymouth State University, CW3E/Scripps Contribution from: Heini Werni Peter Knippertz Harold Sodemann Andreas Stohl Francina Dominguez Huancui Hu 2016 International Atmospheric Rivers Conference 8–11 August 2016 Scripps Institution of Oceanography Objective and Outline Objective • What components of midlatitude circulation support formation and structure of atmospheric rivers? Outline • Part 1: ARs, midlatitude storm track, and cyclogenesis • Part 2: ARs, tropical moisture exports, and warm conveyor belt Objective and Outline Objective • What components of midlatitude circulation support formation and structure of atmospheric rivers? Outline • Part 1: ARs, midlatitude storm track, and cyclogenesis • Part 2: ARs, tropical moisture exports, and warm conveyor belt Mimic TPW (SSEC/Wisconsin) • Global water vapor distribution is concentrated at lower latitudes owing to warmer temperatures • Observations illustrate poleward extrusions of water vapor along “tropospheric rivers” or “atmospheric rivers” Zhu and Newell (MWR-1998) • >90% of meridional water vapor transports occurs along ARs • ARs part of midlatitude cyclones and move with storm track Climatology of Water Vapor Transport Global mean IVT 150 kg m−1 s−1 • ECMWF ERA Interim Reanalysis • Oct–Mar 99/00 to 08/09 (i.e., ten winters) • IVT calculated for isobaric layers between 1000 and 100 hPa Tropical–Extratropical Interactions Waugh and Fanutso (2003-JAS) Knippertz -
Downbursts: As Dangerous As Tornadoes? Winds Can Be Experienced Along the Leading Edge of This “Spreading Out” Air
DDoowwnnbbuurrssttss:: AAss DDaannggeerroouuss aass TToorrnnaaddooeess?? National Weather Service Greenville-Spartanburg, SC What is a Downburst? “It had to be a tornado!” This is a “updraft.” common statement made by citizens of the Carolinas and On a typical day in the warm season, North Georgia who experience once a cloud grows to 20,000 to damaging winds associated with 30,000 feet, it will begin to produce severe thunderstorms, especially heavy rain and lightning. The falling if those winds cause damage to rain causes a “downdraft,” or sinking their homes. However, the column of air to form. A thunderstorm combination of atmospheric may eventually grow to a height of ingredients that are necessary for 50,000 feet or more before it stops tornadoes occurs only rarely developing. Generally speaking, the across our area. In fact the 46 Fig. 1. Tracks of tornadoes across the “taller” the storm, the more likely it is Carolinas and Georgia from 1995 through counties that represent the to produce a strong downdraft. Once 2011. Compare this with the downburst Greenville-Spartanburg Weather reports during this time (Figure 3). the air within the downdraft reaches Forecast Offices’s County Warning the surface, it spreads out parallel to Area only experience a total of 12 the ground. Very strong to damaging to 15 tornadoes during an average year. However, thunderstorms and even severe thunderstorms are a relatively common occurrence across our area, especially from late spring through mid-summer. This is because a warm and humid (i.e., unstable) atmosphere is required for thunderstorm development. If some atmospheric process forces the unstable air Fig. -
(Highresmip V1.0) for CMIP6
High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6 1 2 3 5 Reindert J. Haarsma , Malcolm Roberts , Pier Luigi Vidale , Catherine A. Senior2, Alessio Bellucci4, Qing Bao5, Ping Chang6, Susanna Corti7, Neven S. Fučkar8, Virginie Guemas8, Jost von Hardenberg7, Wilco Hazeleger1,9,10, Chihiro Kodama11, Torben Koenigk12, Lai-Yung Ruby Leung13, Jian Lu13, Jing-Jia Luo14, Jiafu Mao15, Matthew S. Mizielinski2, Ryo Mizuta16, Paulo Nobre17, Masaki 18 4 19 20 10 Satoh , Enrico Scoccimarro , Tido Semmler , Justin Small , Jin-Song von Storch21 1Royal Netherlands Meteorological Institute, De Bilt, The Netherlands 2Met Office Hadley Centre, Exeter, UK 15 3University of Reading, Reading, UK 4 Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy 5LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China P.R. 6Texas A&M University, College Station, Texas, USA 7National Research Council – Institute of Atmospheric Sciences and Climate, Italy 20 8Barcelona Super Computer Center, Earth Sciences Department, Barcelona, Spain 9Netherlands eScience Center, Amsterdam, The Netherlands 10Wageningen University, The Netherlands 11Japan Agency for Marine-Earth Science and Technology, Japan 12Swedish Meteorological and Hydrological Institute, Norrköping, Sweden 25 13Pacific Northwest National Laboratory, Richland, USA 14Bureau of Meteorology, Australia 15Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA 16Meteorological Research Institute, -
Trident Technical College Transparency Report February
Trident Technical College Transparency Report February 2020 Identification # Check Date Payee Category Object Department Source of Funds Total 03*0460665 02/05/20 AAA Business Travel Contractual Services Other Contractual Services Accreditations Unrestricted Funds $ 160.00 03*0460665 02/05/20 AAA Business Travel Travel - Out of State Out-Of-State - Air Transp. Achieving The Dream Unrestricted Funds $ 364.10 03*0460666 02/05/20 Adams Outdoor Advertising S.C. Contractual Services Prtg.Bndg.Adv.-Commercial Marketing Services Unrestricted Funds $ 825.00 03*0460667 02/05/20 Adorama Supplies & Materials Education Supplies Foundation Mini Grants Unrestricted Funds $ 4,398.70 03*0460668 02/05/20 Aircraft Technical Publishers Contractual Services Data Processing Serv.-Other IT Software Unrestricted Funds $ 4,872.00 03*0460669 02/05/20 Alternative Staffing Contractual Services Temporary Services Bookstore - Operating Overhead Unrestricted Funds $ 6,306.72 03*0460670 02/05/20 Apple Computer, Inc. Supplies & Materials Data Processing Supplies Foundation Mini Grants Unrestricted Funds $ 4,490.80 03*0460671 02/05/20 Atlantic Electric LLC Contractual Services Other Contractual Services Plant Oper & Maint-M Unrestricted Funds $ 5,605.79 03*0460672 02/05/20 Berkeley County Water & Sanitation Authority Contractual Services Utilities Plant Oper & Maint-B Unrestricted Funds $ 221.71 03*0460673 02/05/20 Berkeley Propane Company Contractual Services Utilities Plant Oper & Maint-M Unrestricted Funds $ 235.48 03*0460674 02/05/20 Berlin's Restaurant Supply, Inc.