DOCSLIB.ORG

Operational Observations of Extreme Reflectivity Values in Convective Cells

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Operational Observations of Extreme Reflectivity Values in Convective Cells OPERATIONAL OBSERVATIONS OF EXTREME REFLECTIVITY VALUES IN CONVECTIVE CELLS Alan E. Gerard NOAA/National Weather Service Forecast Office Jackson, Mississippi Abstract WRIST technique was designed to find was the height of the Digital Video Integrated Processor (DNIP) level 5. The observation of high reflectivity values aloft as an Lemon (1980) found that the presence ofDNIP level 5 or indicator of the potential for a convective storm to produce higher above 27 Kft had a strong correlation with a thun­ severe weather has been widely known for many years. derstorm cell's capability to produce severe weather The advent of the Weather Surveillance Radar-88 Doppler (winds of 50 kt or greater, hail 0.75 in. or larger in diam­ (WSR-88D) has given operational forecasters a new tool eter, or tornadoes). This was because the presence of high for looking at reflectivity data, and a greater spectrum of DNIP levels aloft was an indication of the presence of a values at which to look. In the use of these new data, anec­ strong updraft in the storm, and the strength of the dotal evidence has suggested to some radar operators that updraft has a direct correlation to the ability of a thun­ extremely high reflectivity values (65 dBZ or greater) derstorm to produce severe weather. appear to have a correlation with the production of severe Although the WSR-88D allows for relatively easy weather. Data from the Cleveland, Ohio and Jackson, examination of many meteorological parameters to deter­ Mississippi WSR-88D radars were examined to observe mine a storm's severity, the height of high reflectivities in the formation of extremely high values of reflectivity in a particular storm cell continues to be an important fac­ convective cells, and to determine any correlation with the tor in the warning decision, especially when warning for production of severe weather. The data showed that a pulse-type convection. DNIP level 5 on conventional large majority of extreme reflectivity storms contained the radar is equivalent to reflectivity values of 51 to 57 dBZ extreme values above the freezing level, and that such (Burgess and Lemon 1990). The height of reflectivity in storms had a high correlation with the production of excess of 50 dBZ can be examined on the WSR-88D using severe weather. Conversely, the small percentage of storms several different products, including displays of base which contained extreme reflectivity values only below the reflectivity at various elevation angles, reflectivity cross freezing level were infrequently associated with severe section (RCS) products, the Weak Echo Region (WER) weather reports. Further analysis showed some relation­ product, and the layer composite reflectivity maximum ship between the presence of extreme reflectivity and high (LRM) product. The Vertically Integrated Liquid (VIL; all Vertically Integrated Liquid (VIL) values with severe references to VIL are grid-based VIL) product also gives weather production, although this relationship appeared the radar operator a rough analysis at which storms like­ much less effective operationally than the association ly have high reflectivity values aloft. between severe weather and extreme reflectivity values In addition to the ability of the WSR-88D to show above the freezing level. reflectivity data in many different formats, the WSR-88D also displays base reflectivity data on a much finer inten­ 1. Introduction sity scale, with 16 data levels versus the 6 DNIP levels on the conventional radars. The advantage that the WSR- The use of radar reflectivity data in analyzing severe 88D gives to the radar operator with respect to the better convective storms has been widely known for decades scale resolution is quite apparent at high reflectivity val­ (e.g., Sadkowski and Hamilton 1959, Hamilton 1966, ues. For example, the DNIP level 6 only indicated to the Staff ofNSSL 1966). For many years prior to the advent operator of a conventional radar the presence of greater of the Weather Surveillance Radar-88 Doppler (WSR- than 57 dBZ reflectivity (Burgess and Lemon 1990), 880), the primary radar technique for detecting severe while the WSR-88D currently displays data levels for 55 local convective storms using conventional radar (e.g., dBZ to 59 dBZ, 60 dBZ to 64 dBZ, 65 dBZ to 69 dBZ, 70 Weather Surveillance Radar-57 S-band) was the dBZ to 74 dBZ, and 75 dBZ or higher. Weather Radar Identification of Severe Thunderstorms Since the implementation of the WSR-88D network (WRIST) technique. The WRIST technique was taught and the associated availability of the finer scaled high by the NOAAlNational Weather Service Training reflectivity data, anecdotal evidence from radar operators Center, after work done by Lemon (1980), as the most at some NWS NEXRAD Weather Service Forecast Offices time efficient manner in which to examine the structure (NWSFO) and NEXRAD Weather Service Offices of a convective cell. (NWSO) has suggested that severe weather seems to be One of the critical components of a storm cell the quite common when extreme reflectivity values (defined 3 4 National Weather Digest here as 65 dBZ or greater) are detected in a convective cell by the WSR-88D. This study was undertaken to Table 1. Days on which extreme reflectivity was observed, observe the formation of extreme reflectivity cores in con­ showing height of the freezing level in feet, type of severe vective cells, and to determine what correlation, if any, weather reported, and predominant storm type based on subjective sounding analysis. Days from 1994 are for KCLE exists between the presence of extreme reflectivity and radar data and days from 1996 are for KJAN radar data. the production of severe weather. Freezing Severe Storm 2. Methodology Date Level (FT) Weather Type The data for this study were taken from two WSR-88D 4/26/94 10600 HAIL MULTICELL radars located in markedly different regions of the coun­ 6/14/94 14400 NONE PULSE try, in an attempt to draw conclusions that might be valid 6/15/94 13100 WIND PULSE over a large area of the country. The first data set used 6/16/94 12700 HAIL, WIND PULSE was all Archive Level IV data (products archived from the 6/18/94 14500 HAIL, WIND PULSE local Principal User Processor (PUP» available from the 6/19/94 14100 HAIL, WIND PULSE Cleveland, Ohio, (KCLE) WSR-88D for convective events 6/20/94 13700 WIND PULSE in 1994. This consisted of 20 convective events from 12 6/21/94 14100 WIND MULTICELL April to 1 November 1994. The second data set was from 6/29/94 12900 HAIL, WIND, SUPERCELL available Archive Level IV data from the Jackson, TORNADO Mississippi, (KJAN) WSR-88D, for convective events dur­ 7/07/94 14200 WIND PULSE ing the 1996 season. This consisted of 19 convective 7/22194 13150 WIND MULTICELL events from 19 February to 16 September 1996. 9/25/94 9200 HAIL, WIND SUPERCELL For this study, any cell for which the WSR-88D detect­ ed at least 65 dBZ reflectivity was considered to have 3/18/96 12150 HAIL, WIND, SUPERCELL extreme reflectivity values and was examined. The cell TORNADO was maintained as a single storm as long as it showed a 3/31/96 12000 HAIL, WIND, SUPERCELL discrete identity to the radar observer, and no more than TORNADO 30 minutes passed between the volume scans during 4/22196 13400 HAIL SUPERCELL which it contained extreme reflectivity values (i.e., the 5/28/96 14200 HAIL, WIND MULTICELL storm would be considered a new storm if more than 30 6/12196 13100 WIND PULSE minutes passed between occurrences of extreme reflec­ 6/20/96 14600 WIND PULSE tivity). For the KCLE WSR-88D, only storms detected 7/08/96 14800 HAIL PULSE within the range ofthe 0.54 nm resolution base reflectiv­ ity products (124 nm), and located within the state of Ohio, were used in the study. For the KJAN radar, storms during normal waking hours. This prerequisite for a located within the office's county warning area (CWA) storm to be considered in the database as non-severe is were used (a range of approximately 90 nm from the similar to that used by Amburn and Wolf (1997). A storm KJAN radar). Of the 39 convective events in the KCLE was considered severe if it was clearly associated with a and KJAN areas which were examined for this study, report of severe weather within one hour after containing there were 19 events (12 from the KCLE radar and extreme reflectivity for the first time. 7 from the KJAN radar) during which extreme reflectiv­ ity was observed (Table 1). 3. Maximum Height of the Extreme Reflectivity As has been discussed in previous research (e.g., Hales and Kelly 1985), one of the main problems in conducting Starting with data from the KCLE radar, 45 storms a study with regard to production of severe weather is were found which contained extreme reflectivity during reliable verification reports. This is especially true with the convective events shown in Table 1. Based on the cri­ regard to a study, such as this, being conducted in an teria outlined above, 41 of these storms were able to be operational environment, as the source of verifYing data classified as severe or non-severe. Of these storms, 34 is highly dependent upon population density and time of (82.9%) were severe. From the KJAN radar, 25 storms day (Wyatt and Witt 1997). Hence, tIns study will not were found that contained extreme reflectivity, 23 of focus on actual verification statistics or the determina­ which were able to be classified as severe or non-severe. tion of a criteria to distinguish between severe and non­ Twenty-one ofthese 23 storms (91.3%) were severe based severe convection.
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 .......................................................................................................................
    [Show full text]
  • 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.
    [Show full text]
  • Severe Weather in the United States
    Module 18: Severe Thunderstorms in the United States Tuscaloosa, Alabama May 18, 2011 Dan Koopman 2011 What is “Severe Weather”? Any meteorological condition that has potential to cause damage, serious social disruption, or loss of human life. American Meteorological Society: In general, any destructive storm, but usually applied to severe local storms in particular, that is, intense thunderstorms, hailstorms, and tornadoes. Three Stages of Thunderstorm Development Stage 1: Cumulus • A warm parcel of air begins ascending vertically into the atmosphere. • In an unstable environment, the parcel will continue to rise as long as it is warmer than the air around it. • Billowing, puffy Cumulus Congestus Clouds continue to build in towers. Stage 2: Mature • At a certain altitude, the parcel is no longer warmer than its environment. It is unable to rise any further and develops the distinctive “Anvil” shape as seen in this figure. • In cases of particularly strong updrafts, the vertical velocity of the updraft may be sufficient to penetrate this altitude causing an “overshooting top” to develop above the flat top of the anvil. Stage 3: Dissipating • Convective inflow shut off by strong downdrafts, no more cloud droplet formation, downdrafts continue and gradually weaken until light precipitation rains out • Water droplets aloft being to coalesce until they are too heavy to support and begin to fall to the surface, strengthening the downdraft. Basic Ingredients for Thunderstorms • Moisture • Unstable Air • Forcing Mechanism Moisture Thunderstorms development generally requires dewpoints > 50°F Unstable Air Stable is resistant to change. When the air is stable, even if a parcel of air is lifted above its original position (say by a mountain for example), its temperature will always remain cooler than its environment, meaning that it will resist upwards movement.
    [Show full text]
  • 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,
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Bunkers, M. J., and C. A. Doswell III, 2016
    OCTOBER 2016 C O R R E S P O N D E N C E 1715 CORRESPONDENCE Comments on ‘‘Double Impact: When Both Tornadoes and Flash Floods Threaten the Same Place at the Same Time’’ MATTHEW J. BUNKERS NOAA/National Weather Service, Rapid City, South Dakota CHARLES A. DOSWELL III Doswell Scientific Consulting, Norman, Oklahoma (Manuscript received 14 June 2016, in final form 1 September 2016) 1. Introduction However, this is not necessarily true, especially with respect to precipitation efficiency (e.g., Davis 2001). Nielsen et al. (2015) studied the environments of Although these items are minor, we believe that in total concurrent, collocated tornado and flash flood (hereafter they are sufficient to be addressed formally. Therefore, our TORFF) events that occurred across the continental goals in this comment are to 1) discuss the importance of United States from 2008 to 2013. They found that storm (and supercell) motion in potentially helping to an- TORFF events are difficult to distinguish from tornado ticipate TORFF events; 2) provide examples contrary to the events that do not produce flash flooding, although the notion that tornadic storms are necessarily fast moving, as TORFF environments tended to possess greater moisture well as discuss briefly the characteristics of slow-moving and synoptic-scale forcing for ascent. tornadic supercell environments; and 3) clarify the re- Although we agree with most of the content of the paper lationship of CAPE with rainfall and precipitation efficiency. by Nielsen et al. (2015), one thing that they did not address was storm motion as it relates to these TORFF events; neither the mean wind nor any other proxy for storm or 2.
    [Show full text]
  • Weather Spotter's Field Guide
    Weather Spotter’s Field Guide © Roger Edwards A Guide to Being a SKYWARN® Spotter U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration National Weather Service Ⓡ June 2011 The Spotter’s Role Section 1 The SKYWARN® Spotter and the Spotter’s Role The United States is the most severe weather- prone country in the world. Each year, people in this country cope with an average of 10,000 thunderstorms, 5,000 floods, 1,200 tornadoes, and two landfalling hurricanes. Approximately 90% of all presidentially Ⓡ declared disasters are weather-related, causing around 500 deaths each year and nearly $14 billion in damage. SKYWARN® is a National Weather Service (NWS) program developed in the 1960s that consists of trained weather spotters who provide reports of severe and hazardous weather to help meteorologists make life-saving warning decisions. Spotters are concerned citizens, amateur radio operators, truck drivers, mariners, airplane pilots, emergency management personnel, and public safety officials who volunteer their time and energy to report on hazardous weather impacting their community. Although, NWS has access to data from Doppler radar, satellite, and surface weather stations, technology cannot detect every instance of hazardous weather. Spotters help fill in the gaps by reporting hail, wind damage, flooding, heavy snow, tornadoes and waterspouts. Radar is an excellent tool, but it is just that: one tool among many that NWS uses. We need spotters to report how storms and other hydrometeorological phenomena are impacting their area. SKYWARN® spotter reports provide vital “ground truth” to the NWS. They act as our eyes and ears in the field.
    [Show full text]
  • A Rare Supercell Outbreak in Western Nevada: 21 July 2008
    National Weather Association, Electronic Journal of Operational Meteorology, 2009-EJ6 A Rare Supercell Outbreak in Western Nevada: 21 July 2008 CHRIS SMALLCOMB National Weather Service, Weather Forecast Office, Reno, Nevada (Manuscript received 30 June 2009, in final form 29 September 2009) ABSTRACT A rare outbreak of organized severe thunderstorms took place in western Nevada on 21 July 2008. This paper will examine the environment antecedent to this event, including unusual values of shear, moisture, and instability. An analysis of key radar features will follow focusing primarily on WSR-88D base data analysis. The article demonstrates several locally developed parameters and radar procedures for identifying severe convection, along with addressing challenges encountered in warning operations on this day. 1. Introduction – Event Synopsis A rare outbreak of organized severe thunderstorms took place in the National Weather Service (NWS) Reno forecast area (FA) on 21 July 2008. Based on forecaster experience, this has been subjectively classified as a “one in ten” year event for the western Great Basin (WGB), which being located in the immediate lee of the Sierra Nevada Mountains, is unaccustomed to widespread severe weather. Unorganized pulse type convection is a much more frequent occurrence in this portion of the country. On 21 July 2008, numerous instances of large hail and heavy rainfall were associated with severe thunderstorms (Fig. 1). The maximum hail size reported was half-dollar (3.2 cm, 1.25”), however there is indirect evidence even larger hail occurred. Several credible reports of funnel clouds were received from spotters, though a damage survey the following day found no evidence of tornado touchdowns.
    [Show full text]
  • Chapter 3.3 Risk Assessment: Severe Storms
    CHAPTER 3.3 RISK ASSESSMENT: SEVERE STORMS 3.3. Risk Assessment: Severe Storms Description A severe storm is an atmospheric disturbance that results in one or more of the following phenomena: strong winds and large hail, thunderstorms, tornadoes, rain, snow, or other mixed precipitation. Of the 47 Presidential Disaster declarations in Idaho since 1954, 10 have been attributed to include “storms” or “severe” storms. Several damaging elements of severe storms are detailed as their own hazard in Chapter 3.2 (flooding to include dam/levee/canal failure). The sub-sections in this chapter include details for winter storms, lightning, hail, straight-line winds, and tornadoes. Winter Storms Winter storms range widely in size, duration, and intensity. These storms may impact a single community or a multi-State area. They may last hours or days. The severity of storms can range from a small amount of dry snow to a large, blanketed area of wet snow and ice. Generally, winter storms are characterized by low temperatures and blowing snow. A severe winter storm is defined as one that drops 4 or more inches of snow during a 12-hour period, or 6 or more inches during a 24-hour span. A blizzard is a winter storm with winds exceeding 35 miles per hour accompanied by snow or blowing snow and reduced visibility. Strong winds can lower the effective temperature through “wind chill.” An ice storm occurs when damaging accumulations of ice are expected during freezing rain situations, and when cold rain freezes immediately on contact with the ground, structures, and vegetation.
    [Show full text]
  • The Storm Cell Identification and Tracking Algorithm: an Enhanced
    JUNE 1998 JOHNSON ET AL. 263 The Storm Cell Identi®cation and Tracking Algorithm: An Enhanced WSR-88D Algorithm J. T. JOHNSON,* PAMELA L. MACKEEN,*1 ARTHUR WITT,* E. DEWAYNE MITCHELL,*1 GREGORY J. STUMPF,*1 MICHAEL D. EILTS,* AND KEVIN W. T HOMAS*1 * NOAA, Environmental Research Laboratories, National Severe Storms Laboratory, Norman, Oklahoma 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma (Manuscript received 28 February 1997, in ®nal form 9 December 1997) ABSTRACT Accurate storm identi®cation and tracking are basic and essential parts of radar and severe weather warning operations in today's operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identi®cation and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identi®ed 68% of all cells with maximum re¯ectivities over 40 dBZ and 96% of all cells with maximum re¯ectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identi®cation performance, the potential exists for better and more accurate tracking infor- mation. The SCIT algorithm tracked greater than 90% of all storm cells correctly. The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997.
    [Show full text]