DOCSLIB.ORG

Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development VOLUME 34 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY MARCH 2017 Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development a,b c a LINDSEY M. RICHARDSON, JEFFREY G. CUNNINGHAM, W. DAVID ZITTEL, a a,c d,e ROBERT R. LEE, RICHARD L. ICE, VALERY M. MELNIKOV, f,g f,h NICOLE P. HOBAN, AND JOSHUA G. GEBAUER a Radar Operations Center, National Weather Service, Norman, Oklahoma b Centuria Corporation, Norman, Oklahoma c 557th Weather Wing, Offutt Air Force Base, Nebraska d Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma e National Severe Storms Laboratory, Norman, Oklahoma f Research Experiences for Undergraduates, National Weather Center, Norman, Oklahoma g North Carolina State University, Raleigh, North Carolina h University of Oklahoma, Norman, Oklahoma (Manuscript received 25 January 2016, in final form 6 December 2016) ABSTRACT Studies have shown that echo returns from clear-air Bragg scatter (CABS) can be used to detect the height of the convective boundary layer and to assess the systematic differential reflectivity (ZDR) bias for a radar site. However, these studies did not use data from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) or data from a large variety of sites. A new algorithm to automatically detect CABS from any operational WSR-88D with dual-polarization capability while excluding contamination from precipitation, biota, and ground clutter is presented here. Visual confirmation and tests related to the sounding parameters’ relative humidity slope, refractivity gradient, and gradient Richardson number are used to assess the algo- rithm. Results show that automated detection of CABS in operational WSR-88D data gives useful ZDR bias information while omitting the majority of contaminated cases. Such an algorithm holds potential for radar calibration efforts and Bragg scatter studies in general. 1. Introduction Bragg scatter is caused by turbulent inhomogeneities with sizes around one-half of a transmitted radar wave- a. Bragg scatter background length (e.g., Cowley 1995; Hardy and Katz 1969; Knight Since the completion of the dual-polarization upgrade and Miller 1993; Doviak and Zrnic´ 2006, chapter 11). to the Weather Surveillance Radar-1988 Doppler (WSR- Bragg scatter has been observed as a layer in clear air and 88D), users have been able to make more detailed developing clouds, and it is mostly associated with re- assessments of the atmosphere. One phenomenon of in- fractivity gradients (Atlas 1959; Ottersten 1969). Studies terest is clear-air Bragg scatter (CABS). Specifically, it has have found that refractivity gradients are related to gra- been shown that CABS can be used to assist with radar dients of moisture in dynamically unstable regions calibration and detection of the height of the convective (Ottersten 1969; Hardy and Katz 1969; Hardy and boundary layer (Melnikov et al. 2011, 2013a; Cunningham Ottersten 1969). In maritime environments, moisture et al. 2013; Zittel et al. 2014). This paper describes the proved to be a more important factor than temperature, development and testing of an automated algorithm for and the primary generation mechanisms consisted of detecting CABS on operational WSR-88Ds. turbulent mixing and detrainment/entrainment of cloudy air (Knight and Miller 1993, 1998; Cohn 1994; Gage et al. 1999; Davison et al. 2013a,b). Corresponding author e-mail: Lindsey M. Richardson, lindsey.m. Melnikov et al. (2011, 2013a) used Bragg scatter to [email protected] detect the height of the turbulent convective boundary DOI: 10.1175/JTECH-D-16-0030.1 For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/ PUBSReuseLicenses). 465 466 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 34 layer in a continental environment. Information about (e.g., Melnikov et al. 2011)wereusedtoformulatea the dual-polarization field characteristics allowed them specific setup for capturing CABS data. Reflectivity (Z), to easily distinguish biota and precipitation from CABS. velocity (V), spectrum width (W), correlation coefficient Their results showed that Bragg scatter corresponds (rHV or CC), and signal-to-noise ratio (SNR) are used to with strong vertical gradients of humidity. Both Davison identify a potential CABS layer and filter out contami- et al. and Melnikov et al. suggest that soundings could be nants (described further in sections 2d and 2e). These used to indicate layers conducive to producing Bragg fields are collectively referred to as base data.3 scattering (discussed in section 3b). There are six main steps for identifying a potential CABS layer and estimating the Z bias from the re- b. Practical application of Bragg scatter DR turns on a WSR-88D: The turbulent eddies associated with CABS are ran- 1) Find radar data within certain spatial limits over a domly oriented and thus have an intrinsic differential certain time domain. reflectivity (Z ) near 0 dB on an unbiased system DR 2) Create a Z histogram of data within the spatial and (Melnikov et al. 2011, 2013a). External targets with known temporal limits. intrinsic Z values,suchasCABS,canbeusedtoin- DR 3) Create a separate Z histogram of range gates that dependently verify the systematic bias of Z (Z bias) DR DR DR pass base data filters using the same spatial limits of a radar site. Thus, CABS without contamination can applied over the specified time domain. be a potential estimator of the Z bias. The Z bias DR DR 4) Use statistical filters to assess Z histogram data for within the WSR-88D can be introduced via an engineering- DR statistical validity and potential contamination from derived internal parameter known as ZDR .This Offset non-Bragg sources. ZDR is applied automatically to the measured Z Offset DR 5) Use a precipitation filter to further reduce the likeli- field (Cunningham et al. 2013; Melnikov et al. 2013b). If the hood of contamination. ZDR fails to capture correctly some aspect of hard- Offset 6) Calculate the mode of the Z histogram if it passes ware bias, the result is a bias in Z . Thus, an error in DR DR all previous filters. ZDROffset translates to a ZDR bias. CABS returns with ZDR estimates not near 0 dB reflect a bias in the radar. For this study, specific range limits, elevation angles, and Results from initial development and testing of an volume coverage patterns (VCPs) mitigate clutter and automated algorithm to collect ZDR bias estimates from some precipitation contamination (described further in CABS in radar data are presented here. Datasets in this section 2b). Values of Z from passing range gates within study span from October 2013 through September 2014 the spatial limits are collected into a histogram over a from over 130 WSR-88D radar sites across the United certain time window for further statistical testing. We used States.1 Archived operational Level II2 data were the 1700–1900 UTC time window for our initial testing, processed offline in a MATLAB environment from though CABS is not limited to this time domain (section sites across the contiguous United States (CONUS) 2c). A ZDR histogram is created using the same VCP, and outside-CONUS (OCONUS) sites, such as Alaska, spatial, and temporal limits, but only range gates that pass Hawaii, and Puerto Rico. Section 2 describes an algorithm thebasedatafilters(section 2d) are included in the dis- to detect CABS with the operational dual-polarization tribution. A test for the sample size and spread of the ZDR WSR-88Ds with a focus on ZDR calibration aspects. histogram is used to check the statistical validity of the Section 3 covers results from visual confirmation and distribution and potential contamination (section 2e). compares the algorithm output to sounding tests fol- Next, the separate Z histogram for precipitation contam- lowed by a summary and a discussion in section 4. ination (section 2f) is checked. Finally, once these filters arepassed,themodefromtheZDR histogram is calculated as an assessment of Z bias from CABS-like returns. 2. Bragg scatter detection algorithm DR b. Volume coverage pattern and range limits a. Algorithm overview CABS layers are expected to lie at the top of a Preliminary visual confirmation and knowledge of boundary layer (convective or marine) a majority of the the dual-polarization characteristics of Bragg scatter 1 3 Radar data were not always available every day for each radar The differential phase (FDP) is also a dual-polarization base site due to radar downtime/data feed errors. variable, but the FDP characteristics of CABS is similar to the 2 See Crum et al. (1993) for the distinction between the various characteristics of light precipitation. Thus, we opted to not use FDP levels of radar data available. for this study. MARCH 2017 R I C H A R D S O N E T A L . 467 time. Several studies show that the convective at 2.58. Additionally, many sites use VCP 31 exclu- boundary layer is generally no higher than 3 km sively in light precipitation (especially snow) cases. (;10 000 ft) above ground level (AGL; Kaimal et al. c. Time considerations 1982; vanZanten et al. 1999; Stensrud 2007, chapter 5; Heinselman et al. 2009). To cover the heights where For initial widespread testing, a time window of 1700– CABS has been observed, a range limit of 10 # R # 1900 UTC was chosen to analyze the October 2013– 80 km was selected. Given typical WSR-88D scanning September 2014 data. This time frame corresponds well angles, CABS is less likely to be detected beyond with heating and convective boundary layer mixing (in 80 km (;43 n mi) and still fill the radar beam4 in op- the central plains of the United States). However, it erational WSR-88D volume scans. Additionally, data could be too early for some western areas and too late within 10 km (;5 n mi) are susceptible to ground for some eastern areas.
Load more

Recommended publications
  • Bragg Scatter Detection by the WSR-88D
    MARCH 2017 R I C H A R D S O N E T A L . 479 Bragg Scatter Detection by the WSR-88D. Part II: Assessment of ZDR Bias Estimation LINDSEY M. RICHARDSON Radar Operations Center, National Weather Service, and Centuria Corporation, Norman, Oklahoma W. DAVID ZITTEL AND ROBERT R. LEE Radar Operations Center, National Weather Service, Norman, Oklahoma VALERY M. MELNIKOV Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma RICHARD L. ICE Radar Operations Center, National Weather Service, Norman, Oklahoma, and 557th Weather Wing, Offutt Air Force Base, Nebraska JEFFREY G. CUNNINGHAM 557th Weather Wing, Offutt Air Force Base, Nebraska (Manuscript received 25 January 2016, in final form 6 December 2016) ABSTRACT Clear-air Bragg scatter (CABS) is a refractivity gradient return generated by turbulent eddies that oper- ational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems can detect. The randomly oriented nature of the eddies results in a differential reflectivity (ZDR) value near 0 dB, and thus CABS can be used as an assessment of ZDR calibration in the absence of excessive contamination from precipitation or biota. An automated algorithm to estimate ZDR bias from CABS was developed by the Radar Operations Center and can be used to assess the calibration quality of the dual-polarized WSR-88D fleet. This technique supplements existing ZDR bias assessment tools, especially the use of other external targets, such as light rain and dry snow. The estimates of ZDR bias from CABS using a 1700–1900 UTC time window were compared to estimates from the light rain and dry snow methods.
    [Show full text]
  • 6 Improving and Exploiting Polarimetric Weather Radar Data – Plans and Status Richard L. Ice and J. G. Cunningham U.S. Air
    6 Improving and Exploiting Polarimetric Weather Radar Data – Plans and Status Richard L. Ice and J. G. Cunningham U.S. Air Force, Air Weather Agency, Operating Location K, Norman, Oklahoma J. N. Chrisman WSR-88D Radar Operations Center, Norman, Oklahoma 1. INTRODUCTION The program also established a significant infrastructure for capturing, processing, and This paper will present recent and planned archiving the digital output of the radar receiver improvements to the Weather Surveillance Radar through time series recording. 1988 Doppler (WSR-88D), addressing near term operational improvements as well as future signal This capability has been the key to the rapidly processing enhancements. It describes practical increasing pace of signal processing ideas, many proven very recently, that have improvements, and also formed the basis of all potential for enhancing the foundational data engineering evaluations aimed at ensuring new from the WSR-88D Doppler Weather Radar. It is signal processing features meet or exceed forward looking, and intended to aid program system requirements. The infrastructure stakeholders as they sustain and improve resulting from the data quality program was operations for this critical national weather asset. instrumental in the evaluation and resulting It follows the spirit of earlier visionary work that approval of the recently completed polarimetric has made the radar a success (Elvander, 2001). upgrade. This potential technology survey and operational There have been many surveys regarding the status update will first review a range of possible future of weather radar that addressed signal technologies and will then present the status of processing improvements (Fabry, 2003, Keeler, the near term software updates that the WSR- 1990, National Academy of Sciences, 2004, 88D Radar Operations Center (ROC) has been Snow, 2003, Zrnic, 2003).
    [Show full text]
  • PROJECT CRAFT a Real-Time Delivery System for NEXRAD Level II Data Via the Internet
    PROJECT CRAFT A Real-Time Delivery System for NEXRAD Level II Data Via the Internet BY KEVIN E. KELLEHER, KELVIN K. DROEGEMEIER, JASON J. LEVIT, CARL SINCLAIR, DAVID E. JAHN, SCOTT D. HILL, LORA MUELLER, GRANT QUALLEY, TIM D. CRUM, STEVEN D. SMITH, STEPHEN A. DEL GRECO, S. LAKSHMIVARAHAN, LINDA MILLER, MOHAN RAMAMURTHY, BEN DOMENICO, AND DAVID W. FULKER A multi-institution collaboration demonstrated real time compression and Internet-based transmission technology to make possible an affordable nationwide operational capture, distribution, and archiving of Level II WSR-88D data. he National Oceanic and Atmospheric Admin- severe weather research throughout the country. The istration’s (NOAA’s) National Weather Service Center for Analysis and Prediction of Storms (CAPS) T (NWS) had an underutilized national resource located at the University of Oklahoma (OU) had a in the real-time high-resolution level II radar data. need to use the data to initialize a high-resolution NOAA’s National Severe Storms Laboratory (NSSL) cloud model. NOAA’s National Climatic Data Center had developed a method to access the data in real time, (NCDC) needed a method to improve its data capture but the challenge was finding an affordable way to rate of the level II data for archival purposes. Baron’s disseminate the data given the limitations of the net- Services, a company specializing in radar data for work bandwidth. NSSL needed the data to extend its the commercial television market, needed the data AFFILIATIONS: KELLEHER—NOAA/National Severe Storms Labo- #CURRENT
    [Show full text]
  • Low Elevation Scanning Environmental Assessment For
    Environmental Assessment – Relocation of the KLIX WSR-88D SENSOR ENVIRONMENTAL LLC www.sensorenvirollc.com Draft Environmental Assessment Report • July 2020 ENVIRONMENTAL ASSESSMENT (EA) RELOCATION OF THE WEATHER SURVEILLANCE RADAR - MODEL 1988, DOPPLER (WSR-88D) SERVING NEW ORLEANS / BATON ROUGE, LOUISIANA, AREA Prepared by James Manitakos, Project Manager Sensor Environmental LLC 296 West Arbor Avenue Sunnyvale, CA 94085 Andre Tarpinian, Radio Frequency Engineer Alion Science and Technology 306 Sentinel Drive Suite 300 Annapolis-Junction, MD 20701 Prepared for: WSR-88D Radar Operations Center Norman, Oklahoma Environmental Assessment - Relocation of the KLIX WSR-88D This page intentionally left blank. Environmental Assessment - Relocation of the KLIX WSR-88D EXECUTIVE SUMMARY The National Weather Service (NWS) owns and operates the existing Weather Surveillance Radar, Model 1988 Doppler (WSR-88D) serving the New Orleans/Baton Rouge, LA area. The International Civil Aviation Organization designator for the radar is KLIX and the radar is located adjacent to the Weather Forecast Office (WFO) and Lower Mississippi River Forecast Center (RFC) at Slidell Airport in Slidell, St. Tammany Parish, LA. The radar site is about 30 miles northeast of downtown New Orleans, LA and about 78 miles east of downtown Baton Rouge, LA. The KLIX WSR-88D was commissioned in February 1995 and has been in continuous operation since 1995. It is one of 159 WSR-88Ds in the nationwide network. NWS plans to relocate the KLIX WSR-88D from its current site to a new location at Hammond North Shore Regional Airport (HDC) in Hammond, Tangipahoa Parish, LA (about 40 miles west-northwest of the WSR-88D’s existing location).
    [Show full text]
  • Real-Time Implementation of Refractivity Retrieval: Partnership Between the University of Oklahoma, National Severe Storms Laboratory, and the Radar Operations Center
    P8B.8 1 REAL-TIME IMPLEMENTATION OF REFRACTIVITY RETRIEVAL: PARTNERSHIP BETWEEN THE UNIVERSITY OF OKLAHOMA, NATIONAL SEVERE STORMS LABORATORY, AND THE RADAR OPERATIONS CENTER B. L. Cheong1 ,∗, R. D. Palmer1 , C. Curtis2 ,3 , K. Hondl3 , P. Heinselman2 ,3 , D. Zrnic3 , D. Forsyth3 , R. Murnan4 , R. Reed4 and R. Vogt4 1 School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma, USA 3 NOAA/OAR National Severe Storm Laboratory, Norman, Oklahoma, USA 4 NWS NEXRAD Radar Operations Center, Norman, Oklahoma, USA ABSTRACT 1. INTRODUCTION High-resolution, near-surface refractivity measurements Until recently, moisture measurements were normally have the potential of becoming an important tool for only possible by in situ instruments. For example, the ra- operational forecasting and general scientific studies. diosonde network across the nation with approximately Access to measured refractivity fields with high spatial 50-100 km spacing performs hourly measurements. To and temporal resolution near the surface opens a new pursue further understanding and better prediction of paradigm for understanding the convective processes convective processes, e.g., convective precipitation and within the boundary layer. It has been shown via ad- its intensification, the existing surface instruments sim- vanced physical models that surface refractivity plays ply do not provide sufficient spatial and temporal reso- an important role in convective processes and, there- lution [Weckwerth and Parsons, 2003]. Fortunately, sur- fore, is expected to be valuable for forecasting the initi- face moisture is now possible to be retrieved remotely ation and intensity of convective precipitation. For this using radar echoes from ground targets [Fabry et al., project, the refractivity field is retrieved remotely us- 1997].
    [Show full text]
  • Nexrad/Wsr-88D History
    NEXRAD/WSR-88D HISTORY NEXRAD (As of June 22, 2018) 1971 - First Doppler radar installed (at National Severe Storms Laboratory (NSSL), OK) to study morphology of storms (June). 1973 - Second Doppler radar installed (at Cimarron Airport, OK) to study morphology of storms (May). 1976 - DOC, DOD, DOT (tri-agency) formed Joint Doppler Operational Project (JDOP) to explore benefits of Doppler radar observations. 1978 - JDOP report presented three basic findings: .. 20 minute average lead time for detecting storm before occurrence; .. Doppler able to detect gust fronts; .. Doppler information can be processed for display in real time. 1979 - The US Air Force Geophysical Laboratory (AFGL) transferred 5 cm Doppler radar to Norman, OK to compare with NSSL radar (Spring). - April 10th storm (at Wichita Falls, TX) provided evidence that 5 cm radar had; .. More attenuation; .. More range folding; .. More velocity aliasing. - The Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM) approved concept document of NEXRAD (July). - OFCM established NEXRAD Program Council (NPC) (July). - NPC approved formation of Radar Test and Development Branch (RTDB) (Fall). - The Congressional Office of Management and Budget (OMB) directed OFCM to conduct a tri-agency crosscut study for NEXRAD (October). - The National Oceanic and Atmospheric Administration (NOAA) approved establishing of the Joint System Program Office (JSPO) (November). 1980 - NPC approved establishment of Interim Operational Test Facility (IOTF) (forerunner of Operational Support Facility) (OSF). - NPC formed NEXRAD Technical Advisory Committee (TAC) (Spring). - Congress appropriated first funding (1981) for NEXRAD (October). 1981 - RTDB name changed to Interim Operational Test Facility (IOTF). - NPC approved Joint Operational Requirements (JOR) (January).
    [Show full text]
  • Weather Surveillance Radar - 1988 Doppler (WSR-88D) Integrated Logistics Support Plan
    R400-IS301C 15 March 2002 Supercedes R400-IS301B 01 June 1998 Weather Surveillance Radar - 1988 Doppler (WSR-88D) Integrated Logistics Support Plan TABLE OF CONTENTS Page 1. INTRODUCTION ..................................................1-1 1.1 Overview..................................................1-1 1.1.1 Purpose ...........................................1-1 1.1.2 Program Summary...................................1-2 1.2 Applicability ................................................1-2 1.2.1 Background ........................................1-2 1.2.2 Scope.............................................1-2 1.2.3 Program Management Responsibility .....................1-3 1.3 References................................................1-3 1.4 WSR-88D System Description .................................1-4 1.4.1 Radar Data Acquisition ................................1-4 1.4.2 Radar Product Generator ..............................1-4 1.4.3 Principal User Processor ..............................1-5 1.4.4 Communications.....................................1-5 1.4.5 Facilities ...........................................1-5 2. AGENCY, DEPOT AND USER RESPONSIBILITIES ......................2-1 2.1 General ...................................................2-1 2.2 Program Management Committee ..............................2-1 2.3 Department of Commerce, National Weather Service ...............2-2 2.3.1 Office of Science and Technology .......................2-2 2.3.2 Office of Operational Systems ..........................2-4 2.3.3 National Weather
    [Show full text]
  • MEMORANDUM of AGREEMENT Among the Department Of
    MEMORANDUM OF AGREEMENT among the Department of Commerce Department of Defense and Department of Transportation for Interagency Operation of the Weather Surveillance Radar-1988, Doppler (WSR-88D) March 2021 NOAA-DOT-DOD-NEXRAD-2021 CONTENTS Page ACRONYMS AND ABBREVIATIONS.......................................... iv DEFINITION OF TERMS................................................. v 1. PURPOSE........................................................ 1 2. BACKGROUND..................................................... 2 A. NEXRAD Program Council ..................................... 2 B. NEXRAD Program Management Committee ........................ 3 C. Radar Operations Center .................................. 3 D. Technical Advisory Committee ............................. 4 E. WSR-88D Sites .............................................. 4 F. Federal Meteorological Handbook Number 11, Doppler Radar Meteorological Observations ................. 4 G. Recent Changes in Data Distribution to Principal Users .... 5 3. POLICY......................................................... 5 4. UNIT RADAR COMMITTEE........................................... 5 A. Membership ................................................... 6 B. Functions .................................................... 6 C. Responsibilities and Limitations of Authority .............. 8 D. Equitable Apportionment of Costs ........................... 9 5. FOCAL POINTS ................................................. 10 6. DEPARTMENT OF COMMERCE RESPONSIBILITIES.......................
    [Show full text]
  • Comparing Dual-Polarization Radar Lightning Forecast Methods Across Southwest Utah Daniel O
    Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-22-2019 Comparing Dual-Polarization Radar Lightning Forecast Methods across Southwest Utah Daniel O. Katuzienski Follow this and additional works at: https://scholar.afit.edu/etd Part of the Atmospheric Sciences Commons, and the Meteorology Commons Recommended Citation Katuzienski, Daniel O., "Comparing Dual-Polarization Radar Lightning Forecast Methods across Southwest Utah" (2019). Theses and Dissertations. 2203. https://scholar.afit.edu/etd/2203 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. Comparing Dual-Polarization Radar Lightning Forecast Methods Across Southwest Utah THESIS Daniel O. Katuzienski, 1st Lt, USAF AFIT-ENP-MS-19-M-083 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENP-MS-19-M-083 COMPARING DUAL-POLARIZATION RADAR LIGHTNING FORECAST METHODS ACROSS SOUTHWEST UTAH THESIS Presented to the Faculty Department of Engineering Physics Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Atmospheric Science Daniel O.
    [Show full text]
  • P1.13 IDENTIFYING the CAUSE of WSR-88D GHOST ECHOES Randy
    P1.13 IDENTIFYING THE CAUSE OF WSR-88D GHOST ECHOES Randy M. Steadham* NOAA/NEXRAD Radar Operations Center, Norman, Oklahoma Charles A. Ray RS Information Systems, Norman, Oklahoma 1. INTRODUCTION During April 2004, staff at the Lubbock (KLBB) National A review of the WSR-88D Hotline database, culminating Weather Service (NWS) Weather Forecast Office (WFO) over a decade of records about WSR-88D field were seeing bogus echoes in WSR-88D (Weather problems, yielded no similarities to the ghost echoes Surveillance Radar, 1988-Doppler) products. The Radar found at Lubbock. Since ghost echoes appeared to be Operations Center (ROC) began an investigation to find a singular problem, team members were inclined to the cause and to provide a solution. We called the false suspect a Lubbock hardware problem. Critical echoes “ghost echoes.” components at KLBB were soon found to be operable. From fields of meteorology, engineering, and electronics, Troubleshooting efforts were vastly aided when we a ROC troubleshooting team was assembled. The team could study ghost echoes in a controlled environment. examined KLBB products containing these false returns. A detailed account of the troubleshooting process is not We found ghost echoes were only produced from WSR- discussed in this paper. 88D batch waveform. WSR-88D scanning strategies often utilize batch waveform for elevation angles above 1.65º and below 6º. Batch waveform consists of a short series of long radar pulse intervals (Surveillance) followed by a longer series of short radar pulse intervals (Doppler). The true range detected from the surveillance pulses is used to correct range folding in Doppler pulses (Doviak and Zrnic 1993).
    [Show full text]
  • 1988 Doppler (WSR-88D) Integrated Logistics Support Plan
    R400-IS301E 29 May 2020 Supersedes R400-IS301D.2 15 Aug 2010 Weather Surveillance Radar - 1988 Doppler (WSR-88D) Integrated Logistics Support Plan WSR-88D Integrated Logistics Support Plan R400-IS301E 29 May 2020 Prepared by: WSR-88D Radar Operations Center, Program Branch Submitted by: Terrance J. Clark, WSR-88D Integration Program Manager Approved: For the Department of Commerce: _____________________________________ ____________________ Thomas Cuff Date Director, Office of Observations National Weather Service For the Department of Defense: _____________________________________ ____________________ Mr. John P. Dreher Date Chief, Weather Programs Branch Air Force Life Cycle Management Center For the Department of Transportation: _____________________________________ ____________________ Malcolm Andrews Date AJM-3, Director, Enterprise Services Federal Aviation Administration For the WSR-88D Program: _____________________________________ ____________________ Kevin Cooley Date Director, Office of Planning and Programming for Service Delivery, National Weather Service Chairman, WSR-88D Program Management Committee ii TABLE OF CONTENTS Page 1. Introduction ................................................................................................................1-1 1.1 Overview ........................................................................................................1-1 1.1.1 Purpose ...............................................................................................1-1 1.1.2 Program Summary .............................................................................1-1
    [Show full text]
  • NEXRAD Strategic Plan 2021 – 2025
    National Weather Service NEXRAD Strategic Plan 2021 – 2025 1 NEXRAD is the Premier System for Hazardous Weather Detection NEXRAD is a tri-agency system instrumental in assisting the National Weather Service (NWS), Department of Defense (DoD), and Federal Aviation Administration (FAA) in meeting mission objectives. The system’s performance meets or exceeds the 96% operational availability requirement to reliably observe and detect hazardous weather; support forecast/warning programs; protect lives, property, and military assets; promote a safe and efficient National Airspace System, and enhance the nation’s economy. Since deployment began in 1992, the NEXRAD program has executed a continuous program of modifications, retrofits, technology refreshments, and pre-planned product improvement upgrades. The goal of this effort has been to extend system life, upgrade functionality and capabilities, improve data quality, meet new mission requirements, improve system maintainability and reliability, and control NEXRAD operations and maintenance (O&M) costs. The Service Life Extension Program (SLEP) began in 2015 and will continue into 2024. SLEP consists of five projects to replace and refurbish major components of the radar: signal processor, transmitter, equipment shelters, pedestal, and backup generator. As a result of these sustaining engineering, hardware, and technology refresh investments, NEXRAD continues to be upgradable, reliable, and maintainable beyond 2035 or until a suitable replacement for the WSR-88D is deployed. In the NEXRAD network, there are 12 FAA, 25 DoD, and 122 NWS operational radars. The NWS also operates 10 support systems for training, parts refurbishment, and testing of hardware and software modifications. The Radar Operations Center (ROC) budget supports daily ROC operations, depot-level radar maintenance, and system modification costs that are equitably shared in accordance with the tri-agency signed Cost Share Allocation Memorandum of Agreement (MOA).
    [Show full text]