DOCSLIB.ORG

Gauge and Radarradargauge

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gauge and Radarradargauge GaGaugugeeaandndRRaadadarr PPaaoo--LLiaiangngChaChangng CentCentrarallWWeaeattherherBBurureaeau,u,TTaaiwiwaann A Training Course on Quantitative Precipitation Estimation/Forecasting (QPE/QPF) Crowne Plaza Manila Galleria, Quezon City, Philippines 27-30 March 2012 OOuutlitlinnee RaRadadarraandndGGaaugugeeNetNetwwoorkrkininTTaaiwiwaann RaRadadarrDaDattaaQQCCususingingRefReflectlectivivitityyaandnd RaRainfinfaallllClimClimaattoolologgyy RaRadadarrQQPPEEaandndGGaaugugee--cocorrrrectectededQQPPEE OOututloloookk 2 OOppeerratiationonalalRRadadararNNeetwtwororkkiinnTTaiaiwwanan RRCCWWFF RRCCCCKK RRCCHHLL RRCCMMKK RRCCCCGG RRCCKKTT CCWBWB::RRCCWFWF,,RRCCHHLL,,RRCCKKTT,,RRCCCCGG((DDopoppplleerr,,wwaveavelleenngtgthh::10c10cmm)) AAiirrFFororccee::RRCCCCKK,,RRCCMMKK((dduualal--ppololarariizzatatiionon,,wwaveavelleenngtgthh::5c5cmm)) Taiwan operational radar network basic information RCWF RCHL RCCG RCKT RCCK RCMK Observation Range (km) 460,230 460,230 460,230 460,230 460,160 460,160 (Z,Vr) Gematronik Gematronik Gematronik Gematronik Gematronik Type WSR-88D 1500S 1500S 1500S 1500C 1500C Height (m) 766 63 38 42 203 48 Wavelength (cm) 10 10 10 10 5 5 Polarization Single Single Single Single Dual Dual Max. Unambiguous 26.55 21.15 21.15 49.5 49.5 49.5 Velocity (m/s) GGaaugugeeStStaattioionsns inin TTaaiwiwaann Data from CWB and Gov. agencies (WRA, SWCB,TPC,..) •All Gauge stations ~570 stations •Overland ~560 stations MMeaeannGGaaugugeeSpaSpacingcing ffrroommCWBCWBSitSiteses((dadattaainin22000077)) Number of Gauges CoConceptnceptooffRefReflectlectivivitityyClimClimaattoolologgyy ininCoConsnsttructructingingRaRadadarrHHyybridbridScaScansns TThhee hhybybrriiddssccanan ooff aarradadarariinncclluuddeesstthheelloowweesstt rraaddarar bbiinnss tthhatatddoo nnotot hhaveave ssiigngniiffiiccanantt bbllocockkageagess ororcclluutttteerr ccononttamamiinnatatiiononss.. TThhee rradadarar rreefflleeccttiivviittyy cclliimmatatoollogyogy iiss ddeeffiinneedd aass tthhee ffrreeqquueennccyy ooff ococccuurrrreennccee ooff rreefflleeccttiivviittyy((FFOORR)) tthhatat iiss hhiighgheerrtthhanan aacceerrttaiainn tthhrreesshhoolldd,, anandd iittiiss ccalalccuullatateedd ononaappiixxeell--bbyy--ppiixxeellbbasasiissiinntthheerradadaarr ((sspphheerriiccalal)) ccooroorddiinnatateess.. TThhee vavalluueess ooff FFOORRrranangegeffrromom00ttoo11,,wwiitthh11rreepprreesseennttiinngg eecchhoeoess tthhatatararee ddeetteecctteedd aalllltthheettiimmee((cclluutttteerrss))anandd 00iinnddiiccatatiinngg tthhatatnnoo eecchhoeoess hhiighgheerr tthhanantthheetthhrreesshhololdd araree eeveverr ddeetteecctteedd ((bbllocockkageagess)).. TThhee rreefflleeccttiivviittyy cclliimmatatolologyogy atatananyy ggiivevennllococatatiioonn iiss rreellatateedd ttoo wwhheetthheerr tthheerree iiss rraiainn ororcclluutttteerr atattthheellooccatatiionon.. IIff alallltthheeeecchhoeoess ddeetteecctteedd aatt tthhee llococatatiionon aarree rraaiinn,,tthheenn tthheeeexxppeecctteeddrranangegeooff FFOORR valvaluueess sshhououlldd bbee ccllososee ttootthhoosseeooff tthheeffrreeqquueennccyy ooff ococccuurrrreennccee ooff gaugaugege rraiainnffalalll ((FFOOGG)) Chang, P. L., P. F. Lin, B. J. -D. Jou, and J. Zhang, 2009:An application of reflectivity climatology in constructing radar hybrid scans over complex terrains. J. Atmos. Oceanic Technol. 26, 1315-1327. RaRadadarrdadattaaquaqualitlityycocontntrrooll RaRaddaarrSSccaann SSttrraatteeggyy TTeerrrraaininHHeeigighhtt ReReff..CliClimmaattoolologgyy GGaauuggeeRaRaininffaallll CliClimmaattoolologgyy FFrrequencyequencyooffooccurccurrrenceence(%(%))ooff ≧ 00--dBZdBZ rrefleflectectiivviitytyaatt00..55degdegrreeseeselelevevaatitioonn RCWF RCHL RCCG RCKT FFrrequencyequencyooffooccurccurrrenceence(%(%))ooff ≧ 1100--dBZdBZ rrefleflectectiivviitytyaatt22..44degdegrreeseeselelevevaatitioonn RCWF RCHL RCCG RCKT FFrrequencyequencyooffooccurccurrrenceenceooffrrefleflectectiivviityty aatt00..55degdegrreeseeselelevevaatitioonnfoforrfofourursseaeassoonsns Spring Summer Fall Winter GaGaugugeeraraiinfanfallllfrfrequencyequencyooff ooccurccurrrenceenceduriduringng22000055--22000077 spring summer fall winter HyHybribriddsscacanntatablblesesdeterdetermmiinednedfrfroommterterraraiin,n, sscacannsstratrategtegyy,,aandndraradadarrclcliimmaatotollooggyy MMososaiaicckkeedd HHyybbrriiddHHeeiighghtt(fou(fourrrradadararss)) RRCCCCKK RRCCMMKK Severe blockage FFrrequencyequencyooffooccurccurrrenceence(%(%))ooff rrefleflectectiivviititieses ≥ 15 dBZdBZ wwiiththhyhybribriddsscacanntatablblesesbefobeforreeaandndaaftefterrQCQC summer summer before After (terrain only) (Terrain + Climo. winter winter SumSummmaaryryooffRefReflectlectivivitityy aandndGGaaugugeeRaRainfinfaallllClimClimaattoolologgyy BByy ccomompparariinngg tthhee reflectivity cclliimmatatolologyogy wwiitthh gaugaugege obobseserrvatvatiionons,s, iitt wwasas ffouounndd tthhatat 1515ddBBZZ wwasas aa goodgood apappprroxioximmatatiionon fforor rraiainn//nnoo--rraiainn sesegrgreegatgatiionon iinn ccooloolseseasonasonss anandd 2020 ddBBZZ wwororkkeedd wweellll iinn wwararmm seseasonasonss.. CCoommpparariisonsonss bbeettwweeeenn tthhee ststananddarardd anandd nnononststaannddarardd hhybybrriidd scscanan FFOORRss shshowoweedd tthhatat tthhee fforormmeerr ddiidd nnotot aaccccuurratateellyy rreefflleecctt tthhee cclluutttteerr anandd bbllocockkageage ddiiststrriibbuuttiiononss iinn tthhee rreealalatatmmosposphheerree.. TThhee apappplliiccatatiionon oofftthhee reflectivity cclliimmatatolologyogy wwasas shshowownn ttoo sisigngniiffiiccananttllyy rreedduuccee tthhee iimmppacacttss ofofcclluutttteerr anandd bbllocockkageagess anandd pprrovioviddeedd iimmpprroveovedd rradadarar QQPPEEss oveoverrtthhee ccomompplleexx tteerrrraiainn.. RaRadadarr RefReflectlectivivttyy QQPPEE LoLoccaalliizzeeddZZ(r(reeflfleecctitivviityty))-- RR(r(raaiinnrraatete))rreellaatitioonnsshhiipp Hybrid reflectivity Hourly rain rate 1- and 3-h acc 6- to 72-h acc RRaaiinnffaalllliinnffoorrmmaattiioonnoovveerrtthheeaarreeaawwiitthhoouuttrraaiinnggaauuggee CoConvnvectectivive/Ste/StrarattififoormrmSegSegrregegaattioionn ddBBZZ >>5050iinnananyybbiinnoror,, ddBBZZ >>3030atattteemmppeerratatuurreess<< --1010 CCoror,, 11lliighghttnniinnggffllashash non-tropical precipitation non-tropical precipitation tropical precipitation tropical precipitation ZZ--R relationRrelation Z= 10 log dBZ Z D6 Z N (D) D 6 dD R N(D) D3w(d)dD 3 6 t R D Z= ? R Depends on N(D) ! There are various Z-R relationships applicated over the world. Z=200R1.6 Marshall and Palmer(1948) Z=140R1.5 drizzle Z=500R1.5 Thunderstorm Z=300R1.4 NEXRAD雷達 ……… Z=32.5R1.65 QPESUMS ZZ--R relationshipsRrelationships Precipitation a b Type convective 300.0 1.4 hail 300.0 1.4 stratiform 200.0 1.6 tropical 32.5 1.65 snow 75.0 2.0 StStepsepsooffGGaaugugeeCoCorrrrectectededRaRadadarrQQPPEE 11))CCoompmpuuttiinnggggaauuggee--raraddaarrbbiiaasseessaatteeaacchhggaauuggeessttaattiioonnssuussiinngg hhoouurlrlyyraraiinnffaallllssffrorommggaauuggeeoobbsseervrvaattiioonnssaannddffrorommraraddaarreessttiimatmateess aattccoo--llooccaatteeddggririddcceellllss.. ~560 22))IInntteerprpoollaattiinnggtthheeggaauuggee--raraddaarr bbiiaasseessffrorommggaauuggeessttaattiioonnssoonnttoo tthheeHHRRQQaannaallyyssiissggriridd.. 33))AAppppllyyiinnggtthheeggririddddeeddggaauuggee--raraddaarrbbiiaasseess ttootthheeraraddaarr--bbaasseeddQPQPEE.. 44))RReeppllaacciinnggtthheeffiinnaall LLGGCCQPQPEEaattaallll ggririddcceellllsstthhaatt ccoo--llooccaatteewwiitthhaaggaauuggeessttaattiioonnwwiitthhtthheeggaauuggeeoobbsseervrveeddvvaalluueess.. 55))AAppppllyyiinnggaallaannddmasmaskkttootthheeLLGGCCQPQPEEffiieellddaannddccuuttttiinnggoofffftthhee ddaattaa oovveerrtthheeoocceeaannbbeeccaauusseennooggaauuggeeccoorrerreccttiioonniissaavvaaiillaabblleeiinn tthheeoocceeaannaarereaa.. RRaiainngaugaugegeccororrreeccteteddrradadararQQPPEE 00--2424hhrrssQQPPEE+gau+gaugege 00--2424hhrrssQQPPEE 0-24 hrs gauge obs FFiinneerraannddmmoorreerreeaassoonnaabblleeQQPPEEoovveerrllaanndd DDiiffefferreenncceebbeetwtweeeennththeellococalalgaugaugege ccororrreeccteteddrradadararQQPPEEananddaagaugaugegeananalalyyssiiss a b Radar QPE GAUGE c LGC QPE LGC QPE - Gauge LGLGCCQQPPEEEvalEvaluuatiationon SSeelleeccttaaddiiffffeerreennttnnuummbbeerrooffrraaiinnggaauuggeessffoorrtthheeccaallccuullaattiioonnooff LLGGCCQQPEPE,,tthheerreessttggaauuggeessaarreeuusseeddffoorrvveerriiffiiccaattiioonnss.. IInntteerrpprreetttthheesseelleecctteeddggaauuggeerraaiinnffaallllttooCaCarrtteessiiaannggrriidd.. CaCallccuullaatteetthheeccoorrrreellaattiioonnssbbeettwweeeennLLGGCCQQPEPEaannddrreessttggaauuggeess,, aasswweellll aassggaauuggeerraaiinnffaalllliinnCaCarrtteessiiaann.. 50 gauge 250 gauge stations selected stations selected Total ~400 in 2005 Gauge Corrected QPE Southwest flow caseSouthwest case 50 gauge stations selected Guage 250 gauge stations selected Gauge Corrected QPE TTyypphhoonoon HHaiaitantangg (2005)(2005) 50 gauge stations selected Guage 250 gaugeGauge stations Corrected selected QPE 200520050612~06160612~0616 ––5050sstatitationonsssseelleecctetedd 20052005 0612~06160612~0616––250250 sstatitationonsssseelleecctetedd SSououththwweessttFFllowow//TTyypphhoonoon HHaiaitantangg ccasaseess Local Gauge Corrected QPE Correlations Coefficient R (%) Gauges Only (interpolated) 350 300 250 200 150 100 Number of Gauges used CoCommpaparisrisoonsnsbetbetwweeneenggaaugugee aandndggaaugugeecocorrrrectectededQQPPEE Gauge Gauge corrected QPE + + + + + + ++ + + + + + ＋:Gauge station Typhoon Megi (2010/1018~22) 96 hours rainfall accumulation TTyyphophooonnFFaananapipi ((22001100/0/0991188~~2211))
Load more

Recommended publications
  • 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.
    [Show full text]
  • Quantitative Interpretation of Laser Ceilometer Intensity Profiles
    396 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 14 Quantitative Interpretation of Laser Ceilometer Intensity Pro®les R. R. ROGERS AND M.-F. LAMOUREUX Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada L. R. BISSONNETTE Defence Research Establishment Valcartier, Courcelette, Quebec, Canada R. M. PETERS Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania (Manuscript received 23 July 1996, in ®nal form 28 October 1996) ABSTRACT The authors have used a commercially available laser ceilometer to measure vertical pro®les of the optical extinction in rain. This application requires special signal processing to correct the raw data for the effects of receiver noise, high-pass ®ltering, and the incomplete overlap of the transmitted beam with the receiver ®eld of view at close range. The calibration constant of the ceilometer, denoted by C, is determined from the pro®le of the corrected returned power in conditions of moderate attenuation in which the power is completely extin- guished over a distance on the order of 1 km. In this determination, the value of the backscatter-to-extinction ratio k of the scattering medium must be speci®ed and an allowance made for the effects of multiple scattering. These requirements impose an uncertainty on C that can amount to 650%. An alternative to determining the calibration constant is explained, which does not require specifying k, although it assumes that k is constant with height. Using this alternative approach, the authors have estimated many extinction pro®les in rain and compared them with radar re¯ectivity pro®les measured with a UHF boundary layer wind pro®ler.
    [Show full text]
  • Ott Parsivel - Enhanced Precipitation Identifier for Present Weather, Drop Size Distribution and Radar Reflectivity - Ott Messtechnik, Germany
    ® OTT PARSIVEL - ENHANCED PRECIPITATION IDENTIFIER FOR PRESENT WEATHER, DROP SIZE DISTRIBUTION AND RADAR REFLECTIVITY - OTT MESSTECHNIK, GERMANY Kurt Nemeth1, Martin Löffler-Mang2 1 OTT Messtechnik GmbH & Co. KG, Kempten (Germany) 2 HTW, Saarbrücken (Germany) as a laser-optic enhanced precipitation identifier and present weather sensor. The patented extinction method for simultaneous measurements of particle size and velocity of all liquid and solid precipitation employs a direct physical measurement principle and classification of hydrometeors. The instrument provides a full picture of precipitation events during any kind of weather phenomenon and provides accurate reporting of precipitation types, accumulation and intensities without degradation of per- formance in severe outdoor environments. Parsivel® operates in any climate regime and the built-in heating device minimizes the negative effect of freezing and frozen precipitation accreting critical surfaces on the instrument. Parsivel® can be integrated into an Automated Surface/ Weather Observing System (ASOS/AWOS) as part of the sensor suite. The derived data can be processed and 1. Introduction included into transmitted weather observation reports and messages (WMO, SYNOP, METAR and NWS codes). ® OTT Parsivel : Laser based optical Disdrometer for 1.2. Performance, accuracy and calibration procedure simultaneous measurement of PARticle SIze and VELocity of all liquid and solid precipitation. This state The new generation of Parsivel® disdrometer provides of the art instrument, designed to operate under all the latest state of the art optical laser technology. Each weather conditions, is capable of fulfilling multiple hydrometeor, which falls through the measuring area is meteorological applications: present weather sensing, measured simultaneously for size and velocity with an optical precipitation gauging, enhanced precipitation acquisition cycle of 50 kHz.
    [Show full text]
  • Downloaded 09/24/21 04:33 AM UTC APRIL 2013 F R I E D R I C H E T a L
    1182 MONTHLY WEATHER REVIEW VOLUME 141 Drop-Size Distributions in Thunderstorms Measured by Optical Disdrometers during VORTEX2 KATJA FRIEDRICH AND EVAN A. KALINA University of Colorado, Boulder, Colorado FORREST J. MASTERS AND CARLOS R. LOPEZ University of Florida, Gainesville, Florida (Manuscript received 13 April 2012, in final form 22 October 2012) ABSTRACT When studying the influence of microphysics on the near-surface buoyancy tendency in convective thun- derstorms, in situ measurements of microphysics near the surface are essential and those are currently not provided by most weather radars. In this study, the deployment of mobile microphysical probes in con- vective thunderstorms during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) is examined. Microphysical probes consist of an optical Ott Particle Size and Velocity (PARSIVEL) disdrometer that measures particle size and fall velocity distributions and a surface obser- vation station that measures wind, temperature, and humidity. The mobile probe deployment allows for targeted observations within various areas of the storm and coordinated observations with ground-based mobile radars. Quality control schemes necessary for providing reliable observations in severe environ- ments with strong winds and high rainfall rates and particle discrimination schemes for distinguishing be- tween hail, rain, and graupel are discussed. It is demonstrated how raindrop-size distributions for selected cases can be applied to study size-sorting and microphysical processes. The study revealed that the raindrop-size distribution changes rapidly in time and space in convective thunderstorms. Graupel, hailstones, and large raindrops were primarily observed close to the updraft region of thunderstorms in the forward- and rear-flank downdrafts and in the reflectivity hook appendage.
    [Show full text]
  • Radar Artifacts and Associated Signatures, Along with Impacts of Terrain on Data Quality
    Radar Artifacts and Associated Signatures, Along with Impacts of Terrain on Data Quality 1.) Introduction: The WSR-88D (Weather Surveillance Radar designed and built in the 80s) is the most useful tool used by National Weather Service (NWS) Meteorologists to detect precipitation, calculate its motion, estimate its type (rain, snow, hail, etc) and forecast its position. Radar stands for “Radio, Detection, and Ranging”, was developed in the 1940’s and used during World War II, has gone through numerous enhancements and technological upgrades to help forecasters investigate storms with greater detail and precision. However, as our ability to detect areas of precipitation, including rotation within thunderstorms has vastly improved over the years, so has the radar’s ability to detect other significant meteorological and non meteorological artifacts. In this article we will identify these signatures, explain why and how they occur and provide examples from KTYX and KCXX of both meteorological and non meteorological data which WSR-88D detects. KTYX radar is located on the Tug Hill Plateau near Watertown, NY while, KCXX is located in Colchester, VT with both operated by the NWS in Burlington. Radar signatures to be shown include: bright banding, tornadic hook echo, low level lake boundary, hail spikes, sunset spikes, migrating birds, Route 7 traffic, wind farms, and beam blockage caused by terrain and the associated poor data sampling that occurs. 2.) How Radar Works: The WSR-88D operates by sending out directional pulses at several different elevation angles, which are microseconds long, and when the pulse intersects water droplets or other artifacts, a return signal is sent back to the radar.
    [Show full text]
  • Observation of Cloud Base Height and Precipitation Characteristics at a Polar Site Ny-Ålesund, Svalbard Using Ground-Based Remote Sensing and Model Reanalysis
    remote sensing Article Observation of Cloud Base Height and Precipitation Characteristics at a Polar Site Ny-Ålesund, Svalbard Using Ground-Based Remote Sensing and Model Reanalysis Acharya Asutosh 1,2,*, Sourav Chatterjee 1,3 , M.P. Subeesh 1, Athulya Radhakrishnan 1 and Nuncio Murukesh 1 1 National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Goa 403804, India; [email protected] (S.C.); [email protected] (M.P.S.); [email protected] (A.R.); [email protected] (N.M.) 2 Indian Institute of Technology, Bhubaneswar, Odisha 752050, India 3 School of Earth, Ocean, and Atmospheric Sciences, Goa University, Goa 403206, India * Correspondence: [email protected] Abstract: Clouds play a significant role in regulating the Arctic climate and water cycle due to their impacts on radiative balance through various complex feedback processes. However, there are still large discrepancies in satellite and numerical model-derived cloud datasets over the Arctic region due to a lack of observations. Here, we report observations of cloud base height (CBH) characteristics measured using a Vaisala CL51 ceilometer at Ny-Ålesund, Svalbard. The study highlights the monthly and seasonal CBH characteristics at the location. It is found that almost 40% of the lowest Citation: Asutosh, A.; Chatterjee, S.; CBHs fall within a height range of 0.5–1 km. The second and third cloud bases that could be detected Subeesh, M.P.; Radhakrishnan, A.; by the ceilometer are mostly concentrated below 3 km during summer but possess more vertical Murukesh, N. Observation of Cloud spread during the winter season. Thin and low-level clouds appear to be dominant during the Base Height and Precipitation summer.
    [Show full text]
  • METEOROLOGICAL HAZARDS by Werner Wehry and Lutz Lesch Prcsenledat the XXV Ostmongress, St
    NOWCASTING METEOROLOGICAL HAZARDS by Werner Wehry and Lutz Lesch Prcsenledat the XXV OsTMongress, St. Auban, Fnnce cal elements of interest are process€d and displayed by The main workofregional forecasters and especially of means ofstatistical model inlerpretation (Knup ffer, 1996). m€teorologists briefing geneml aviation and, especially, Some of this information is available, already, in the Cer, flight comp€titions is to provide most accurate Nowcastin g man Weather Service systems. (up to twohours) and VeryShortRange Forecasting (VSRF, Hosever. mrssinS are the hard \rlue, concernrng up to 12 hours) including wamings of hazardous weather extreme weather, i-e- w€ath€rhazards likemaximum gusts, events in general and in task actions. In co-operation with large amount of convective rain, conditions of fl ash fl oods, the German National Weather Service (DWD) our Work- hail, and - in wint€r - sudden tuming ofrain into snow or ing Croup does res€arch pr€paring a modular system to glaze conditions. "Waming points" are available in the which will work mostly automatically, and it includes DWD monitoring system, now, like hail heads derived empirical parts. This system will enable the forecaster to from satellite cloud top temperature and thunderstorms, give objective information for proper decisions. A similar derived from radar and w€ather observations and, espe system working for several parts of the USA has been cially, from lightning location sysiems. described by Eilts et al (1996). 3. Special Methods for Nowcasting Hazards 2. Monitoring We are developing an empirical syst€m, combining all Such a nowcasting system needs all available meteoro' available Direct Model Output (DMO)and Model Output loSical inputin order to monitor the atmospherjc€lements Statistics (MOS) information, in order to catch theseldom- like t€mperature, precipitation, gusts etc.
    [Show full text]
  • Triggered Upward Lightning from Towers in Rapid City, South Dakota
    LIGHTNING-TRIGGERED UPWARD LIGHTNING FROM TOWERS IN RAPID CITY, SOUTH DAKOTA Tom A. Warner 1*, Marcelo M. F. Saba 2, Scott Rudge 3, Matthew Bunkers 3, Walt Lyons 4, and Richard E. Orville 5 1. Dept. of Atmospheric and Environmental Sciences, South Dakota School of Mines and Technology, Rapid City, SD, USA 2. National Institute for Space Research – INPE – Sao Jose dos Campos, Brazil 3. NOAA /National Weather Service, Rapid City, SD, USA 4. FMA Research, Inc., Fort Collins, CO, USA 5. Dept. of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA 1. INTRODUCTION Since 2004, 84 upward flashes from 10 towers (91–191 m AGL) have been observed using optical Recent research efforts have shown that nearby instrumentation. High-speed cameras were added in flashes can trigger upward lightning from tall towers 2008 along with electric field sensing instrumentation in (e.g., Wang and Takagi, 2010; Mazur and Ruhnke , 2009, which allowed for coordinated measurements for 2011; Warner, 2011, Warner et al., 2011, and Zhou et some flashes. An analysis has shown that preceding al., 2011). Findings from these studies suggest that visible flash activity triggered all but one upward flash. these lightning-triggered upward lightning (LTUL) The upward flashes discussed below were flashes are more common with summer thunderstorms, observed with up to three high-speed cameras whereas self-initiated upward lightning (SIUL) flashes, operating between 1,000 and 67,000 images per where an upward leader initiates from a tall object second (ips). Fast electric field change data were without nearby preceding flash activity, favor less collected using a modified whip antenna at a sensor site electrically active winter storms.
    [Show full text]
  • Analysis of Two Independent Methods for Retrieving Liquid Water Profiles in Spring and Summer Arctic Boundary Clouds U
    JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D7, 4219, doi:10.1029/2002JD002861, 2003 Analysis of two independent methods for retrieving liquid water profiles in spring and summer Arctic boundary clouds U. Lo¨hnert,1 G. Feingold,2 T. Uttal,2 A. S. Frisch,3 and M. D. Shupe4 Received 20 August 2002; revised 19 November 2002; accepted 9 January 2003; published 10 April 2003. [1] A large number of all-liquid, nondrizzling stratus clouds (163 hours of measurements) were observed with a dual-channel microwave radiometer and a colocated 35-GHz cloud radar during the spring and summer months of the Surface Heat Budget of the Arctic Ocean (SHEBA) project. An algorithm developed by Frisch et al. [1995, 1998] to derive the liquid water content (LWC) is applied to these measurements assuming constant cloud drop number density and cloud drop size distribution breadth with height. A second algorithm developed by Lo¨hnert et al. [2001] is specifically adapted for SHEBA clouds using a priori information from a large eddy simulation (LES) model initialized with summertime SHEBA radiosondes; about 50 soundings during nondrizzling, low-level, all- liquid water clouds are used. Using model-derived drop size distributions, a relationship between simulated radar reflectivity (Z) and model LWC is derived as well as an a priori LWC profile. Once the theoretical error covariance matrix of the Z-LWC relation is derived and the covariance matrix of the LWC profile is calculated, an optimal estimation method is applied to the SHEBA data. The Frisch et al. and Lo¨hnert et al. methods are also applied to the LES model output, resulting in overall root-mean-square differences on the order of 30 to 60%.
    [Show full text]
  • Radar Meteorology
    Radar Meteorology AOS C110/C227 Goals • Gain appreciation of uses and limitations of radar information • Summarize materials available through Comet Program ● Create free account at https://www.meted.ucar.edu ● Access basic weather radar module at https://www.meted.ucar.edu/training_module.php? id=960 ● This module can be downloaded to your own computer, and viewed in your browser offline. Unzip the archive, go to folder comet/radar/basic_wxradar/, and open index.htm in your browser. Radar types • Radar = originally an acronym for Radio Detection And Ranging • Many different types of radar • Focus on WSR-88D NEXRAD Doppler radar ● 10 cm wavelength ● Scans horizontally 360˚, at various elevation angles ● Volume scan completed in about 5 min Radars emit pulses, listen for returns targets pulse returning signal Only a small fraction of emitted energy is returned. Returned energy = radar reflectivity. Scanning pattern Scan starts with 0.5˚ elevation angle, goes up to 19.5˚ Dual polarization Example hail Scanning modes • Precipitation mode vs. clear-air mode • Precip mode (illustrated at right) scans a wide variety of elevation angles • Clear air mode (not shown) more slowly scans only up to 4.3˚, inclusive “Cone of silence” above radar “Cone of silence” example Reflectivity • Proportional to sixth power of target diameter ● As a consequence, it takes a very large number of smaller particles to appear as “bright” as a few, large particles • Measured as “Z” based on diameter to the 6th power (mm6) per cubic meter (m3) • Further converted to “dBZ”, a dimensionless, log-scaled value Characteristic• Precip values mode reflectivities can range up to about 75 dBZ ● Above 45dBZ: intense precip ● Above 60 dBZ generally indicates hail present ● Ice is less reflective than liquid ● Melting ice can be very bright on radar • Clear-air mode uses a different scale, with red still being low values ● Watch the scale! Base vs.
    [Show full text]
  • The Wyoming King Air and Mixed-Phase Clouds
    The Wyoming King Air and Mixed-Phase Clouds Zhien Wang University of Wyoming University Wyoming King Air A part of USA NSF low atmospheric facility since 1987 Examples of N2UW missions •BL Studies •Turbulence Studies •Satellite Validation •Cloud Physics & •Aerosol Studies •Airborne Remote Dynamics Sensing •Air/Sea Interaction •Radiative Fluxes & •Air Pollution Forcing •Air Chemistry •Tropospheric Profiling University Wyoming King Air A unique platform for Cloud Study Provide extended cloud microphysical properties for GVR WCR cloud physics study 2D-C • Remote sensors LWC 100 WCL-I – Cloud radar – Cloud lidar WCL-II – Microwave radiometer • In situ sensors – Aerosol – Cloud – H2O,CO2 Cloud Property Retrieval Algorithms Ice clouds Water Clouds Mixed-phase clouds IWC LWC, effective IWC and D for ice Retrieval ge Properties and General radius (reff), and phase effective radius drizzle flux LWC and reff for water (Dge) phase Lidar input Extinction Extinction Extinction Depolarization ratio Radar input Radar reflectivity Radar reflectivity Radar reflectivity or and Doppler Doppler spectrum velocity Radiometer LWP LWP References Wang and Sassen 2002; Sassen et al. 2000; Wang and Sassen 2001; Wang Comstock et al. 2007; Wang 2007; Turner et 2007; Wang et al. 2004, Wang Deng et al. 2010. al. 2007 and Zhao 2007 High Occurrence of possible mixed- phase clouds Possible mixed-phase: Tbase > -38 C and Ttop < 0 C All ICE phase - Water Possible mixed Possible The Importance of Mixed-phase Cloud Representation in GCMs • Contribution to cloud feedback uncertainty in GCMs. Net LW SW The spread of TOA cloud forcing changes among 17 AR4 coupled GCMs Cloud phase Distribution Mixed Ice Water December-January-February Cloud-phase Dependent and Regional Inter-model CRF Spread JJA Mean Net CRF MAM Net CRF STD Example of Ground-based Mixed-phase Cloud Retrieval Inputs MMCR MPL Outputs Water Phase Ice Phase Mixed-phase clouds observed at the Barrow site on 10 October 2004 by combining Radar (MMCR), Lidar (MPL), MWR, and radiosonde data.
    [Show full text]
  • What Is Dual-Polarization Radar and What Can It Do for Me?
    What is Dual-Polarization Radar and What Can It Do for Me? National Weather Service Louisville, KY Benefits of Dual-Polarization Theoretically, improve the accuracy of precipitation estimates, leading to better flash flood detection (questionable at this time) Ability to discern between heavy rain, hail, snow, and sleet Improved detection of non-meteorological echoes (e.g., ground clutter, chaff, anomalous propagation, birds, and tornado debris) Identification of the melting layer (e.g., bright band) What Dual-Polarization is Not • Will not improve tornado lead times • Will not provide exact precipitation type at ground level What is Dual-Polarization & How Is It Different from Current Radar?? • Many radars transmit and receive radio waves with a single horizontal polarization • Polarimetric radars transmit and receive both horizontal and vertical polarizations • Can determine: – SIZE – SHAPE – VARIETY Base Products Available with Dual-Pol • Still get: – Reflectivity (Z) – Velocity (V) – Spectrum Z ZDR Width (SW) • Plus: – Differential Reflectivity (ZDR) – Correlation Coefficient (CC) CC KDP – Specific Differential Phase (KDP) 5 ZDR Ref: WDTB E-learning Increasing Melting & Wetness Differential Reflectivity - ZDR • Definition: . Difference between horizontal and vertical reflectivity factors = Drop Shape . ZDR values for rain typically > 1 to as high as 5 for large drops . ZDR values for snow typically less than 0.5 (except for wet or melting snow when it’s much higher) . ZDR for hail generally between -1 and +1 (larger values for melting) Range of Values Units Abbreviations -7.9 to 7.9 decibels (dB) ZDR or ZDR CC How Do I Interpret CC? Non-Meteorological Metr (Non-Uniform) Metr (Uniform) (birds, insects, etc.) (hail, melting snow, etc.) (rain, snow, etc.) Complex scattering from Somewhat complex Well-behaved scattering pulse-to-pulse.
    [Show full text]