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Take away: A versatile and useful tool for research and operations Outline: Local network in the Tennessee Valley Basic variables A sample of research and operational applications. Why does NASA care about ground-based dual-polarimetric radar?. NASA Precipitation Science. - PowerPoint PPT Presentation

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  • Dual Polarimetric RadarWalt Petersen, NASA-MSFCTake away: A versatile and useful tool for research and operations

    Outline:Local network in the Tennessee ValleyBasic variablesA sample of research and operational applications

  • Why does NASA care about ground-based dual-polarimetric radar?NASA Precipitation ScienceDual-pol radar provides the means to remotely sense precipitation processes over volumes of atmosphere not practically accessible by other means. Advancement of physically-based satellite retrieval algorithm development.

  • Basic Polarimetric Radar VariablesReflectivity factor Z at horizontal (Zh) or vertical (Zv) polarization [Conventional radar measure]- Measure of drop size and concentration; Most sensitive to drop SIZE (D6)2.Differential reflectivity ZDR (Zh/Zv)- Measure of median drop diameter SIZE/SHAPE - Useful for rain / hail / snow discrimination SIZE/SHAPE3.Propagation differential phase KDP S(kh kv)- Measure of water content and drop size NUMBER/SHAPE- Immune to radar calibration, attenuation, partial beam blockage4.Correlation coefficient hv- Indicator of mixed precipitation SHAPE/PHASE/CANTING (Depolarization)- Useful for identifying non-meteorological scatterers too!Advantages: Better description of various particle types/shapes in a given volumeDetermine size distribution- more accurate rain rates (improved QPE)Hydrometeor ID and non-meteorological scatterers (clutter!)Consistent calibrationWe need the measurement in H and V directions!Walter A. Petersen NASA MSFC VP-61 Variables..

    Some Tools: N. Alabama Dual-Pol Radars and Supporting Infrastructure Instruments/InfrastructureRadars: C and X-band dual-polarimetricVHF Lightning mapping arrayMobile Integrated Profiling System (MIPS)Surface met./SoundingNetwork of Disdrometers, Rain gaugesModel/Forecast R&D (NASA SPoRT)Science/Application: Operational: Severe weather, QPE, lightning, WRF model; NOAA-HWT, NWS-COMET, TVA Basic/Applied Research: QPE, Cloud Physics and kinematics, cloud electrification, boundary Layer and convective initiationSatellite: NASA satellite precipitation retrieval algorithms, NOAA-GLM risk reductionTechnology Transfer :UAH: Graduate student educationNWS-Huntsville: Ops/TrainingTVA: Radar applicationsWHNT-TV (other markets): Dual-pol applications, public awareness

    RESEARCH + TECHNOLOGY TRANSFERAcademic, Govt./Public, Private Sectors

  • Jumping right in with both feet..Microphysics: 25 July 2007 Heavy rain/hail mix,large dropsZrhvZDRDemonstrating the Microphysical sequence in a T-storm: Antiquity of application.

  • Proprietary content: Walter A. Petersen NASA MSFC VP-61Using DP Variables for Thunderstorm /Lightning Remote SensingProject: NASA MSFC Support of launch operations at KSC/CCAFSQuestion: Can we exploit the dual-pol characteristics for lightning initiation and cessation nowcasting/Phase shifts due to vertically-aligned ice crystals

    E-field increaseVertical alignmentLightningE-field decreaseCrystal relaxation

  • Proprietary content: Walter A. Petersen NASA MSFC VP-61Cool season applications: Melting level asymmetry. Veering in a pre-frontal zoneNote asymmetries in ZDR and RHOHV bright bandsPolarimetric variables, in particular RHOHV, are much more sensitive to presence of mixed phaseVRZDRrhvMelting level ID is a big problem in traditional radar QPE

  • ARMOR 1/29/2010: Cold Season Mixed Phase w/Complex FL heightProblem: Temperatures around or just above freezing..Where is it liquid, freezing and frozen?

    Z has only limited information

    In this case, RHOHV adds considerable information

    We could interpret individual variables (takes time/practice)

    or..........?

  • Combine Pol variables into easy to digest information: Hydrometeor IDVivekanandan et al. (1999, BAMS)Vivekanandan et al., 1999Liu and Chandrasekar, 2001Originally (90s) we used simple tables.But boundaries between categories in nature are softmove to fuzzification

  • ARMOR: 1/29/10 Cold Season Mixed Phase: Now add HID- easier to interpret

  • Dual-Pol HID: N. AL Downburst Case and LightningZeFirst precip echo 1639 UTCLow level rain w/ strong development at T< 0oC Mixed phase development 1648-1652 UTCFirst Lightning 1655 UTCWhole mixed phase core falls by 1702 UTC, last CG lightning 1703 UTC, last IC 1705First CG 1702

  • QPE: Dual-Pol Selling point- Hybrid Polarimetric Rainfall AlgorithmsPremise: Combined variables account for clutter, DSD variability, and phase ID

  • Application for TVA : ARMOR Distributed Rainfall Products (AREPS)1-hr Accum.6-hr Accum.6-hr Basin MosaicText file for 6-hour accumulationAccumulated and Generated Every 5-minutesAlso transmitted to TVA:Basin 1-hour accumulationsGauge-location max, min and meanOperational Products: Image and TextWalter A. Petersen NASA MSFC VP-61

  • AREPS QPE Product Verification: ARMOR vs. TVA rain gauges (October 2007 June 2008) Point ComparisonsBias = -10% (-0.99 mm)Error = 12%Critical: For operational applications a constant monitoring of calibration maintains precision and accuracy of product.Walter A. Petersen NASA MSFC VP-61

  • CHILL: Rainfall AccumulationOptimization AlgorithmCHILL: Rainfall AccumulationNEXRAD Z-R AlgorithmColoradoCSU-CHILLRadarICE-Algorithm(R. Cifelli)

  • Tornado Debris (2/6/2008) KDP, RHOVH used to map debris traceTropical Cyclone Rita Tornadoes Size sorting and spatial separation of drops-microphysics feedbacks on dynamics?

  • Dual Pol radar is an outstanding tool for exploring and detecting many different manifestations of weather.

    When implemented in network form, it will be the next leap in both research and operational applications.

    Having said that.

  • We should also admit that dual-pol radar is not the pot of gold at the end of the precip remote sensing rainbow

  • It wont completely replace our best rain gauges

  • It wont solve all of our rainfall-related issues.

  • In the end..it will be the combination of Dual-pol radar with other observations and our ability to assimilate that ensemble of information into human and automated analysis/modeling systems that will be the key!DSDParticle types/phaseRain rate3-D Precip structure and evolutionMicrophysical feedbacks to dynamicsHydrology and water budgetsAnd more.W. Petersen, NASA-MSFC