YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • View Item
    •   YE&T Library
    • AMS
    • Monthly Weather Review
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Assimilation of ZDR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments

    Source: Monthly Weather Review:;2017:;volume( 145 ):;issue: 012::page 5033
    Author:
    Carlin, Jacob T.;Gao, Jidong;Snyder, Jeffrey C.;Ryzhkov, Alexander V.
    DOI: 10.1175/MWR-D-17-0103.1
    Publisher: American Meteorological Society
    Abstract: AbstractAchieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System?s cloud analysis is modified from its original reflectivity-based formulation to provide moisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivity structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted.
    • Download: (4.133Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Assimilation of ZDR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4246603
    Collections
    • Monthly Weather Review

    Show full item record

    contributor authorCarlin, Jacob T.;Gao, Jidong;Snyder, Jeffrey C.;Ryzhkov, Alexander V.
    date accessioned2018-01-03T11:03:09Z
    date available2018-01-03T11:03:09Z
    date copyright10/12/2017 12:00:00 AM
    date issued2017
    identifier othermwr-d-17-0103.1.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4246603
    description abstractAbstractAchieving accurate storm-scale analyses and reducing the spinup time of modeled convection is a primary motivation for the assimilation of radar reflectivity data. One common technique of reflectivity data assimilation is using a cloud analysis, which inserts temperature and moisture increments and hydrometeors deduced from radar reflectivity via empirical relations to induce and sustain updraft circulations. Polarimetric radar data have the ability to provide enhanced insight into the microphysical and dynamic structure of convection. Thus far, however, relatively little has been done to leverage these data for numerical weather prediction. In this study, the Advanced Regional Prediction System?s cloud analysis is modified from its original reflectivity-based formulation to provide moisture and latent heat adjustments based on the detection of differential reflectivity columns, which can serve as proxies for updrafts in deep moist convection and, subsequently, areas of saturation and latent heat release. Cycled model runs using both the original cloud analysis and above modifications are performed for two high-impact weather cases: the 19 May 2013 central Oklahoma tornadic supercells and the 25 May 2016 north-central Kansas tornadic supercell. The analyses and forecasts of convection qualitatively and quantitatively improve in both cases, including more coherent analyzed updrafts, more realistic forecast reflectivity structures, a better correspondence between forecast updraft helicity tracks and radar-derived rotation tracks, and improved frequency biases and equitable threat scores for reflectivity. Based on these encouraging results, further exploration of the assimilation of dual-polarization radar data into storm-scale models is warranted.
    publisherAmerican Meteorological Society
    titleAssimilation of ZDR Columns for Improving the Spinup and Forecast of Convective Storms in Storm-Scale Models: Proof-of-Concept Experiments
    typeJournal Paper
    journal volume145
    journal issue12
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-17-0103.1
    journal fristpage5033
    journal lastpage5057
    treeMonthly Weather Review:;2017:;volume( 145 ):;issue: 012
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian