YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Applied Meteorology and Climatology
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Applied Meteorology and Climatology
    • 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

    Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

    Source: Journal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 008::page 2317
    Author:
    Griffin, Sarah M.;Otkin, Jason A.;Rozoff, Christopher M.;Sieglaff, Justin M.;Cronce, Lee M.;Alexander, Curtis R.;Jensen, Tara L.;Wolff, Jamie K.
    DOI: 10.1175/JAMC-D-17-0004.1
    Publisher: American Meteorological Society
    Abstract: AbstractIn this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-?m brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.
    • Download: (2.583Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4246235
    Collections
    • Journal of Applied Meteorology and Climatology

    Show full item record

    contributor authorGriffin, Sarah M.;Otkin, Jason A.;Rozoff, Christopher M.;Sieglaff, Justin M.;Cronce, Lee M.;Alexander, Curtis R.;Jensen, Tara L.;Wolff, Jamie K.
    date accessioned2018-01-03T11:01:40Z
    date available2018-01-03T11:01:40Z
    date copyright7/6/2017 12:00:00 AM
    date issued2017
    identifier otherjamc-d-17-0004.1.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4246235
    description abstractAbstractIn this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-?m brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.
    publisherAmerican Meteorological Society
    titleSeasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification
    typeJournal Paper
    journal volume56
    journal issue8
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-17-0004.1
    journal fristpage2317
    journal lastpage2334
    treeJournal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 008
    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