YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Hydrometeorology
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Hydrometeorology
    • 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

    Global Assessment of Atmospheric River Prediction Skill

    Source: Journal of Hydrometeorology:;2018:;volume 019:;issue 002::page 409
    Author:
    DeFlorio, Michael J.
    ,
    Waliser, Duane E.
    ,
    Guan, Bin
    ,
    Lavers, David A.
    ,
    Ralph, F. Martin
    ,
    Vitart, Frédéric
    DOI: 10.1175/JHM-D-17-0135.1
    Publisher: American Meteorological Society
    Abstract: AbstractAtmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7?10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%?20% at a 7-day lead during boreal winter relative to boreal summer. AR hit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Niño?Southern Oscillation (ENSO) conditions and at 7- and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific?North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Niño + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Niña + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill.
    • Download: (3.447Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Global Assessment of Atmospheric River Prediction Skill

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4260773
    Collections
    • Journal of Hydrometeorology

    Show full item record

    contributor authorDeFlorio, Michael J.
    contributor authorWaliser, Duane E.
    contributor authorGuan, Bin
    contributor authorLavers, David A.
    contributor authorRalph, F. Martin
    contributor authorVitart, Frédéric
    date accessioned2019-09-19T10:01:53Z
    date available2019-09-19T10:01:53Z
    date copyright1/10/2018 12:00:00 AM
    date issued2018
    identifier otherjhm-d-17-0135.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4260773
    description abstractAbstractAtmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7?10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%?20% at a 7-day lead during boreal winter relative to boreal summer. AR hit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Niño?Southern Oscillation (ENSO) conditions and at 7- and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific?North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Niño + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Niña + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill.
    publisherAmerican Meteorological Society
    titleGlobal Assessment of Atmospheric River Prediction Skill
    typeJournal Paper
    journal volume19
    journal issue2
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-17-0135.1
    journal fristpage409
    journal lastpage426
    treeJournal of Hydrometeorology:;2018:;volume 019:;issue 002
    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