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    A Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

    Source: Monthly Weather Review:;2018:;volume 146:;issue 007::page 2221
    Author:
    Annane, Bachir
    ,
    McNoldy, Brian
    ,
    Leidner, S. Mark
    ,
    Hoffman, Ross
    ,
    Atlas, Robert
    ,
    Majumdar, Sharanya J.
    DOI: 10.1175/MWR-D-17-0240.1
    Publisher: American Meteorological Society
    Abstract: AbstractIn preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA?s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.
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      A Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4261217
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    • Monthly Weather Review

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    contributor authorAnnane, Bachir
    contributor authorMcNoldy, Brian
    contributor authorLeidner, S. Mark
    contributor authorHoffman, Ross
    contributor authorAtlas, Robert
    contributor authorMajumdar, Sharanya J.
    date accessioned2019-09-19T10:04:21Z
    date available2019-09-19T10:04:21Z
    date copyright5/23/2018 12:00:00 AM
    date issued2018
    identifier othermwr-d-17-0240.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4261217
    description abstractAbstractIn preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA?s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.
    publisherAmerican Meteorological Society
    titleA Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors
    typeJournal Paper
    journal volume146
    journal issue7
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-17-0240.1
    journal fristpage2221
    journal lastpage2236
    treeMonthly Weather Review:;2018:;volume 146:;issue 007
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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