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    On the Limits of Estimating the Maximum Wind Speeds in Hurricanes

    Source: Monthly Weather Review:;2014:;volume( 142 ):;issue: 008::page 2814
    Author:
    Nolan, David S.
    ,
    Zhang, Jun A.
    ,
    Uhlhorn, Eric W.
    DOI: 10.1175/MWR-D-13-00337.1
    Publisher: American Meteorological Society
    Abstract: his study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10%?20%. Even an unusually large number of anemometers (e.g., 3?5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5%?10%. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3%?5%.
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      On the Limits of Estimating the Maximum Wind Speeds in Hurricanes

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    contributor authorNolan, David S.
    contributor authorZhang, Jun A.
    contributor authorUhlhorn, Eric W.
    date accessioned2017-06-09T17:31:44Z
    date available2017-06-09T17:31:44Z
    date copyright2014/08/01
    date issued2014
    identifier issn0027-0644
    identifier otherams-86766.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230360
    description abstracthis study uses an observing system simulation experiment (OSSE) approach to test the limitations of even nearly ideal observing systems to capture the peak wind speed occurring within a tropical storm or hurricane. The dataset is provided by a 1-km resolution simulation of an Atlantic hurricane with surface wind speeds saved every 10 s. An optimal observing system consisting of a dense field of anemometers provides perfect measurements of the peak 1-min wind speed as well as the average peak wind speed. Suboptimal observing systems consisting of a small number of anemometers are sampled and compared to the truth provided by the optimal observing system. Results show that a single, perfect anemometer experiencing a direct hit by the right side of the eyewall will underestimate the actual peak intensity by 10%?20%. Even an unusually large number of anemometers (e.g., 3?5) experiencing direct hits by the storm together will underestimate the peak wind speeds by 5%?10%. However, the peak winds of just one or two anemometers will provide on average a good estimate of the average peak intensity over several hours. Enhancing the variability of the simulated winds to better match observed winds does not change the results. Adding observational errors generally increases the reported peak winds, thus reducing the underestimates. If the average underestimate (negative bias) were known perfectly for each case, it could be used to correct the wind speeds, leaving only mean absolute errors of 3%?5%.
    publisherAmerican Meteorological Society
    titleOn the Limits of Estimating the Maximum Wind Speeds in Hurricanes
    typeJournal Paper
    journal volume142
    journal issue8
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-13-00337.1
    journal fristpage2814
    journal lastpage2837
    treeMonthly Weather Review:;2014:;volume( 142 ):;issue: 008
    contenttypeFulltext
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