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Probabilistic Hurricane Track Generation for Storm Surge Prediction.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Probabilistic Hurricane Track Generation for Storm Surge Prediction./
Author:	Smith, Jessica L.
Description:	1 online resource (38 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Subject:	Physical oceanography. -
Online resource:	click for full text (PQDT)
ISBN:	9780355596373

Probabilistic Hurricane Track Generation for Storm Surge Prediction.
Smith, Jessica L.

Probabilistic Hurricane Track Generation for Storm Surge Prediction. - 1 online resource (38 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--The University of North Carolina at Chapel Hill, 2017.

Includes bibliographical references

Storm surge is a major source of devastation from hurricane events, and as such it is important to understand the uncertainty associated with storm surge forecasting. Uncertainty in predicted storm surge can be quantified by synthesizing a suite of probable storm tracks that are based on errors in predictions of previous hurricanes and computing storm surge with a suitable model. Davis et al. (2010) developed an approach to track generation based on cross-track errors in the official National Hurricane Center (NHC) forecast tracks. In this work their methods are extended to include along-track and maximum wind speed errors. Errors in the NHC forecasts are used to compute probability distributions that are sampled to generate synthetic tracks on either side of the official forecast track, with each track having an equal likelihood of occurrence. Storm surge for each track is then computed with the ADCIRC model (Westerink et al., 2008) to generate probability of exceedance maps and worst-case potential storm surge (maximum of maximums). The ideal error distribution sampling number for the forecast experiments used in this study for each dimension are 27 in the cross-track dimension, 9 in the along-track dimension, and 9 in the intensity dimension.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355596373Subjects--Topical Terms:

1178843
Physical oceanography.
Index Terms--Genre/Form:

554714
Electronic books.

Probabilistic Hurricane Track Generation for Storm Surge Prediction.
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100 1 $a Smith, Jessica L. $3 1186207
245 1 0 $a Probabilistic Hurricane Track Generation for Storm Surge Prediction.
264 0 $c 2017
300 $a 1 online resource (38 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Rick Luettich.
502 $a Thesis (M.S.)--The University of North Carolina at Chapel Hill, 2017.
504 $a Includes bibliographical references
520 $a Storm surge is a major source of devastation from hurricane events, and as such it is important to understand the uncertainty associated with storm surge forecasting. Uncertainty in predicted storm surge can be quantified by synthesizing a suite of probable storm tracks that are based on errors in predictions of previous hurricanes and computing storm surge with a suitable model. Davis et al. (2010) developed an approach to track generation based on cross-track errors in the official National Hurricane Center (NHC) forecast tracks. In this work their methods are extended to include along-track and maximum wind speed errors. Errors in the NHC forecasts are used to compute probability distributions that are sampled to generate synthetic tracks on either side of the official forecast track, with each track having an equal likelihood of occurrence. Storm surge for each track is then computed with the ADCIRC model (Westerink et al., 2008) to generate probability of exceedance maps and worst-case potential storm surge (maximum of maximums). The ideal error distribution sampling number for the forecast experiments used in this study for each dimension are 27 in the cross-track dimension, 9 in the along-track dimension, and 9 in the intensity dimension.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Physical oceanography. $3 1178843
655 7 $a Electronic books. $2 local $3 554714
690 $a 0415
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Chapel Hill. $b Marine Sciences. $3 1186208
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681932 $z click for full text (PQDT)