東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Probabilistic Hurricane Track Genera...

Smith, Jessica L.

Linked to FindBook

Google Book

Amazon

博客來

Probabilistic Hurricane Track Generation for Storm Surge Prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Probabilistic Hurricane Track Generation for Storm Surge Prediction./
Author:	Smith, Jessica L.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	38 p.
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Physical oceanography. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681932
ISBN:	9780355596373

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

Probabilistic Hurricane Track Generation for Storm Surge Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 38 p.

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

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

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.

ISBN: 9780355596373Subjects--Topical Terms:

3168433
Physical oceanography.

Probabilistic Hurricane Track Generation for Storm Surge Prediction.
LDR:02174nmm a2200289 4500 001 2158868
005 20180618102643.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355596373
035 $a (MiAaPQ)AAI10681932
035 $a (MiAaPQ)unc:17462
035 $a AAI10681932
040 $a MiAaPQ $c MiAaPQ
100 1 $a Smith, Jessica L. $3 3346717
245 1 0 $a Probabilistic Hurricane Track Generation for Storm Surge Prediction.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 38 p.
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.
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.
590 $a School code: 0153.
650 4 $a Physical oceanography. $3 3168433
690 $a 0415
710 2 $a The University of North Carolina at Chapel Hill. $b Marine Sciences. $3 1670010
773 0 $t Masters Abstracts International $g 57-02(E).
790 $a 0153
791 $a M.S.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681932