Characteristics of Storm Surge Based on the Forward Speed of the Storm

2021 ◽  
Vol 114 (sp1) ◽  
Author(s):  
Young Hyun Park ◽  
Daeok Youn
Keyword(s):  
2021 ◽  
Vol 33 (5) ◽  
pp. 187-194
Author(s):  
Young Hyun Park ◽  
Woo-Sun Park

The damage caused by typhoons is gradually increasing due to the climate change recently. Hence, many studies have been conducted over a long period of time on various factors that determine the characteristics of storm surge, and most of relationships have been discovered. Because storm surge is complexly determined by various factors, it often show different results and draw different conclusions. For this reason, this study was conducted to understand the various characteristics of storm surge caused by changes in the forward speed of typhoons. This study was carried out with a numerical model, and the effect of forward speed could be analyzed by simplifying other factors as much as possible. When forward speed is increased, storm surges caused by typhoons tended to increase gradually. The storm surge showed a wide and gentle increase at a slow speed, but a narrow and steep one at a fast speed. In the case of the same forward speed, it was found that the storm surge was significantly influenced by the water depth of actual sea area. It was confirmed that the change in forward speed after passing Jeju Island did not significant affect on the storm surge in the south coast of Korea.


2021 ◽  
Vol 9 (9) ◽  
pp. 963
Author(s):  
Abram Musinguzi ◽  
Madinah Shamsu ◽  
Muhammad K. Akbar ◽  
Jason G. Fleming

In this study, it is demonstrated that hurricane wind intensity, forward speed, pressure, and track play an important role on the generation and propagation of coastal storm surges. Hurricane Irma, which heavily impacted the entire Florida peninsula in 2017, is used to study the storm surge sensitivity to varying storm characteristics. Results show that the west coast experiences a negative surge due to offshore wind of the approaching storm, but the positive surge returns after the hurricane eye passes over a location and wind became onshore. In the west coast peak, surges are intensified by an increase in onshore wind intensity and forward speed. In the Florida Keys, peak surges are intensified by an increase in wind intensity, a decrease in forward speed and a decrease in pressure. In southeast and east Florida, peak surges are intensified by decrease in pressure, although overall surges are less significant as the water can slide along the coastline. In the recessed coastline of Georgia-Carolinas, maximum surge is elevated by an increase in onshore wind intensity. Shifting the track westward increases peak surges on the west coast, while shifting the track eastward increases peak surge on the east coast. The results demonstrate a new understanding about the sensitivity of surge to varying parametric conditions and the importance of considering changes in the coastline orientation in storm surge predictions.


Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3326
Author(s):  
Yu-Lin Tsai ◽  
Tso-Ren Wu ◽  
Chuan-Yao Lin ◽  
Simon C. Lin ◽  
Eric Yen ◽  
...  

This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan’s forward speed is increased by 10%.


2020 ◽  
Author(s):  
Alvaro Prida ◽  
Manuel Andres Diaz Loaiza ◽  
Jeremy Bricker ◽  
Oswaldo Morales ◽  
Remy Meynadier ◽  
...  

<p><strong>Bayesian Networks for storm surge estimation in Mississippi (US)</strong></p><p><strong>A. Prida<sup>1,</sup> A. Diaz Loaiza<sup>1</sup>, J. Bricker<sup>1</sup>, R. Meynadier<sup>2</sup>, O. Morales-Napoles<sup>1</sup>, T. Duong<sup>3</sup>, R. Ranasinghe<sup>3</sup>, A. Luijendijk<sup>1</sup></strong></p><p>The unprecedented damage due to flood caused by hurricanes like Katrina (2005) has reinforced the interest of the hydraulic community to improve the storm surge estimation for the North Gulf of Mexico. Very high-resolution hydrodynamic models have been traditionally used for this end. However, these models are computationally very expensive. In this paper, a Bayesian Network (BN) is built to estimate storm surge at the coastal areas of Mississippi. A catalogue of HURDAT2 historical hurricanes is simulated in Delft3D FM to generate a surge data base that is used for the training of the Bayesian Network. The storm surge obtained from Delft3D FM is validated against observations recorded during a past historical event. The landfall location, the maximum wind speed, the forward speed and the forward direction of the hurricane at landfall are the other variables considered in the Bayesian Network. The Bayesian Network is validated by inferring values from past historical events in the model and comparing the modeled surge to observations.</p>


2019 ◽  
Vol 137 ◽  
pp. 1-19 ◽  
Author(s):  
Ajimon Thomas ◽  
JC Dietrich ◽  
TG Asher ◽  
M Bell ◽  
BO Blanton ◽  
...  
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