Storm Surge Simulations of Hurricane Ike (2008): Its Impact in Louisiana and Texas

Forbes, Cristina; Luettich, Rick; Mattocks, Craig

doi:10.1061/41121(388)41

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The cones of uncertainty for all other forecast times are much larger. If one visually compares the forecast storm tracks of Hurricane Gustav with those of Hurricane Ike (Forbes et al 2010), the latter seems a more erratic, more difficult storm to forecast. However, the OFCL forecast track error computed by NHC is actually higher for Gustav (Beven and Kimberlain 2009) from forecast hours 12 to 96 than for Ike (Berg 2009).…”

Section: Forecast Simulations a Hurricane Gustavmentioning

confidence: 99%

A Retrospective Evaluation of the Storm Surge Produced by Hurricane Gustav (2008): Forecast and Hindcast Results

Forbes

Luettich

Mattocks

et al. 2010

Weather and Forecasting

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The evolution and convergence of modeled storm surge were examined using a high-resolution implementation of the Advanced Circulation Coastal Ocean and Storm Surge (ADCIRC) model for Hurricane Gustav (2008). The storm surge forecasts were forced using an asymmetric gradient wind model (AWM), directly coupled to ADCIRC at every time step and at every grid node. A total of 20 forecast advisories and best-track data from the National Hurricane Center (NHC) were used as input parameters into the wind model. Differences in maximum surge elevations were evaluated for ensembles comprised of the final 20, 15, 10, and 5 forecast advisories plus the best track. For this particular storm, the final 10-12 forecast advisories, encompassing the last 2.5-3 days of the storm's lifetime, give a reasonable estimate of the final storm surge and inundation. The results provide a detailed perspective of the variability in the storm surge due to variability in the meteorological forecast and how this changes as the storm approaches landfall. This finding is closely tied to the consistency and accuracy of the NHC storm track forecasts and the predicted landfall location and, therefore, cannot be generalized to all storms in all locations. Nevertheless, this first attempt to translate variability in forecast meteorology into storm surge variability provides useful insights for guiding the potential use of storm surge models for forecast purposes. Model skill was also evaluated for Hurricane Gustav by comparing observed water levels with hindcast modeled water levels forced by river flow, tides, and several sources of wind data. The AWM (which ingested best-track information from NHC) generated winds that were slightly higher than those from NOAA's Hurricane Research Division (HRD) H*Wind analyses and substantially greater than the North American Mesoscale (NAM) model. Surge obtained using the AWM more closely matched the observed water levels than that computed using H*Wind; however, this may be due to the neglect of the contribution of wave setup to the surge, especially in exposed areas. Several geographically distinct storm surge response regimes, some characterized by multisurge pulses, were identified and described.

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Section: Forecast Simulations a Hurricane Gustavmentioning

confidence: 99%

A Retrospective Evaluation of the Storm Surge Produced by Hurricane Gustav (2008): Forecast and Hindcast Results

Forbes

Luettich

Mattocks

et al. 2010

Weather and Forecasting

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The local impact of typhoons on economic activity in China: A view from outer space

Elliott

Strobl

Sun

2015

Journal of Urban Economics

136

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We examine the impact of typhoons on local economic activity in coastal China. To capture potential damages from an individual typhoon we use historical typhoon track data in conjunction with a detailed wind-field model. We then combine our damage proxy with satellite derived nightlight intensity data to construct a panel data set that allows us to estimate the impact of typhoons at a spatially highly disaggregated level (approx. 1km). Our results show that typhoons have a negative and significant, but short-term, impact on local activity -a typhoon that is estimated to destroy 50% of the property reduces local economic activity by 20% for that year. Over our period of analysis (1992-2010) total net economic losses are estimated to be in the region of US$28.34 billion. To assess the damage risk from future typhoons we use simulated probability distributions of typhoon occurrence and intensity and combine these with our estimated effects. Results suggest that expected annual losses are likely to be around US $0.54 billion.

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An Automated Operational Storm Surge Prediction System for the National Hurricane Center

Forbes¹,

Rhome²

2012

Estuarine and Coastal Modeling

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Storm Surge Simulations of Hurricane Ike (2008): Its Impact in Louisiana and Texas

Cited by 3 publications

References 19 publications

A Retrospective Evaluation of the Storm Surge Produced by Hurricane Gustav (2008): Forecast and Hindcast Results

A Retrospective Evaluation of the Storm Surge Produced by Hurricane Gustav (2008): Forecast and Hindcast Results

The local impact of typhoons on economic activity in China: A view from outer space

An Automated Operational Storm Surge Prediction System for the National Hurricane Center

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