J. Mar. Sci. Eng. 2014, 2(2), 437-476; doi:10.3390/jmse2020437
Article

Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model

1,†,* email, 1,†email, 1,†email and 2,†email
Received: 4 December 2013; in revised form: 28 April 2014 / Accepted: 30 April 2014 / Published: 19 May 2014
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012) are carried out using the National Weather Service (NWS) Sea Lakes and Overland Surges from Hurricanes (SLOSH) storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA) tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS), that the SLOSH-simulated water levels at 71% (89%) of the data measurement locations have less than 20% (30%) relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.
Keywords: storm; surge; hurricane; Sandy; inundation; tides; high; water; marks; sensors
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MDPI and ACS Style

Forbes, C.; Rhome, J.; Mattocks, C.; Taylor, A. Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model. J. Mar. Sci. Eng. 2014, 2, 437-476.

AMA Style

Forbes C, Rhome J, Mattocks C, Taylor A. Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model. Journal of Marine Science and Engineering. 2014; 2(2):437-476.

Chicago/Turabian Style

Forbes, Cristina; Rhome, Jamie; Mattocks, Craig; Taylor, Arthur. 2014. "Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model." J. Mar. Sci. Eng. 2, no. 2: 437-476.

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