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

Journal of Marine Science and Engineering

卷:2

Arthur Taylor¹  Jamie Rhome²  Cristina Forbes²  Craig Mattocks² 

[1] Meteorological Development Laboratory, National Weather Service, National Oceanic and Atmospheric Administration, 1325 East West Highway, Silver Spring, MD 20910, USA;

[2] National Hurricane Center, National Weather Service, National Oceanic and Atmospheric Administration, 11691 SW 17th St. Miami, FL 33165, USA;

关键词: storm;    surge;    hurricane;    Sandy;    inundation;    tides;    high;    water;    marks;    sensors;   

DOI  :  10.3390/jmse2020437

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown