A real-time, event-triggered storm surge forecasting system for the state of North Carolina

Mattocks, Craig; Forbes, Cristina

doi:10.1016/j.ocemod.2008.06.008

Cited by 152 publications

(110 citation statements)

References 17 publications

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“…The basis of this study was to assess a baseline skill level of SLOSH and compare it to its latest improvements demonstrated by the inclusion of tidal constituents in SLOSH. Implementing gridded wind fields, an improved parametric wind model [12], and a combination thereof are planned upgrades to SLOSH.…”

Section: Discussionmentioning

confidence: 99%

“…An improved version of the Mattocks and Forbes [12] asymmetric parametric wind model, GWAVA (Gradient Wind Asymmetric Vortex Algorithm), is currently being incorporated into SLOSH. Blending the near-field winds from this more advanced parametric wind model with gridded far-field winds from the GFS or other numerical weather prediction models will potentially improve storm surge prediction by providing more realistic multi-scale wind forcing at the ocean surface and its hydrodynamic response.…”

Section: Discussionmentioning

confidence: 99%

“…Forecasts are run using storm track information that includes the latitude and longitude of the storm's center, intensity (maximum sustained 1-min wind speed), pressure drop and radius of maximum winds from NHC's Best Track operational data and parameters from all of the model information available to the Hurricane Specialists at NHC. The SLOSH parametric wind model is used to ensure that the parameters in the SLOSH wind formulation are consistent with those in the model guidance, i.e., the resulting wind speed in the SLOSH wind model is in accordance to the NHC's Best Track and the model guidance intensity, in a manner similar to other storm surge forecast systems [12,13].…”

Section: Forecastsmentioning

confidence: 99%

See 2 more Smart Citations

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

Forbes

Rhome

Mattocks

et al. 2014

JMSE

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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 OPEN ACCESS J. Mar. Sci. Eng. 2014, 2 438 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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Forecastsmentioning

confidence: 99%

See 1 more Smart Citation

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

Forbes

Rhome

Mattocks

et al. 2014

JMSE

Self Cite

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“…However, hurricanes often have asymmetric wind field distributions. The Asymmetric Holland Model (AHM) uses either R64, R50, or R34 distance to strongest wind isotach (i.e., 64 kt, 50 kt, or 34 kt away from the eye) to solve for a different maximum radius in each storm quadrant (NE, NW, SW, SE) [22,33]. In other words, AHM uses the single strongest isotach in each quadrant.…”

Section: Model Detailsmentioning

confidence: 99%

Effect of Bottom Friction, Wind Drag Coefficient, and Meteorological Forcing in Hindcast of Hurricane Rita Storm Surge Using SWAN + ADCIRC Model

Akbar

Kanjanda

Musinguzi

2017

JMSE

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An evaluation of the effect of bottom friction, wind drag coefficient, and meteorological forcing is conducted using a tightly coupled wave and circulation model, SWAN + ADCIRC (i.e., Simulating WAves Nearshore + ADvanced CIRCulation), to hindcast the storm surge of Hurricane Rita (2005). Wind drag coefficient formulations of Powell, Zijlema, and Peng & Li are used to calculate wind stresses. Bottom friction and wind drag coefficients are systematically increased and decreased to quantify their impacts on the hindcast. Different meteorological forcing options are applied to study the effect of wind fields on storm surge development and propagation. Simulated water levels are compared with observed data collected from about 150 locations. It is evident that a lower bottom friction causes higher and faster surge propagation, and earlier arrival of inundation peak at locations far from the land fall. Drag coefficients of Powell, with or without a cap of 0.002, and Zijlema produce similar results, while that of Peng & Li slightly overpredicted the surge. Wind fields may cause overprediction or underprediction of the surge, depending on the choice of the wind model. A good agreement is found between Zijlema's findings and this study; that simultaneously decreasing or increasing both bottom friction and wind drag essentially provides the same hindcast results.

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“…To fully convey surge hazard requires both a reliable estimate of surge elevation and a measure of expected damages (Irish and Resio 2010). Significant advances in physics-based surge modeling over the last several decades now make it possible to accurately simulate hurricane surge at high resolution (e.g., Westerink et al 2008, Mattocks andForbes 2008). However, real-time simulation with high-resolution numerical models is limited by computational requirements, and, when these models are employed in a forecast mode, the number of storm possibilities considered, namely track and intensity combinations, is severely limited.…”

Section: Introductionmentioning

confidence: 99%

Rapid Probabilistic Hurricane Surge and Damage Forecasting Using Surge Response Functions

Irish

Ferreira

Olivera

et al. 2011

Int. Conf. Coastal. Eng.

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In this paper, a joint probability approach is used with scaling laws for hurricane surge to rapidly develop probabilistic based hurricane surge and damage forecasts. The method presented is demonstrated along the Texas, USA coastline for Hurricane Ike, which made landfall in September of 2008. The probabilistic approach presented here is both accurate and fast, with a single surge and percent damage forecast taking less than one minute while representing more than 170,000 distinct hurricane possibilities.

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A real-time, event-triggered storm surge forecasting system for the state of North Carolina

Cited by 152 publications

References 17 publications

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

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

Effect of Bottom Friction, Wind Drag Coefficient, and Meteorological Forcing in Hindcast of Hurricane Rita Storm Surge Using SWAN + ADCIRC Model

Rapid Probabilistic Hurricane Surge and Damage Forecasting Using Surge Response Functions

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