Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac

Dietrich, J. C.; Muhammad, Auwal; Curcic, Milan; Fathi, Arash; Dawson, Clint; Chen, Shuyi S.; Luettich, Rick

doi:10.1061/(asce)ww.1943-5460.0000419

Cited by 58 publications

(50 citation statements)

References 61 publications

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“…It is designed as a multimodel system with the flexibility of exchanging individual model components for the atmosphere, waves, ocean, land, and sea ice. The model has been used to study the role of Stokes drift in surface transport (Curcic et al, ), the impacts of coupling on boundary layer structure (Zhu et al, ) and storm surge prediction (Dietrich et al, ) in Hurricane Isaac in 2012, and the influence of atmospheric forcing on the transport in the GoM on diurnal and seasonal scales (Judt et al, ). Here, the system consists of fully coupled atmosphere, surface wave, and ocean circulation models.…”

Section: Methodsmentioning

confidence: 99%

Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico

et al. 2019

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During the Lagrangian submesoscale experiment (LASER), 1,000 drifters were launched to sample the surface ocean flow in the northern Gulf of Mexico. Due to half a dozen strong winter storms, about 40% of the drifters lost their drogue. This unintended situation facilitated documentation of both near‐surface (5 cm) and deeper (60 cm) flows. These depths are relevant to transport of oil spills, as well as marine debris, such as microplastics, a rapidly growing environmental problem. Here, we improve the surface Lagrangian current prediction by combining a state‐of‐the‐art ocean forecast model with wind and wave data. The ocean surface velocities are obtained from the Navy Coordinate Ocean Model at 1‐km horizontal resolution, while the wind and wave fields are from the Unified Wave INterface Coupled Model coupled atmosphere‐wave‐ocean model. Two Lagrangian parameterizations are tested: one is based on Ekman dynamics, and the other directly on the surface winds. LASER data set is then used to assess the performance of these formulations, as a function of wind/wave conditions, as well as geographic region. It is found that incorporation of wind and wave data into the ocean circulation model can lead to major prediction improvement, by reducing the average 2‐day separation from the modeled and real LASER trajectories by a factor ranging from 1.4 to 4.9. This is a significant improvement for applications, where a rapid deployment of assets is needed, such as oil spill response, or other tracking problems.

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Section: Methodsmentioning

confidence: 99%

Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico

et al. 2019

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“…In general, parametric wind models approximate the surface wind field as the sum of the background wind speed of the atmosphere, V b , and the axisymmetric wind speed, V, of the storm. The definition used for each of these components differentiates various parametric wind field models that have been developed, e.g., [13,16,23,24]. In the SLOSH model, the background wind field is modeled as a radially variable fraction of the storm's translational speed, V s , and the axisymmetric wind profile is modeled as a radially variable fraction of the storm's maximum wind speed, V max .…”

Section: Introductionmentioning

confidence: 99%

The Effect of the Surface Wind Field Representation in the Operational Storm Surge Model of the National Hurricane Center

Mayo

Lin

2019

Atmosphere

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The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is the operational storm surge model of the National Hurricane Center (NHC). Previous studies have found that the SLOSH model estimates storm surges with an accuracy of ±20%. In this study, through hindcasts of historical storms, we assess the accuracy of the SLOSH model for four coastal regions in the Northeastern United States. We investigate the potential to improve this accuracy through modification of the wind field representation. We modify the surface background wind field, the parametric wind profile, and the maximum wind speed based on empirical, physical, and observational data. We find that on average the SLOSH model underestimates maximum storm surge heights by 22%. The modifications to the surface background wind field and the parametric wind profile have minor impacts; however, the effect of the modification to maximum wind speed is significant—it increases the variance in the SLOSH model estimates of maximum storm surges, but improves its accuracy overall. We recommend that observed values of maximum wind speed be used in SLOSH model simulations when possible.

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“…Atmospheric forcing is the principal driver of storm surge [10,11]. In storm surge models, atmospheric forcing is provided in the form of surface pressure and near-surface wind fields from various sources and configurations.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters needed for implementation in the parametric vortex model include the storm location, minimum central pressure, maximum wind velocity and radius of maximum winds [10,11]. These parameters are available as part of the National Hurricane Center (NHC) forecast advisories issued every 6 h throughout the storm's lifetime.…”

Section: Introductionmentioning

confidence: 99%

“…The sensitivity of storm surge to different meteorological forcing types for the case of Hurricane Isaac (2012) in the Gulf of Mexico has also been studied. Dietrich et al [10] showed that provided availability of accurate forecast advisories, in a hindcast scenario, a parametric vortex model results in reasonable storm surge estimates. Bennett and Mulligan [17] compared the effect of wind fields from two parametric models and a 3D atmospheric model on the generation of surface waves by Hurricane Sandy and concluded that the 3D atmospheric model, which has the best description of the storm wind field, is most suitable for their assessment.…”

Section: Introductionmentioning

confidence: 99%

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Simulating Storm Surge Impacts with a Coupled Atmosphere-Inundation Model with Varying Meteorological Forcing

Ramos-Valle

Curchitser

Bruyère

et al. 2018

JMSE

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Storm surge events have the potential to cause devastating damage to coastal communities. The magnitude of their impacts highlights the need for increased accuracy and real-time forecasting and predictability of storm surge. In this study, we assess two meteorological forcing configurations to hindcast the storm surge of Hurricane Sandy, and ultimately support the improvement of storm surge forecasts. The Weather Research and Forecasting (WRF) model is coupled to the ADvanced CIRCulation Model (ADCIRC) to determine water elevations. We perform four coupled simulations and compare storm surge estimates resulting from the use of a parametric vortex model and a full-physics atmospheric model. One simulation is forced with track-based meteorological data calculated from WRF, while three simulations are forced with the full wind and pressure field outputs from WRF simulations of varying resolutions. Experiments were compared to an ADCIRC simulation forced by National Hurricane Center best track data, as well as to station observations. Our results indicated that given accurate meteorological best track data, a parametric vortex model can accurately forecast maximum water elevations, improving upon the use of a full-physics coupled atmospheric-surge model. In the absence of a best track, atmospheric forcing in the form of full wind and pressure field from a high-resolution atmospheric model simulation prove reliable for storm surge forecasting.

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Sensitivity of Storm Surge Predictions to Atmospheric Forcing during Hurricane Isaac

Cited by 58 publications

References 61 publications

Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico

Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico

The Effect of the Surface Wind Field Representation in the Operational Storm Surge Model of the National Hurricane Center

Simulating Storm Surge Impacts with a Coupled Atmosphere-Inundation Model with Varying Meteorological Forcing

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