Low frequency water level correction in storm surge models using data assimilation

Asher, Taylor G.; Luettich, Richard A.; Fleming, Jason G.; Blanton, Brian

doi:10.1016/j.ocemod.2019.101483

Cited by 22 publications

(21 citation statements)

References 61 publications

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“…Global NWP models lack the resolution to capture these gradients, and so parametric models of storm wind and pressure fields are often employed. Since meteorological forcing far from the storm center can have a sizeable influence on water levels, far-field input can be supplied from another meteorological model (Asher et al 2019;Morey et al 2006). The generalized asymmetric Holland model (GAHM) developed by Gao (2018) was used in this study to transform NHC tropical cyclone best track and forecasts into a parametric wind field.…”

Section: Parametric Tropical Cyclone Representationmentioning

confidence: 99%

“…A limitation of surge guidance is the description of the farfield winds, which can cause a water level setup prior to the arrival of tropical storm strength winds, especially in regions with wide and shallow continental shelves (Asher et al 2019). To this end, we explored the performance gains that could have been achieved during Hurricane Michael if our scenario-based framework was expanded to include far-field winds.…”

Section: A Barriers To Operationalizationmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Simulated Storm Surge Predictions during Hurricane Michael (2018)

Bilskie

Asher

Miller

et al. 2022

Weather and Forecasting

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Storm surge caused by tropical cyclones can cause overland flooding and lead to loss of life while damaging homes, businesses, and critical infrastructure. In 2018, Hurricane Michael made landfall near Mexico Beach, FL, on 10 October with peak wind speeds near 71.9 m s-1 (161 mph) and storm surge over 4.5 m NAVD88. During Hurricane Michael, water levels and waves were predicted near real-time using a deterministic, depth-averaged, high-resolution ADCIRC+SWAN model of the northern Gulf of Mexico. The model was forced with an asymmetrical parametric vortex model (GAHM) based on Michael's National Hurricane Center (NHC) forecast track and strength. The authors report errors between simulated and observed water level time-series, peak water level, and timing of peak for NHC Advisories. Forecasts of water levels were within 0.5 m of observations, and the timing of peak water levels was within 1 hr as early as 48 hr before Michaels eventual landfall. We also examined the effect of adding far-field meteorology in our TC vortex model for use in real-time forecasts. In general, we found that including far-field meteorology by blending the TC vortex with a basin-scale NWP product improved water level forecasts. However, we note that divergence between the NHC forecast track and the forecast track of the meteorological model supplying the far-field winds represents a potential limitation to operationalizing a blended wind field surge product. The approaches and data reported herein provide a transparent assessment of water level forecasts during Hurricane Michael and highlight potential future improvements for more accurate predictions.

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Section: Parametric Tropical Cyclone Representationmentioning

confidence: 99%

Section: A Barriers To Operationalizationmentioning

confidence: 99%

Real-Time Simulated Storm Surge Predictions during Hurricane Michael (2018)

Bilskie

Asher

Miller

et al. 2022

Weather and Forecasting

View full text Add to dashboard Cite

show abstract

“…(2018) used an adjoint-free 4D-Var method to estimate wind drag coefficient and improve storm surge forecasts. Regarding model structural uncertainty, Asher et al. (2019) developed an optimal interpolation-based DA scheme to correct WL residuals arising from physical processes that are not fully resolved in HD models (e.g., steric variations, baroclinicity, and major ocean currents).…”

Section: Quantifying and Reducing Uncertaintiesmentioning

confidence: 99%

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Abbaszadeh¹,

Muñoz²,

Moftakhari³

et al. 2022

iScience

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“…Reasons are manifold. These include uncertainties in atmospheric forcing (Cardone and Cox, 2009;Dietrich et al, 2018;Mayo and Lin, 2019;Abdolali et al, 2021); a lack of available high-quality and highresolution bathymetric data in shallow areas as well as insufficient grid resolution to resolve the topo-bathy in complex coastal areas (Hell et al, 2012;Jacob and Stanev, 2021;Acosta-Morel et al, 2021); a lack of available high-quality water level records during past tropical storms for model calibration (Asher et al, 2019); under-or misrepresentations of physical processes pertaining to flow and to atmosphere-ocean momentum exchange under strong wind regime and in shallow waters (Olabarrieta et al, 2012), a lack of coupling of different model components which have a direct effect on each other (i.e. atmospheric forcing, storm surge, wave and hydrology; Ma et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations

Rahman

Moghimi

et al. 2022

Nat Hazards

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Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018, and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms, and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15 th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies.Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding.

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Low frequency water level correction in storm surge models using data assimilation

Cited by 22 publications

References 61 publications

Real-Time Simulated Storm Surge Predictions during Hurricane Michael (2018)

Real-Time Simulated Storm Surge Predictions during Hurricane Michael (2018)

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations

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