Impact of Tropical Cyclone Landfall Angle on Storm Surge Along the Mid‐Atlantic Bight

Ramos‐Valle, Alexandra N.; Curchitser, Enrique N.; Bruyère, Cindy L.

doi:10.1029/2019jd031796

Cited by 21 publications

(21 citation statements)

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“…In this section, we briefly describe the atmospheric and hydrodynamic data sets used to train the ANN model. The data sets are publicly available through the DesignSafe‐CI repository, published as Ramos‐Valle (2019). We also discuss the design of the ANN model, the metrics used to evaluate the storm surge predictions, and the methods used to analyze the neural network and the individual contributions of the input variables in accurately forecasting storm surge.…”

Section: Methodsmentioning

confidence: 99%

“…Storm surge magnitude is a function of various TC characteristics and physical parameters, including the cyclone intensity (Weisberg & Zheng, 2006), size (Irish et al., 2008), and forward speed (Hussain et al., 2017; Rego & Li, 2009; Thomas et al., 2019). Recent studies have also shown the dependency of storm surge on cyclone landfall angle and impact location (Bloemendaal et al., 2019; Ramos‐Valle et al., 2020). In this study, we explore questions such as: (a) What is the relative importance of each cyclone parameter in determining storm surge?…”

Section: Introductionmentioning

confidence: 99%

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Implementation of an Artificial Neural Network for Storm Surge Forecasting

et al. 2021

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Accurate and timely storm surge forecasts are essential during tropical cyclone events in order to assess the magnitude and location of the impacts. Coupled ocean‐atmosphere dynamical models provide accurate measures of storm surge but remain too computationally expensive to run for real‐time forecasting purposes. Therefore, it is common to utilize a parametric vortex model, implemented within a hydrodynamic model, which decreases computational time at the expense of forecast accuracy. Recently, data‐driven neural networks are being implemented as an alternative due to their combined efficiency and high accuracy. This work seeks to examine how an artificial neural network (ANN) can be used to make accurate storm surge predictions, and explores the added value of using a recurrent neural network (RNN). In particular, it is concerned with determining the parameters needed to successfully implement a neural network model for the Mid‐Atlantic Bight region. The neural network models were trained with modeled data resulting from coupling of the Hybrid Weather Research and Forecasting cyclone model (HWCM) and the Advanced Circulation Model. An ensemble of synthetic, but physically plausible, cyclones were simulated using the HWCM and used as input for the hydrodynamic model. Tests of the ANN were conducted to investigate the optimal lead‐time configuration of the input data and the neural network architecture needed to minimize storm surge forecast errors. Results highlight the accuracy of the ANN in forecasting moderate storm surge levels, while indicating a deficiency in capturing the magnitude of the peak values, which is improved in the implementation of the RNN.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Neural Network for Storm Surge Forecasting

et al. 2021

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“…Our results may be influenced by moderate differences in tracks of the CTRL and PGW simulations, which can have important implications for landfall location and angle of approach. Perpendicularly landfalling storms tend to produce more wide spread surges than those that make landfall diagonally, however this is rare (Hall and Sobel, 2013;Ramos-Valle et al, 2020). Also, in some areas, track fluctuations lead to landfalls of areas with different physical characteristics of the coastline and shelf that can either amplify or diminish surge magnitude (Azam et al, 2004;Mori et al, 2014).…”

Section: Influence Of Storm Characteristicsmentioning

confidence: 99%

“…Historically, it has been assumed that storm surge is directly and solely related to hurricane intensity, i.e., maximum sustained wind speed and minimum central pressure. However, over time, historical evidence and research have shown that other meteorological properties of hurricanes including size, translation speed, and angle of approach, as well as geophysical characteristics of impacted areas (e.g., coastal geography, topography, and bottom friction) have significant impacts on the generation and propagation of storm surges (Irish et al, 2008;Li, 2009, 2010;Resio et al, 2009;Irish and Resio, 2010;Weaver and Slinn, 2010;Mayo et al, 2014;Needham and Keim, 2014;Akbar et al, 2017;Fossell et al, 2017;Thomas et al, 2019;Ramos-Valle et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States

Camelo

Mayo

Gutmann

2020

Front. Built Environ.

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The properties of hurricanes directly influence storm surges; however, the implications of projected changes to the climate are unclear. Here, we simulate the storm surges of historical storms under present day and end of century climate scenarios to assess the impact of climate change on storm surge inundation. We simulate 21 storms that impacted the Gulf of Mexico and Atlantic Coasts of the continental U.S. from 2000 to 2013. We find that the volume of inundation increases for 14 storms and the average change for all storms is +36%. The extent of inundation increases for 13 storms, and the average change for all storms is +25%. Notable increases in inundation occur near Texas, Louisiana, Mississippi, the west coast of Florida, the Carolinas, and New Jersey. Our calculations of inundation volume and extent suggest that at the end of the century, we can expect hurricanes to produce larger storm surge magnitudes in concentrated areas, as opposed to surges with lower magnitudes that are widespread. We examine changes in maximum wind speed, minimum central pressure, translation speed, and radius of the 33 ms−1 wind to assess the impacts of individual storm characteristics on storm surge. We find that there is no single storm characteristic that directly relates to storm surge inundation or its climate induced changes. Even when all the characteristics are considered together, the resulting influences are difficult to anticipate. This is likely due to the complexity of the hydrodynamics and interactions with local geography. This illustrates that even as climate change research advances and more is known about projected impacts to hurricanes, implications for storm surge will be difficult to predict without explicit numerical simulation.

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“…(2018) used ROMS forced with meteorological forcing obtained from the U.S. National Centers for Environmental Prediction Climate Forecast System Reanalysis to simulate SSL along the Australian coastline. The process‐driven hydrodynamic models contribute to the understanding of physical mechanisms of the storm surge process and can predict storm surges accurately (Dietrich et al., 2011; Ramos‐Valle et al., 2020); however, they are often numerically expensive and may fail to fully resolve bathymetric and geometric features due to limited details in the available data sets or the resolution of computational grids, thereby affecting the applicability and accuracy of the SSL simulation (Arns et al., 2020; Beisiegel et al., 2020; Cyriac et al., 2018; Fernández‐Montblanc et al., 2019; Zou et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Characterization of Extreme Storm Surges Induced by Tropical Cyclones

Zhang

Wang

2021

JGR Atmospheres

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The overtopping of flood defenses by extreme storm surges during tropical cyclones poses a significant threat to life and property in coastal and estuarine regions. Since little effort has been devoted to investigating the complex interaction of multiple mechanisms causing extreme storm surges, for the first time, we propose a robust data‐driven framework to explicitly uncover the complex interaction and thus to improve the characterization of extreme storm surges induced by tropical cyclones. The framework constructs a probabilistic ensemble of dependence structures of multiple climatological forcing factors that potentially cause extreme storm surges based on a multi‐structure regular vine copula approach. The uncertainty in the vine‐based model structure is explicitly addressed in a Bayesian framework. The climatological forcing factors sensitive to extreme storm surge levels are selected using partial correlations, including 10‐m wind, 850‐mb temperature, 700‐mb geopotential height, precipitation, sea level pressure, and its spatial gradient extracted from the ERA5 reanalysis data. We demonstrate the framework by an in‐depth analysis of the extreme storm surge levels observed over two tidal gauging stations in Hong Kong during 1979–2018. Our findings show that the proposed framework substantially improves the characterization of extreme storm surges by taking into account the joint evolution of multiple mechanisms causing extreme storm surges and underlying uncertainties. Furthermore, the framework not only demonstrates higher skill than previous single‐structure vine‐based models but also can outperform the principal components regression and the random forest regression in terms of characterizing extreme storm surges.

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Impact of Tropical Cyclone Landfall Angle on Storm Surge Along the Mid‐Atlantic Bight

Cited by 21 publications

References 53 publications

Implementation of an Artificial Neural Network for Storm Surge Forecasting

Implementation of an Artificial Neural Network for Storm Surge Forecasting

Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States

Probabilistic Characterization of Extreme Storm Surges Induced by Tropical Cyclones

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