Estimating tropical cyclone-induced wind, waves, and surge: A general methodology based on representative tracks

Bakker, T; Antolínez, José A. Á.; Leijnse, Tim; Pearson, Stuart; Giardino, Alessio

doi:10.1016/j.coastaleng.2022.104154

Cited by 16 publications

(14 citation statements)

References 54 publications

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“…Due to the limited number of observations on TC evolution, for shortterm operational analyses, an autoregressive technique that imposes potential errors on top of the forecasted track is preferred over those parametric sampling techniques used for long-term strategic risk assessments based on historical records (e.g., Nederhoff et al, 2021). In addition, for the same scarcity of observations, there is limited knowledge of the underlying joint distribution between TC and ocean characteristics, which makes Monte Carlo sampling preferred compared to sampling techniques that are highly efficient for complex multivariate patterns such as cluster analysis (e.g., Choi et al, 2009) and MDA methods (e.g., Bakker et al, 2022). However, exploring the possibility of increasing efficiency via the aforementioned methods is important, especially since the error space increases as a function of lead time, and estimating these events requires increasing numbers of ensemble members (Fig.…”

Section: Discussionmentioning

confidence: 99%

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Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Nederhoff,

van Ormondt,

Veeramony

et al. 2024

Geosci. Model Dev.

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Abstract. Tropical-cyclone impacts can have devastating effects on the population, infrastructure, and natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed and/or intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound-flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecasting the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results 3 d before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.

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Section: Discussionmentioning

confidence: 99%

“…However, these machine learning downscaling methods lack nonlinear interactions between relevant coastal processes driving compound flooding. Hybrid methods focus on reducing the number of tracks simulated and have proved to be capable of accurately representing a larger set of scenarios (Bakker et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Nederhoff,

van Ormondt,

Veeramony

et al. 2024

Geosci. Model Dev.

Self Cite

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“…Areas in the feature space with a larger data density represent a larger area of the SOM (Clark et al, 2020). Selecting a maximum dissimilar subset prevents that the SOM is dominated by surface activities that occur more often and enables a better representation and identification of rare surface activities (Bakker et al, 2022).…”

Section: Subset Selection For Trainingmentioning

confidence: 99%

Characterization of Morphological Surface Activities Derived From Near-Continuous Terrestrial Lidar Time Series

Hulskemper

Anders²,

Antolínez³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Earth’s landscapes are shaped by processes eroding, transporting and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict (e.g., the landward movement of a shoreline), knowledge is needed on the occurrences and impact of these activities. Near-continuous terrestrial laser scanning enables the acquisition of large datasets of surface morphology, represented as three-dimensional point cloud time series. Exploiting the full potential of this large amount of data, by extracting and characterizing different types of surface activities, is challenging. In this research we use a time series of 2,942 point clouds obtained over a sandy beach in The Netherlands. We investigate automated methods to extract individual surface activities present in this dataset and cluster them into groups to characterize different types of surface activities. We show that, first extracting 2,021 spatiotemporal segments of surface activity using an object detection algorithm, and second, clustering these segments with a Self-organizing Map (SOM) in combination with hierarchical clustering, allows for the unsupervised identification and characterization of different types of surface activities present on a sandy beach. The SOM enables us to find events displaying certain type of surface activity, while it also enables the identification of subtle differences between different events belonging to one specific surface activity. Hierarchical clustering then allows us to find and characterize broader groups of surface activity, even if the same type of activity occurs at different points in space or time.

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“…Ideally, all possible combinations need to be simulated by either the use of extensive computational resources or computationally efficient methods. Computationally efficient methods can be achieved by an acceleration of the direct simulations, developing a series of event reduction techniques, or by a combination of the two, for example, through hybrid downscaling (Bakker et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?

Nederhoff¹,

Leijnse²,

Parker³

et al. 2023

Preprint

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Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones in coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are thirty times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more area will be flooded, regardless if that flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

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Estimating tropical cyclone-induced wind, waves, and surge: A general methodology based on representative tracks

Cited by 16 publications

References 54 publications

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Characterization of Morphological Surface Activities Derived From Near-Continuous Terrestrial Lidar Time Series

Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?

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