An extreme value model for maximum wave heights based on weather types

Rueda, Ana; Camus, Paula; Méndez, Fernando J.; Tomás, Antonio; Luceño, Alberto

doi:10.1002/2015jc010952

Cited by 33 publications

(36 citation statements)

References 35 publications

(46 reference statements)

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“…">C1.Obtain the categorical distribution of sea‐state types (e.g., unimodal, bimodal, or multimodal wave conditions) for each weather type. C2.Fit the marginal distributions of the daily sea‐state parameters ( Hs, Tp , and D ) of the sea and swell systems for each weather type [ Rueda et al ., ]. C3.Model the dependence between predictand variables for each weather type using a multivariate Gaussian copula function [ Rueda et al ., ]. …”

Section: Overview Of the Methodologymentioning

confidence: 98%

“…The methodology (Figure ) is based on a number of steps grouped into four main modules: (A) parameterization of spectral data, (B) statistical model for the predictor, (C) statistical model for the predictand, and (D) climate‐based stochastic simulation of synthetic time series of the multivariate predictand. In relation to previous works [ Perez et al ., ; Camus et al ., ; Rueda et al ., ], the primary new contributions of this work are highlighted in Figure in grey boxes. The substeps of the methodology are listed below:…”

Section: Overview Of the Methodologymentioning

confidence: 99%

“…Based on previous work [ Camus et al ., ; Perez et al ., ; Rueda et al ., ], the daily predictor is defined as the SLP and squared SLP gradient (SLPG) fields, representing the geostrophic wind conditions over a spatial domain that covers the corresponding wave‐generation area (given by ESTELA). However, other predictors such as geopotential height [ Bárdossy et al ., ] or SLP and winds [ Martínez‐Asensio et al ., 2016] could be used.…”

Section: Statistical Model For the Predictormentioning

confidence: 99%

“…[] for directional wave spectra, in Rueda et al . [] for an extreme value model of daily significant wave height maxima, and in Guanche et al . [] for modeling the chronology of weather types.…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate Gaussian copulas [ Nelsen , ; Salvadori et al ., ] are used to capture the correlation between variables. The statistical dependence between consecutive days is addressed by defining a climate‐based extremal index for each weather type [ Rueda et al ., ]. The chronology of multivariate sea states is modeled by a time‐dependent Markov chain model for daily weather types.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Rueda

Hegermiller

Antolínez

et al. 2017

J. Geophys. Res. Oceans

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Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical‐downscaling model‐based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long‐term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea‐state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large‐scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea‐state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time‐dependent, climate‐driven offshore and coastal sea‐state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

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Section: Overview Of the Methodologymentioning

confidence: 98%

Section: Overview Of the Methodologymentioning

confidence: 99%

Section: Statistical Model For the Predictormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Rueda

Hegermiller

Antolínez

et al. 2017

J. Geophys. Res. Oceans

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Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

et al. 2018

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We present a methodology to reconstruct the daily maximum storm surge levels, obtained from tide gauges, based on the surrounding atmospheric conditions from an atmospheric reanalysis (20th Century Reanalysis—20CR). Tide gauge records in Southeast Asia are relatively short, so this area is often underrepresented in studies based on long observational records, and there are just a few studies that have analyzed storm surge trends, variability or return water levels (RWLs) from numerical models in this area. Here we develop, calibrate, and validate a multivariate linear regression model that relates the storm surge with the principal components of the local atmospheric conditions. This allows us to reconstruct storm surges for the 147 year 20CR period (1866–2012) and therefore to calculate more robust RWLs from the entire simulated data set and subsets thereof. RWLs are obtained by fitting the monthly maxima values to the Generalize Extreme Value (GEV) distribution. We find an increase in the 50 year RWL from the second half of the 19th century to the present unrelated to mean sea level; this increase is less noticeable when comparing only recent periods. Therefore, further research is needed since there is evidence that atmospheric reanalyses can include spurious trends in the late 19th and early 20th. RWLs obtained from the statistical reconstruction are validated against the ones obtained from observations and from a numerical model. Agreements are generally higher when using surge levels from the statistical model, even before its calibration.

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Marine climate variability based on weather patterns for a complicated island setting: The New Zealand case

Rueda

Cagigal

Antolínez

et al. 2018

Intl Journal of Climatology

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Understanding marine climate variability is important for coastal planning and marine operations. It is also particularly challenging for complicated settings (e.g., islands) and data‐poor regions. The aim of this work is to establish a relationship between daily synoptic atmospheric patterns, and wave and storm surge conditions around New Zealand waters, based on instrumental and reanalysis data. The daily predictor we developed is able to represent sea and swell wave conditions as well as storm surge variability over different temporal scales. However, when climate variability is analysed on a longer temporal period, based on the 20th century reanalysis, large inhomogeneities are found. This highlights the dangers related to assessing climate variability, especially in data‐poor regions (such as New Zealand), where inhomogeneities could be interpreted as actual changes.

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An extreme value model for maximum wave heights based on weather types

Cited by 33 publications

References 35 publications

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

Marine climate variability based on weather patterns for a complicated island setting: The New Zealand case

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