Wave spectra partitioning and long term statistical distribution

Portilla‐Yandún, Jesús; Cavaleri, Luigi; Vledder, G.P. Van

doi:10.1016/j.ocemod.2015.06.008

Cited by 61 publications

(38 citation statements)

References 26 publications

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“…Portilla-Yandún et al (2015) argue that partitioning based on spectral peaks is more physically accurate than use of a cutoff frequency. However, this results in energy associated with potentially improper partitions (swell with wave periods ,7 s).…”

Section: Parameterization Of Spectral Data a Wave Generation Areasmentioning

confidence: 99%

“…As a result, many studies examine bulk wave parameters of the largest energy peak of a spectrum while virtually eliminating secondary spectral peaks, which leads to incomplete and potentially misleading results. Recent advances allow for simple statistical representation of multimodal wave spectra but have yet to be widely applied (Portilla-Yandún et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

Hegermiller

Antolínez

Rueda

et al. 2017

Journal of Physical Oceanography

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Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into ''wave families,'' defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

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Section: Parameterization Of Spectral Data a Wave Generation Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

Hegermiller

Antolínez

Rueda

et al. 2017

Journal of Physical Oceanography

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“…Specifically, again in the (f,θ) space, the bivariate occurrence distribution of the single original (f p ,θ p ) peaks is found to be a quite skillful descriptor of wave spectra realizations. This technique is described in detail in Portilla et al [2015a]. An example of this distribution is shown in Figure 2c.…”

Section: Wave Spectra Statisticsmentioning

confidence: 99%

Climate patterns derived from ocean wave spectra

Portilla‐Yandún

Salazar

Cavaleri

2016

Geophysical Research Letters

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The fact that ocean surface waves are an integrated effect of meteorological activity has the interesting consequence that the memory of the wave systems is larger than that of the wind and storms that generated them. At each single point the related information is stored as its wave spectrum, a matrix containing the energy distribution of wave systems with different origins in space and time. We describe the concept of spectral partitioning and the technique used to obtain spectral statistics, whose outcome we associate with the physical reality. Using long series of spectral data we derive information of the, possibly very far, generation zones climatologically connected at a confluent point. Working on the eastern equatorial Pacific we focus on the prominent effects of El Niño events, for which interactions of mesoscale phenomena are revealed from the analysis of the local situation.

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“…Journal of Geophysical Research: Oceans 10.1002/2015JC011309 at present the best, source to derive many of these characteristics. A methodology to develop spectral wave climate variables has been presented by Portilla et al [2015]. The method is based on the statistics of spectral partitions, defined as independent wave systems within the spectrum.…”

Section: Long-term 2-d Spectral Statisticsmentioning

confidence: 99%

“…Once consistent and independent wave systems are obtained from a long time series of spectral data, the partitioning technique can be further exploited to characterize the local wave climate on a spectral basis. A proposed methodology for this is given in Portilla et al [2015]. For spectral characterization, several possibilities exist (we can consider different parameters like the mean, median spectrum, or others; some of these were tested in the cited reference).…”

Section: Long-term 2-d Spectral Statisticsmentioning

confidence: 99%

On the specification of background errors for wave data assimilation systems

Portilla‐Yandún

Cavaleri

2016

JGR Oceans

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In this paper, a new methodology is proposed for the computation of Background Errors in wave data assimilation systems. Background errors define the spatial influence of an observation in the model domain. Since at present the directional wave spectrum is the fundamental variable of both state‐of‐the‐art numerical models and most modern instrumentation, this is at the core of the proposed methodology. The advantage of the spectral approach is that the wave spectrum contains detailed information of the different wave systems and physical processes at work (e.g., wind‐sea or swells). These systems have different origins and may be driven by different mechanisms, having therefore different spatial structures, length scales, and sensitivity to local wind conditions. The presented method enables making consistent and specific corrections to each component of the spectrum, in time and space. The innovations presented here require an integral look at the data assimilation algorithm for which a suitable scheme is also proposed. Examples of computed background errors are presented for shelf and oceanic basins showing the spatial structures of the different wave systems active in these areas.

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Wave spectra partitioning and long term statistical distribution

Cited by 61 publications

References 26 publications

A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

Climate patterns derived from ocean wave spectra

On the specification of background errors for wave data assimilation systems

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