An Inner-Shelf Wave Forecasting System for the U.S. Pacific Northwest

Abstract

An operational inner-shelf wave forecasting system was implemented for the Oregon and southwest Washington coast in the U.S. Pacific Northwest (PNW). High-resolution wave forecasts are useful for navigational planning, identifying wave energy resources, providing information for site-specific coastal flood models, and having an informed recreational beach user group, among other things. This forecasting model is run once a day at 1200 UTC producing 84-h forecasts. A series of nested grids with increasing resolution shoreward are implemented to achieve a 30-arc-second resolution at the shelf level. This resolution is significantly higher than what the current operational models produce, thus improving the ability to quantify the alongshore variations of wave conditions on the PNW coast. Normalized root-mean-squared errors in significant wave height and mean wave period range from 0.13 to 0.24 and from 0.13 to 0.26, respectively. Visualization of the forecasts is made available online and is presently being used by recreational beach users and the scientific community. A series of simulations, taking advantage of having a validated shelf-scale numerical wave model, suggests that neither dissipation due to bottom friction nor wind generation is important in the region at this scale for wave forecasting and hindcasting when considering bulk parameters as opposed to the processes of refraction and shoaling. The Astoria and McArthur Canyons; the Stonewall, Perpetua, and Heceta Banks; and Cape Blanco are significant bathymetric features that are shown to be capable of producing alongshore variability of wave heights on the shelf.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

“…Directional wave spectra are usually summarized in terms of simple aggregated parameters, such as significant wave height ( ), peak wave period ( ), and mean wave direction ( ). However, this simplification lacks a description of multimodal sea states with short‐period seas and long‐period swell originating from multiple storm systems [ Boukhanovsky and Guedes Soares , ], all of which are needed to accurately model local nearshore processes [ García‐Medina et al ., ], coastal morphology, and flood hazards. Accurate modeling of multimodal sea states is also relevant for analyzing wave energy resources, computing wave loads on offshore structures, and estimating the probability of rogue waves [ Trulsen et al ., ].…”

“…Recent work addressing statistical wave forecasting has focused on parameterizing partitions of long-term directional spectra [Boukhanovsky and Guedes Soares, 2009], reproducing the short-term chronology of directional spectra types based on transition probabilities [Boukhanovsky et al, 2007;Lucas et al, 2011], and analyzing variability of wave spectra based on weather types [Espejo et al, 2014].…”

“…Statistical downscaling methods based on WTs are flexible frameworks that can explore climate variability of many physical processes at different time scales. These methods have been recently applied in Camus et al [2014b] andB ardossy et al [2015] for the empirical distribution of sea-state parameters, in Espejo et al [2014] for directional wave spectra, in Rueda et al [2016a] for an extreme value model of daily significant wave height maxima, and in Guanche et al [2013] for modeling the chronology of weather types.…”

Predicting Climate‐Driven Coastlines With a Simple and Efficient Multiscale Model

“…Since wave direction bias can be problematic when evaluating the shoreline response, we discuss how we address this systematic error in Appendix C. The nearshore propagation developed for this work is efficient, computational inexpensive, and similar to that presented in García-Medina et al (2013) for the same region.…”

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

“…Akpinar et al presented a potential wave energy assessment in the Black Sea and showed spatial distribution maps based on monthly, seasonal, and annual averages for the establishment and design of a wave energy converter (WEC) system [23]. García-Medina et al conducted a seven-year hindcast, using nested grid WWIII and structured grid SWAN models to assess the temporal and spatial variability as well as the trend of wave resources in Oregon and southwestern Washington [24,25]. Yang et al conducted a wave resource assessment at a test bed off the central Oregon coast, using structured grid WWIII and SWAN with a four-level nested grid approach.…”