Assimilation of peak period from video images in numerical wave models at a local scale

Saavedra, Victor; Montoya, RubénD.; Orfila, A.; AndrésF, Osorio

doi:10.1016/j.cageo.2023.105407

Cited by 2 publications

(1 citation statement)

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“…The first hinges on physicaldynamic principles, utilizing wave numerical models that meticulously simulate the birth, movement, and fading of ocean waves through the computational resolution of the underlying wave dynamic equations. These models incorporate a suite of physical processes such as wave generation, spectral distribution, propagation, nonlinear inter-wave interactions, dissipation mechanisms, and the effects of refraction and diffraction (Zhao et al, 2014;Qiao et al, 2016;Bennis et al, 2020;Wang et al, 2021;Bitner-Gregersen et al, 2022;Saavedra et al, 2023). Prominent among such numerical models are the Wave Watch III (WW3) (Tolman, 2009;Amarouche et al, 2023), the Simulating Waves Nearshore (SWAN) system (Booij et al, 1999;Ris et al, 1999), and the MASUM model (Yong-zeng et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

A transformer-based method for correcting significant wave height numerical forecasting errors

Kang,

Song,

Zhang

et al. 2024

Front. Mar. Sci.

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Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During long-term validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings.

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