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

被引:1
作者
Kang, Xianbiao [1 ]
Song, Haijun [1 ]
Zhang, Zhanshuo [2 ]
Yin, Xunqiang [3 ]
Gu, Juan [1 ]
机构
[1] Civil Aviat Flight Univ China, Coll Aviat Meteorol, Guanghan, Peoples R China
[2] Natl Supercomp Ctr Jinan, Shandong Comp Sci Ctr, Jinan, Peoples R China
[3] Minist Nat Resources, Inst Oceanog 1, Key Lab Marine & Numer Modeling, Qingdao, Peoples R China
基金
中国国家自然科学基金;
关键词
significant wave height; transformer; 2D-Geoformer; numerical forecasting; error correcting; COASTAL REGIONS; MODEL; ENERGY; ASSIMILATION; PREDICTION; VALIDATION;
D O I
10.3389/fmars.2024.1374902
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 51 条
[1]   Development and validation of a regional-scale high-resolution unstructured model for wave energy resource characterization along the US East Coast [J].
Allandadi, M. Nabi ;
Gunawan, Budi ;
Lai, Jonathan ;
He, Ruoying ;
Neary, Vincent S. .
RENEWABLE ENERGY, 2019, 136 :500-511
[2]   Prediction of significant wave height using spatial function [J].
Altunkaynak, Abdusselam .
OCEAN ENGINEERING, 2015, 106 :220-226
[3]   Assessment of SWAN and WAVEWATCH-III models regarding the directional wave spectra estimates based on Eastern Black Sea measurements [J].
Amarouche, Khalid ;
Akpinar, Adem ;
Rybalko, Aleksandra ;
Myslenkov, Stanislav .
OCEAN ENGINEERING, 2023, 272
[4]   Numerical modelling of three-dimensional wave-current interactions in complex environment: Application to Alderney Race [J].
Bennis, Anne-Claire ;
Furgerot, Lucille ;
Du Bois, Pascal Bailly ;
Dumas, Franck ;
Odaka, Tina ;
Lathuiliere, Cyril ;
Filipot, Jean-Francois .
APPLIED OCEAN RESEARCH, 2020, 95
[5]  
Bertasius G, 2021, PR MACH LEARN RES, V139
[6]   Uncertainties in long-term wave modelling [J].
Bitner-Gregersen, Elzbieta M. ;
Waseda, Takuji ;
Parunov, Josko ;
Yim, Solomon ;
Hirdaris, Spyros ;
Ma, Ning ;
Guedes Soares, C. .
MARINE STRUCTURES, 2022, 84
[7]   A third-generation wave model for coastal regions - 1. Model description and validation [J].
Booij, N ;
Ris, RC ;
Holthuijsen, LH .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 1999, 104 (C4) :7649-7666
[8]   Improving prediction performance of significant wave height via hybrid SVD-Fuzzy model [J].
Celik, Anil .
OCEAN ENGINEERING, 2022, 266
[9]   Real-time significant wave height estimation from raw ocean images based on 2D and 3D deep neural networks [J].
Choi, Heejeong ;
Park, Minsik ;
Son, Gyubin ;
Jeong, Jaeyun ;
Park, Jaesun ;
Mo, Kyounghyun ;
Kang, Pilsung .
OCEAN ENGINEERING, 2020, 201
[10]   Nowcasting significant wave height by hierarchical machine learning classification [J].
Demetriou, Demetris ;
Michailides, Constantine ;
Papanastasiou, George ;
Onoufriou, Toula .
OCEAN ENGINEERING, 2021, 242