Local Ocean Wave Field Estimation Using a Deep Generative Model of Wave Buoys

被引:1
作者
Han, Peihua [1 ]
Hildre, Hans Petter [1 ]
Zhang, Houxiang [1 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Ocean Operat & Civil Engn, N-6009 Alesund, Norway
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
关键词
Ocean waves; Predictive models; Atmospheric modeling; Data assimilation; Wind forecasting; Data models; Oceans; Neural networks; ocean wave field estimation; wave buoys;
D O I
10.1109/TGRS.2023.3334304
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Estimating oceanic wave fields from sparse observations has been a long-standing challenge in oceanography and an important environmental metric desired for maritime operations. The requirement for frequent real-time updates of the wave field within the local area poses difficulties for data assimilation approaches, as they can be computationally complex and rely on external atmospheric forcing. The relationship between the wave field and local sparse observations is embedded in reanalysis or hindcast data. We propose a data-driven deep-learning model capable of estimating the local wave field using sparsely distributed floating wave buoys. This novel model simultaneously produces wave height, period, and direction, along with their respective uncertainties. In a year-long test period within a local fjord region characterized by complex wave patterns influenced by intricate geography, the proposed model demonstrates remarkable accuracy and efficiency in estimating wave fields. This study demonstrates the promising potential of data-driven deep-learning models as an alternative to rapidly estimating the wave field.
引用
收藏
页数:11
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共 35 条
[1]   A review of operational methods of variational and ensemble-variational data assimilation [J].
Bannister, R. N. .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2017, 143 (703) :607-633
[2]   The quiet revolution of numerical weather prediction [J].
Bauer, Peter ;
Thorpe, Alan ;
Brunet, Gilbert .
NATURE, 2015, 525 (7567) :47-55
[3]   4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry [J].
Beauchamp, Maxime ;
Febvre, Quentin ;
Georgenthum, Hugo ;
Fablet, Ronan .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2023, 16 (08) :2119-2147
[4]  
Booij N., 1996, PROC 25 INT C COASTA, V1, P668
[5]   Application of 4D-Variational data assimilation to the California Current System [J].
Broquet, G. ;
Edwards, C. A. ;
Moore, A. M. ;
Powell, B. S. ;
Veneziani, M. ;
Doyle, J. D. .
DYNAMICS OF ATMOSPHERES AND OCEANS, 2009, 48 (1-3) :69-92
[6]   Data assimilation in the geosciences: An overview of methods, issues, and perspectives [J].
Carrassi, Alberto ;
Bocquet, Marc ;
Bertino, Laurent ;
Evensen, Geir .
WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE, 2018, 9 (05)
[7]   Grid Network: Feature Extraction in Anisotropic Perspective for Hyperspectral Image Classification [J].
Chen, Zhonghao ;
Hong, Danfeng ;
Gao, Hongmin .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 20
[8]   Deep learning for twelve hour precipitation forecasts [J].
Espeholt, Lasse ;
Agrawal, Shreya ;
Sonderby, Casper ;
Kumar, Manoj ;
Heek, Jonathan ;
Bromberg, Carla ;
Gazen, Cenk ;
Carver, Rob ;
Andrychowicz, Marcin ;
Hickey, Jason ;
Bell, Aaron ;
Kalchbrenner, Nal .
NATURE COMMUNICATIONS, 2022, 13 (01)
[9]   Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields [J].
Fablet, Ronan ;
Beauchamp, Maxime ;
Drumetz, Lucas ;
Rousseau, Francois .
FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, 2021, 7
[10]   A 4D-variational ocean data assimilation application for Santos Basin, Brazil [J].
Fragoso, Mauricio da Rocha ;
de Carvalho, Gabriel Vieira ;
Mendes Soares, Felipe Lobo ;
Faller, Daiane Gracieli ;
de Freitas Assad, Luiz Paulo ;
Toste, Raquel ;
Barbosa Sancho, Livia Maria ;
Passos, Elisa Nobrega ;
Boeck, Carina Stefoni ;
Reis, Bruna ;
Landau, Luiz ;
Arango, Hernan G. ;
Moore, Andrew M. .
OCEAN DYNAMICS, 2016, 66 (03) :419-434