10 Jun 2024
 | 10 Jun 2024
Status: this preprint is currently under review for the journal ESSD.

GEST: Accurate global ocean surface current reconstruction withmulti-scale dynamics-informed neural network

Linyao Ge, Guiyu Wang, Baoxiang Huang, Chuanchuan Cao, Xiaoyan Chen, and Ge Chen

Abstract. Exceptional precision and excellent resolution reconstruction of sea surface currents are beneficial for exploring complex oceanic dynamic processes. Normally, this required physical inversion models for global or regional oceans are constructed to reconstruct oceanic currents. These models are based on the analysis of sea surface geostrophic and Ekman currents derived from satellite observations of sea level and wind stress fields. Nevertheless, the presence of various typical dynamic processes in marine environments, such as mesoscale eddies and small-scale waves, continues to pose challenges in accurately reconstructing oceanic currents. Meanwhile, any product of surface current that neglects the contribution of wave motion would, at best, be incomplete. Therefore, in this paper, we introduce an accurate sea surface current product at a depth of 15 m, named GEST (Geostrophic-Ekman-Stokes-Tide). This product is produced by a multi-scale dynamics-informed neural network that learns the intricate representation of concealed characteristics in Ekman, geostrophic currents, wave-induced Stokes drift, and TPXO9 tidal currents. Its structure design is predicated upon the intricate coupling relationships between various ocean surface components and the veritable currents discerned by the deployment of drift buoys, with each ocean surface component correlating to discrete physical processes. Compared with the prevailing product, the GEST confers an elevation in precision by approximately 9.2 cm/s over the traditional multinomial fitting method, 10.4 cm/s beyond the OSCAR, and 8.81 cm/s surpassing GlobCurrent.

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Linyao Ge, Guiyu Wang, Baoxiang Huang, Chuanchuan Cao, Xiaoyan Chen, and Ge Chen

Status: open (until 17 Jul 2024)

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Linyao Ge, Guiyu Wang, Baoxiang Huang, Chuanchuan Cao, Xiaoyan Chen, and Ge Chen

Data sets

GEST Ocean Surface Current 2.0 Linyao Ge and Guiyu Wang

Linyao Ge, Guiyu Wang, Baoxiang Huang, Chuanchuan Cao, Xiaoyan Chen, and Ge Chen


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Short summary

High precision in reconstructing sea surface currents is vital for understanding ocean dynamics. Our paper introduces GEST (Geostrophic-Ekman-Stokes-Tide), a 15 m depth sea current product. GEST, generated by a neural network, captures Ekman, geostrophic currents, Stokes drift, and TPXO9 tidal currents. Its design accounts for complex ocean surface dynamics, surpassing OSCAR and GlobCurrent by 10.4 cm/s and 8.81 cm/s, respectively.