Preprints
https://doi.org/10.5194/egusphere-2024-1238
https://doi.org/10.5194/egusphere-2024-1238
03 May 2024
 | 03 May 2024
Status: this preprint is open for discussion.

Long-term Prediction of the Gulf Stream Meander Using OceanNet: a Principled Neural Operator-based Digital Twin

Michael A. Gray, Ashesh Chattopadhyay, Tianning Wu, Anna Lowe, and Ruoying He

Abstract. Many meteorological and oceanographic processes throughout the eastern United States and western Atlantic Ocean, such as storm tracks and shelf water transport, are influenced by the position and warm sea surface temperature of the Gulf Stream (GS)- the region's western boundary current. Due to highly nonlinear processes associated with the GS, predicting its meanders and frontal position have been long-standing challenges within the numerical modeling community. While the weather and climate modeling communities have begun to turn to data-driven machine learning frameworks to overcome analogous challenges, there has been less exploration of such models in oceanography. Using a new dataset from a high-resolution data-assimilative ocean reanalysis (1993–2022) for the Northwest Atlantic Ocean, OceanNet (a neural operator-based digital twin for regional oceans) was trained to identify and track the GS’s frontal position over subseasonal-to-seasonal timescales. Here we present the architecture of OceanNet and the advantages it holds over other machine learning frameworks explored during development while demonstrating predictions of the Gulf Stream Meander are physically reasonable over at least a 60-day period and remain stable for longer.

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Michael A. Gray, Ashesh Chattopadhyay, Tianning Wu, Anna Lowe, and Ruoying He

Status: open (until 03 Jul 2024)

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Michael A. Gray, Ashesh Chattopadhyay, Tianning Wu, Anna Lowe, and Ruoying He
Michael A. Gray, Ashesh Chattopadhyay, Tianning Wu, Anna Lowe, and Ruoying He

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Short summary
The Gulf Stream is a prominent oceanic feature in the northwestern Atlantic Ocean that influences weather patterns in the northern hemisphere and is notoriously difficult to predict. We present a machine learning model, OceanNet, to predict the position of the Gulf Stream months in advance. OceanNet is able to perform a 120-day prediction 4,000,000x faster than traditional methods of ocean modeling with great accuracy.