the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Long-term Prediction of the Gulf Stream Meander Using OceanNet: a Principled Neural Operator-based Digital Twin
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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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1238', Anonymous Referee #1, 19 Jun 2024
Please find attached my general and specific comments
- AC1: 'Reply on RC1', Michael Gray, 28 Aug 2024
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RC2: 'Comment on egusphere-2024-1238', Rachel Furner, 24 Jun 2024
Please see my comments attached, thank you for this interesting work.
- AC2: 'Reply on RC2', Michael Gray, 28 Aug 2024
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