Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet

Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet

IntroductionMarine forecasts play a crucial role in ensuring safe navigation, efficient offshore operations, coastal management, and research, particularly in regions with challenging conditions like the Arctic Ocean. These forecasts necessitate precise predictions of ocean currents, wind-driven wav...

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Bibliographic Details
Main Authors: Aleksei V. Buinyi, Dias A. Irishev, Edvard E. Nikulin, Aleksandr A. Evdokimov, Polina G. Ilyushina, Natalia A. Sukhikh
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Marine Science
Subjects:
Arctic
machine learning
ocean prediction
LSTM
short-term forecast
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2024.1456480/full
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https://www.frontiersin.org/articles/10.3389/fmars.2024.1456480/full

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