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Artificial intelligence for ocean phenomena forecasting.

Xiaofeng Li1, Xudong Zhang1, Yibin Ren1

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Artificial intelligence (AI) revolutionizes ocean forecasting, categorizing it into ocean phenomena (OP) and ocean state variable (OSV) prediction. This review synthesizes OP forecasting methods and proposes solutions to integrate OSV models for better disaster mitigation.

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Area of Science:

  • Oceanography
  • Artificial Intelligence
  • Climate Science

Background:

  • Artificial intelligence (AI) is transforming ocean forecasting, with distinct approaches for ocean phenomena (OP) and ocean state variable (OSV) prediction.
  • OP forecasting serves a wider audience, including policymakers and the public, compared to OSV forecasting.
  • Current AI-driven ocean forecasting research is fragmented across different phenomena and timescales, lacking a unified synthesis.

Purpose of the Study:

  • To provide a comprehensive review of various AI-based ocean phenomena (OP) forecasting types.
  • To explore the application of general AI frameworks, incorporating physical knowledge and tailored training strategies, across diverse OP forecasting tasks.
  • To address the disconnect between independent OP forecasting models and large OSV models, proposing the Large Ocean Model (LOM) of OP to bridge this gap.

Main Methods:

  • Reviewing AI applications in OP forecasting across different timescales: hourly-daily (atmosphere-forced hazards), daily-weekly/monthly (mesoscale dynamics and waves), and monthly-yearly (climate-cryosphere modes).
  • Analyzing the integration of physical knowledge and specialized training strategies within AI frameworks for effective OP forecasting.
  • Examining the limitations of independent OP models and proposing the Large Ocean Model (LOM) of OP for enhanced connectivity with OSV models.

Main Results:

  • AI frameworks, enhanced with physical insights and specific training, demonstrate broad applicability to various OP forecasting challenges.
  • OP forecasting models currently operate in isolation, limiting their utility in disaster mitigation and impact assessments.
  • The proposed Large Ocean Model (LOM) of OP offers a potential pathway to connect OSV and OP forecasting.

Conclusions:

  • AI offers significant potential for advancing ocean forecasting across various phenomena and timescales.
  • Integrating OP and OSV forecasting models is crucial for improving disaster response and understanding human impacts on the ocean.
  • Five key insights for designing and implementing AI models in ocean forecasting are presented to guide future research and application.