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Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford

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Summary

Artificial intelligence (AI) and machine learning (ML) excel at post-processing weather and climate model outputs. This work advances AI in atmospheric sciences by promoting trustworthy algorithms, FAIR data, and physics-informed AI, alongside a new dataset repository.

Keywords:
artificial intelligenceclimatemachine learningpost-processingweather

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Area of Science:

  • Atmospheric sciences
  • Artificial intelligence
  • Machine learning

Background:

  • Post-processing of model output is a mature application area for AI/ML in atmospheric sciences.
  • The 2019 Oxford workshop on Machine Learning for Weather and Climate highlighted current AI applications and future directions.

Purpose of the Study:

  • To review the history and current state of AI/ML for weather and climate model post-processing.
  • To propose medium-term goals for advancing AI in atmospheric sciences, focusing on trustworthiness, interpretability, FAIR data, and physics-informed methods.
  • To establish a community resource for AI-driven weather and climate research.

Main Methods:

  • Literature review and synthesis of discussions from a dedicated workshop.
  • Proposal of actionable items for advancing AI in the field.
  • Initiation of a public repository for atmospheric science datasets relevant to AI/ML.

Main Results:

  • Five datasets for AI-driven weather and climate problems are presented and archived.
  • Jupyter notebooks are provided for data processing and baseline result assessment.
  • A call for community engagement in testing algorithms and contributing results.

Conclusions:

  • AI/ML holds significant potential for advancing weather and climate modeling, particularly in output post-processing.
  • Future progress requires a focus on algorithm transparency, data accessibility, and integration with physical understanding.
  • The established dataset repository and baseline assessments aim to accelerate research and development in this interdisciplinary field.