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Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford
Sue Ellen Haupt1, William Chapman2, Samantha V Adams3
1Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.
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.
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.
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