Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop
Abstract
The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 15, 2021
- Source ID
- 10.1098/rsta.2020.0091
Entities
People
- Aneesh Subramanian
- Charlie Kirkwood
- J. Scott Hosking
- Niall H. Robinson
- Samantha V. Adams
- Sebastian Lerch
- Sue Ellen Haupt
- William Chapman
Organizations
- British Antarctic Survey
- German Research Foundation
- Karlsruhe Institute of Technology
- National Center for Atmospheric Research
- National Science Foundation
- Office of Naval Research
- Scripps Institution of Oceanography
- University of Colorado Boulder
- University of Exeter