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Machine Learning and Artificial Intelligence to Advance Earth System Science Opportunities and Challenges: Proceedings of a Workshop (2022) / Chapter Skim
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Introduction
Pages 3-8

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From page 3...
... While the Earth system science community has made good use of many statistical and modeling methods, ML/AI techniques could help transform difficult computational problems into those that can better exploit ongoing advances in information frameworks and computer architectures optimized for ML/AI computation. In addition to these opportunities and emerging approaches for using ML/AI to advance Earth system science, there are a number of challenges and risks including technical and data challenges, such as the interoperability of data and the integration of ML/AI tools with existing infrastructure; necessary workforce development; and the "last mile" problem of making forecasts useful for a wide array of users and decision makers.
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From page 4...
... . • Supervised learning is a simple kind of ML algorithm in which a model is trained with data from a potentially noisy labeled dataset, consisting of a set of features and a label.a • Unsupervised learning is a kind of ML in which a model must look for patterns in a dataset with no labels and with minimal human supervision.a • Explainable AI (XAI)
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From page 5...
... . Using the ECMWF workflow as an example, Dueben emphasized that rather than a single ML/AI workflow, there are many ML/AI applications throughout the numerical weather prediction workflow, from observations to data assimilation and numerical weather prediction forward modeling and post-processing.
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From page 6...
... , and five objectives to enable the weather and climate modeling community to utilize machine learning. NOTE: AI, artificial intelligence; HPC, high performance computing; IoT, Internet of Things; ITT, Invitation to Tender.
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From page 7...
... Benchmark datasets are useful because they allow quantitative evaluation of ML approaches, access to relevant data, and separation of concerns between domain scientists and ML and highperformance computing experts. Ideal benchmark datasets would include a problem statement; data that are available online; python code or Jupyter5 notebooks; a reference ML solution; quantitative evaluation metrics; visualization, diagnostics, and robustness tests; and computational benchmarks.
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Key Terms

  • artificial intelligence
  • domain scientists
  • advance earth
  • numerical weather
  • weather prediction

  • benchmark datasets
  • european centre
  • weather forecasts
  • earth sciences
  • dueben suggested

  • analytical capabilities
  • atmospheric sciences
  • prediction workflow
  • data assimilation
  • quantitative evaluation

  • performance computing
  • uncertainty quantification
  • planning committee
  • advance science
  • science domain

  • science forecasts
  • science models
  • range weather
  • advance capabilities
  • science community

  • science peter
  • utilize machine
  • earth science
  • potentially noisy
  • climate modeling


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