Machine Learning for Climate and Weather Working Group Announce

Machine Learning for Climate and Weather Working Group
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Hi everyone,

The next monthly meeting of the Machine Learning for Climate and Weather Working Group will take place Friday, 6 December. Time and zoom details can be found at the top, in the first post.

Here is the agenda:

Facilitator: Ryan Holmes
Co-chairs: Ryan, Tennessee, Micael, Sanaa

  • Acknowledgement of country
  • Updates from ACCESS-NRI
  • New member introductions
  • Updates from the Chairs:
    • AMOS Session 8.6 Advancing Weather and Climate Research with Machine Learning - Keynote speaker: Terry O’Kane (CSIRO)
  • Updates from the Community
    • From a community member: Can allocations of GPU through the WG be extended beyond the current 48-hours limit?
  • ML talks:
    • Nicholas Loveday from the Bureau of Meteorology will give a presentation on verification of AI models:
      Understanding the performance of the latest data-driven (pure AI) models is a pressing scientific question. While much of the evaluation in the past has focused on point-based verification, spatial verification techniques have received less attention. Additionally, there has been a lack of evaluation using threshold-weighted scoring rules to assess how well these models predict extremes. In this talk, we demonstrate how combining threshold-weighted scoring rules with spatial verification techniques allows us to compare how well the HRRR and GraphCast models in predict extreme events. This verification approach has several advantages; a) it does not suffer from the double penalty issue within a specified radius, b) it can emphasize the performance of predicting extremes, c) it discourages hedging and does not reward biased forecasts, d) it can account for climatological differences when calculating the mean score across the domain, and e) it can be used to compare models with different grid resolutions. The verification approach demonstrated has the potential to be a useful tool in the future to complement other evaluation methods in a testing and evaluation framework.
    • Tennessee will present ‘Edit,’ a tool developed by the BoM to facilitate ML/AI programming. This will be followed by community feedback on interest in making the tool available, with the potential for ACCESS-NRI to host it.
  • Share the minutes from the previous community meeting. Add new items, and discuss priorities, and actions.
  • Papers of the month.

See you in a couple of weeks!

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