Machine learning for climate systems

1 ML for climate simulation

• Climate Informatics

  Climate Informatics is an open community interested in research combining climate science with approaches from statistics, machine learning and data mining. Through the annual conference series and through this community website, we hope to stimulate discussion of new ideas, foster new collaborations, grow the community, and thus accelerate discovery across disciplinary boundaries.

2 ML for climate drivers

• ESA - Trio of Sentinel satellites map methane super-emitters

  In a recent paper published in Remote Sensing of Environment (Schuit et al. 2023), researchers from SRON found that the Sentinel-3 satellites can retrieve methane enhancements from its shortwave infrared band measurements. Impressively, it can detect the largest methane leaks of at least 10 tonnes per hour, depending on factors like location and wind conditions, every single day.

  See also Pandey et al. (2023).

3 ML for climate solutions

Jeff Dean’s NeurIPS 2019 talk suggests ideas. His talk is an advertisement for tensorflow probability as a solution for machine learning for physics simulations for making nuclear fusion feasible etc.

4 Incoming

• The rise of machine learning in weather forecasting | ECMWF

• Yanir Seroussi, The mission matters: Moving to climate tech as a data scientist

• How AI is improving climate forecasts

• Gencast/graphcast

  • google-deepmind/graphcast
  • GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy - Google DeepMind

5 References