Machine learning for climate systems

April 2, 2020 — September 25, 2023

calculus
climate
dynamical systems
geometry
Hilbert space
how do science
machine learning
neural nets
PDEs
physics
regression
sciml
SDEs
signal processing
statistics
statmech
stochastic processes
surrogate
time series
uncertainty
wonk
Figure 1

How to model the world with data-hungry methods. How to think our way out of climate crisis/

1 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).

2 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.

3 Incoming

4 References

Australian Information Industry Association. 2023. “Tech and Sustainability.”
Pandey, van Nistelrooij, Maasakkers, et al. 2023. “Daily Detection and Quantification of Methane Leaks Using Sentinel-3: A Tiered Satellite Observation Approach with Sentinel-2 and Sentinel-5p.” Remote Sensing of Environment.
Rolnick, Donti, Kaack, et al. 2019. “Tackling Climate Change with Machine Learning.” arXiv:1906.05433 [Cs, Stat].
Schiermeier. 2018. “Droughts, Heatwaves and Floods: How to Tell When Climate Change Is to Blame.” Nature.
Schuit, Maasakkers, Bijl, et al. 2023. “Automated detection and monitoring of methane super-emitters using satellite data.” Atmospheric Chemistry and Physics.