Machine learning for climate systems

2020-04-02 — 2025-10-02

Wherein satellite shortwave‑infrared retrievals are described and daily detection of methane super‑emitters of at least 10 tonnes per hour is shown, and ML‑driven control of chaotic weather systems is surveyed.

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

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 driver identification

  • 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 interventions

I just saw Miyoshi presenting about this Japan Moonshot program on weather control. See Coverage in IEEE Spectrum.

Miyoshi and Sun (2022), Sun, Miyoshi, and Richard (2023) are interesting proofs-of-concept showing we can achieve high-efficiency control of chaotic systems in simulation.

Their methods are not particularly “ML-like”; I’m curious how much better we could do.

The possibility of “switching off” typhoons before they make landfall is very interesting.

4 ML for climate solutions

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

5 Incoming

6 References

Australian Information Industry Association. 2023. “Tech and Sustainability.”
Bodnar, Bruinsma, Lucic, et al. 2024. “Aurora: A Foundation Model of the Atmosphere.”
Guibas, Mardani, Li, et al. 2021. “Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators.” In.
Huang, Zhang, Lan, et al. 2023. “Adaptive Frequency Filters As Efficient Global Token Mixers.”
Kurth, Subramanian, Harrington, et al. 2023. “FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators.” In Proceedings of the Platform for Advanced Scientific Computing Conference. PASC ’23.
Lam, Sanchez-Gonzalez, Willson, et al. 2023. “GraphCast: Learning Skillful Medium-Range Global Weather Forecasting.”
Miyoshi, Amemiya, Otsuka, et al. 2023. “Big Data Assimilation: Real-Time 30-Second-Refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. SC ’23.
Miyoshi, and Sun. 2022. “Control simulation experiment with Lorenz’s butterfly attractor.” Nonlinear Processes in Geophysics.
Mukkavilli, Civitarese, Schmude, et al. 2023. “AI Foundation Models for Weather and Climate: Applications, Design, and Implementation.”
Nguyen, Brandstetter, Kapoor, et al. 2023. “ClimaX: A Foundation Model for Weather and Climate.”
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.
Price, Sanchez-Gonzalez, Alet, et al. 2024. “Probabilistic Weather Forecasting with Machine Learning.” Nature.
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.
Sun, Miyoshi, and Richard. 2023. “Control simulation experiments of extreme events with the Lorenz-96 model.” Nonlinear Processes in Geophysics.