🌎 We developed an ML-based atmospheric model suitable for climate prediction! Long-term stable, capable of assessing mass/moisture conservation, and predicts diagnostics for (eventually) coupling to ocean.
Very excited about this project! 🎉
We're excited to present ACE (AI2 Climate Emulator), a long-term (at least 10 yr) stable ML emulator of a comprehensive 100-km resolution global atmospheric model: https://t.co/1Rd3a9XDXd
🌍☀️❄️ Can AI forecast year-to-year differences in the seasons?
New research with @metofficeUK shows our ACE2 ML model demonstrates seasonal forecasting skill—matching traditional physics-based methods while using dramatically less compute. 🧵
We partnered with researchers @UCSanDiego and @ucsd_cse to supercharge climate modeling, speeding up predictions and enhancing accuracy.🌍 Check out the blog post below and come find us at #NeurIPS this week!
We are now seeking applicants for a full-time research scientist position on the climate modeling team at Ai2! Come work with an excellent team at the frontier of using AI for climate modeling. 🌎🌦
Hybrid in Seattle, app review begins Oct 15.
Details 👉https://t.co/XAHuhAvjyA
Excited to share our new review paper on the use of ML for climate science https://t.co/HHJAhkpWLX led by Veronika Eyring and Bill Collins and based on a great workshop in Colorado in 2022
We are delighted to announce the #ICML2024 ML4ESM Best Paper Award winner:
[2406.14798] Probabilistic Emulation of a Global Climate Model with Spherical DYffusion (https://t.co/zEuA5qC3Vu)
Congrats to @salvaRC7 and co-authors @ai2_climate@yuqirose for their amazing work!
Hope everyone is enjoying #ICML2024! Don't forget to stop by our workshop on Machine Learning for Earth System Modeling tomorrow, Friday 26th in Stolz 1! Schedule here: https://t.co/PuxJJrNfvQ
🌍We've developed the first conditional generative model for accurate & efficient ensemble climate simulations!
📄Read the full paper: https://t.co/a47yL2uC6f
🎉Excited to share that a preliminary version was accepted as an oral presentation @ml4esmworkshop at #ICML2024!
🧵
Don't miss this expert panel, incl. Dave Lawrence, LEAP's @NCAR_Science Model Dev. Liaison, discussing the responsible integration of #AI + #climate modeling.
📅 THIS THURS, 6/27
🕜 12:30-2p EDT
💻 Online: https://t.co/Qr16yxUR2m
@NOAAClimate@CUSEAS@columbiaclimate@NSF
How can AI help us prepare for a changing climate? That's one of the big questions our climate modeling senior director Chris Bretherton wants to answer. Chris spoke with @nvidia's @noahkravitz about how technology like localized models can help us prepare for the future — listen here: https://t.co/cHbpJIGvlr
We have a new ACE preprint led by our former intern, James Duncan, where we’ve successfully emulated a second major atmospheric model, DOE’s EAMv2! 🌎💻
https://t.co/jbjPRb2Py8
A new published article, led by @oliwm, investigates direct learning of heating/moistening rates from a coarsened 3 km (realistic topography!) storm-resolving simulation for use in a 200 km global climate model. 🌡🌧🌐
https://t.co/RUjAW9SViG
For those of you at @ametsoc's #AMS2024 this week, there will be two @ai2_climate in-person presentations on Tuesday. Andre Perkins (@frodre) is presenting on ML emulation of climate model microphysics https://t.co/RXSCtqD4kh
Did you like the IA NWP models? You'll love the AI climate models!
The authors present a climate model based on SFNO block, and with an impressive numerical stability of 10 years (!) at least.
https://t.co/ls10n8doit
It was a breakthrough year for environmental AI, with many new foundation models and innovative solutions for tackling our biggest environmental challenges. Learn more about 2023's major advances and key themes in climate and sustainability research: https://t.co/t1ICk9XYly
@janniyuval For stability, choice in architecture, input variables (insolation, surface height), and normalization strategy all led to improvements. Our dataset on gaussian grid is also well suited for the architecture we used. Haven't done enough ablations to say exactly which matters most.
🌎 We developed an ML-based atmospheric model suitable for climate prediction! Long-term stable, capable of assessing mass/moisture conservation, and predicts diagnostics for (eventually) coupling to ocean.
Very excited about this project! 🎉
We're excited to present ACE (AI2 Climate Emulator), a long-term (at least 10 yr) stable ML emulator of a comprehensive 100-km resolution global atmospheric model: https://t.co/1Rd3a9XDXd
@janniyuval We have some blurring but less than what I've seen for models using 0.25° ERA5. Using 1° data makes it easier. But also we only optimize over a single 6hr step which makes a big difference.
Updated version of AI2 Climate Emulator (ACE) paper with:
-> 100 year run ⌚️
-> testing on unseen realistic SST dataset 🌍
-> code+data+checkpoint released https://t.co/uiZ7LwleAb
https://t.co/sVeMR9FoSC
The updated ACE arxiv paper is available, now with code release https://t.co/1Rd3a9XDXd . We're excited to get to share these results with the community!
6/