Ferran Alet, Tom R. Andersson, Ilan Price et al.
· Nature
·
Operational AI ensemble WeatherNext Cyclones predicts tropical cyclone track, intensity and size to 15 days, gaining about 1 day of lead time over leading operational models.
Anna Vaughan, Stratis Markou, Will Tebbutt et al.
· Nature
·
End-to-end model ingests raw observations to produce global and station forecasts, beating operational NWP on several variables using 8% of input data.
Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet et al.
· Nature
·
GenCast, a diffusion ensemble trained on reanalysis, beats ECMWF ENS on 97.4% of 1320 targets, with better tropical cyclone and wind power prediction, in 8 minutes.
Dmitrii Kochkov, Janni Yuval, Ian Langmore et al.
· Nature
·
NeuralGCM couples a differentiable dynamical solver with ML components, matching ML and ECMWF ensemble skill for 1-15 day forecasts and stable multi-decade climate runs.
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson et al.
· Science
·
GraphCast, a graph neural network trained on ERA5, makes 10-day global forecasts at 0.25 degrees in under a minute, beating operational systems on 90% of targets.
David Rolnick, Priya L. Donti, Lynn H. Kaack et al.
· ACM Computing Surveys
·
Position paper surveying how machine learning can help climate change mitigation and adaptation across sectors such as energy and disaster management; not focused on weather or climate science.