11 papers in the archive.
Nature Communications review of AI for modeling and understanding extreme weather and climate events; abstract absent, judged from title and venue.
Extremes & hazardsInriaMax Planck Institute for BiogeochemistryTechnical University of Munich (TUM)
ArchesWeather is a transformer deterministic model, and ArchesWeatherGen a flow-matching ensemble that beats IFS ENS and NeuralGCM on WeatherBench headline variables at academic compute cost.
Nature Climate Change perspective on machine learning in climate modelling and analysis; abstract absent, judged from title and venue.
Climate emulationAllen Institute for AI (Ai2, ACE)California Institute of Technology (Caltech)Colorado State University (incl. CIRA)Columbia University (LEAP, Lamont-Doherty)Google ResearchInriaLawrence Berkeley National Laboratory / NERSCNSF NCAR / UCARNVIDIANew York University (M2LInES)University of California, BerkeleyUniversity of Oxford
Review of ML across climate physics, covering observation reconstruction, subgrid parameterization, causal inference and climate and weather prediction.
Hybrid physics-ML & parameterizationECMWFInriaLawrence Berkeley National Laboratory / NERSCUniversity of Chicago
CNN wind-speed estimator for Sentinel-1 SAR reduces rain-induced errors by 27% and 45% under rain above 1 and 3 mm/h, validated against buoy measurements.
Data-driven analog data assimilation with ML-emulated models selects the best GCM for observations, performing as well as using the true SPEEDY model.
End-to-end differentiable training of subgrid energy-backscatter parameterizations for 2D quasi-geostrophic turbulence yields models more stable and accurate than a priori-trained schemes.
DeepFire neural emulator of fire spread turns high-resolution weather forecasts into next-day fire danger maps and ensembles for 13 Corsican fires.
Spatio-temporal deep learning corrects day-ahead wind speed forecasts built on WRF predictions for energy use; abstract missing, judged from title.
RainBench is a multimodal global benchmark combining simulated satellite data, ERA5 and IMERG precipitation, with the PyRain library and medium-range baselines.
A neural network fusing past storm trajectories with reanalysis wind and pressure fields forecasts 24 h tropical cyclone displacement in seconds, complementary to dynamical models.
Extremes & hazardsInriaNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
