19 papers in the archive · tier-1 watchlist.
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.
Extremes & hazardsColorado State University (incl. CIRA)Google DeepMindGoogle ResearchMet OfficeNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
A NeuralGCM-based hybrid model trained on satellite precipitation cuts climate-model precipitation biases and beats the ECMWF ensemble at mid-range forecasts.
Combining RCM dynamical downscaling with diffusion refinement lets large CMIP6 ensembles be downscaled cheaply, giving better uncertainty bounds than BCSD and smaller dynamical ensembles.
Downscaling & super-resolutionCalifornia Institute of Technology (Caltech)Google Research
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
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.
Probabilistic & ensemble forecastingGoogle DeepMindGoogle Research
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.
Hybrid physics-ML & parameterizationECMWFGoogle DeepMindGoogle ResearchMassachusetts Institute of Technology (MIT)
WeatherBench 2 provides an open evaluation framework, training and ground-truth data, and baselines for data-driven global 1-14 day forecasts.
Benchmarks, datasets & evaluationECMWFGoogle DeepMindGoogle Research
SEEDS uses diffusion models to emulate operational weather ensembles, generating thousands of realistic members cheaply with improved reliability and extreme-event probabilities after bias correction.
MetNet-3 learns from sparse and dense sensors to forecast precipitation, wind, temperature and dew point up to 24 hours ahead, beating HRRR and ENS over CONUS and running in Google Search.
Neural-network post-processing of global 1 to 7 day forecasts across 30 levels improves RMSE by up to 12% for 850 hPa temperature at day 7, and discusses metric limits.
Post-processing & calibrationGoogle ResearchNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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.
Convolutional neural weather models trained with extreme-focused losses improve global heatwave forecasts 1 to 28 days ahead, beating persistence and ECMWF S2S after two weeks.
Extremes & hazardsCalifornia Institute of Technology (Caltech)Google ResearchUniversity of Oklahoma (AI2ES)
Climate-invariant ML embeds physical knowledge into parameterization emulators so they generalize across climate regimes, maintaining offline accuracy in three atmospheric models.
Explainability, uncertainty & physicsColumbia University (LEAP, Lamont-Doherty)Google ResearchMassachusetts Institute of Technology (MIT)NVIDIAUniversity of California, Los Angeles (UCLA)
Curated nearly decade-long US wildfire spread dataset combining satellite fire data with weather and fuel variables, plus a neural network baseline for next-day spread.
A large-context neural network forecasts precipitation 12 hours ahead over CONUS, beating HRRR and HREF in skill.
MetNet uses axial self-attention on radar and satellite inputs to forecast precipitation up to 8 hours at 1 km, beating NWP for 7-8 hour lead times over the US.
Pure deep learning approach to precipitation nowcasting from radar; abstract unavailable, so classification rests on the title alone.
A U-Net treats 1-hour, 1 km precipitation nowcasting as image-to-image translation and outperforms optical flow, persistence and HRRR.
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.
Applications & impactsETH ZurichGoogle DeepMindGoogle ResearchLawrence Berkeley National Laboratory / NERSCMassachusetts Institute of Technology (MIT)New York University (M2LInES)Stanford UniversityUniversity of California, Berkeley
