27 papers in the archive.
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
GeoCryoAI, a CNN-RNN hybrid, models Alaskan permafrost active layer thickness and CO2/CH4 fluxes, with a 1.327 cm ALT RMSE over 1969–2022.
Climate analysis & attributionNASACalifornia Institute of Technology (Caltech)
Nature Climate Change perspective on combining AI and computing for climate modelling and prediction; abstract absent, judged from title and venue.
Hybrid physics-ML & parameterizationCalifornia Institute of Technology (Caltech)Massachusetts Institute of Technology (MIT)NSF NCAR / UCARPrinceton University
Reviews physics-informed and data-driven visual anemometry, inferring local wind from the motion of trees and flags for renewable energy and urban applications.
Observations & remote sensingMassachusetts Institute of Technology (MIT)California Institute of Technology (Caltech)
Interpretable non-linear ML bias correction removes 3D cloud artifacts from OCO-2 XCO2, cutting unphysical variability by 31% over land and 55% over ocean.
Observations & remote sensingNASACalifornia Institute of Technology (Caltech)
Random forest trained on GFS forecasts and radiometer data predicts zenith atmospheric noise temperature at NASA Deep Space Network sites, enabling up to 2.43 dB downlink gain.
Applications & impactsNASACalifornia Institute of Technology (Caltech)University of California, Los Angeles (UCLA)
An ensemble-based algorithm targets data acquisition to regions and times that most reduce parametric uncertainty in a GCM convection scheme, within calibrate-emulate-sample.
Explainability, uncertainty & physicsMassachusetts Institute of Technology (MIT)California Institute of Technology (Caltech)
Gradient-free Kalman inversion trains hybrid subgrid turbulence and convection parameterizations from large-eddy simulation data, with uncertainty estimates from unscented Kalman inversion.
Hybrid physics-ML & parameterizationNASACalifornia Institute of Technology (Caltech)
Uses Calibrate-Emulate-Sample Bayesian learning to tune the Cloudy bulk microphysics scheme with far fewer model runs, learning a collision kernel parameter from particle-model output.
Hybrid physics-ML & parameterizationCalifornia Institute of Technology (Caltech)
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)
FourCastNet uses adaptive Fourier neural operators to forecast global 0.25 degree weather in under 2 seconds, matching IFS at short leads and beating it for precipitation.
Global medium-range NWPCalifornia Institute of Technology (Caltech)Lawrence Berkeley National Laboratory / NERSCNVIDIA
Calibrating a convection parameterization with seasonally averaged statistics cuts calibration error up to tenfold and narrows posterior spread by two to five times in an idealized GCM.
Explainability, uncertainty & physicsCalifornia Institute of Technology (Caltech)Massachusetts Institute of Technology (MIT)
An ensemble-based experimental design algorithm, using calibrate-emulate-sample, chooses high-resolution simulation regions that most reduce uncertainty in GCM convection parameters, with 10^2 rather than 10^5 model runs.
Explainability, uncertainty & physicsMassachusetts Institute of Technology (MIT)California Institute of Technology (Caltech)
Deep learning enhances ensemble-based data assimilation for high-dimensional nonlinear dynamical systems; the abstract is missing and atmospheric application is unclear.
Data assimilationCalifornia Institute of Technology (Caltech)
A neural network using a microwave rain index and 24 h wind changes identifies tropical cyclone rapid intensification with near-perfect skill and reveals environmental drivers up to 1050 km away.
Extremes & hazardsNASACalifornia Institute of Technology (Caltech)
Benchmarks ESRGAN, EDSR and PhIREGAN for super-resolving NWP wind and solar fields, and releases an ML-ready dataset for renewable forecasting.
Downscaling & super-resolutionMassachusetts Institute of Technology (MIT)California Institute of Technology (Caltech)University of California, Berkeley
Calibrate-emulate-sample with Gaussian process emulators and ensemble Kalman inversion quantifies convective parameter uncertainty in an idealized GCM at a fraction of MCMC cost.
Explainability, uncertainty & physicsCalifornia Institute of Technology (Caltech)
ML trained on a large climate model ensemble yields seasonal precipitation forecasts over the western US competitive with NMME dynamical models.
Sub-seasonal to seasonalCalifornia Institute of Technology (Caltech)NASA
Random forest combined with Gaussian mixture clustering characterizes bias and standard error of water vapour atmospheric motion vectors, addressing a known gap in uncertainty estimation.
Observations & remote sensingNASACalifornia Institute of Technology (Caltech)
Surveys physics-informed ML approaches with ten case studies for emulating, downscaling and forecasting weather and climate, reporting greater consistency and data efficiency.
Explainability, uncertainty & physicsCalifornia Institute of Technology (Caltech)Lawrence Berkeley National Laboratory / NERSCNVIDIAStanford UniversityUniversity of California, Berkeley
Machine learning using satellite inner-core precipitation and outflow temperature beats the NHC operational rapid-intensification consensus, improving Peirce skill score by 12 to 138%.
Extremes & hazardsIBM ResearchNASACalifornia Institute of Technology (Caltech)
Convcast, a convolutional LSTM trained on NASA IMERG satellite precipitation, nowcasts up to 150 minutes with 0.805 mm/h RMSE at 30 minutes, outperforming optical flow.
A conditional GAN reconstructs CloudSat-style cloud vertical structure from MODIS inputs, capturing multilayer clouds and providing uncertainty estimates.
Observations & remote sensingCalifornia Institute of Technology (Caltech)NASA
Coupled CNN-RNN estimates local wind speed (0.75-11 m/s) from video of flags and trees, matching cup anemometer measurements.
Observations & remote sensingCalifornia Institute of Technology (Caltech)Stanford University
A deep neural network retrieves high-latitude rain and snow from GMI and MODIS data, largely outperforming GPROF on snowfall and reducing rain underestimation.
Observations & remote sensingNASACalifornia Institute of Technology (Caltech)Tsinghua University (NowcastNet)University of California, Los Angeles (UCLA)University of Oklahoma (AI2ES)
A blueprint proposes Earth system models that learn parameterizations from observations and high-resolution simulations via data assimilation and machine learning, illustrated on a toy system.
Hybrid physics-ML & parameterizationCalifornia Institute of Technology (Caltech)NASA
