60 papers in the archive · tier-1 watchlist.
Five recommendations for standardized, independent evaluation of ML-based Earth system models, stressing physical consistency for climate projections without historical analogues.
Benchmarks, datasets & evaluationColorado State University (incl. CIRA)Lawrence Berkeley National Laboratory / NERSCNSF NCAR / UCAROak Ridge National LaboratoryUniversity of California, Berkeley
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 machine learning for empirical climate downscaling, covering out-of-distribution generalization, extremes, and a proposed evaluation framework for downscaling algorithms.
Hybrid ANN plus quantile-mapping post-processing of GEFSv12 reforecasts reduces warm bias and improves summer Tmax and extreme-day forecasts over Taiwan at extended ranges.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCAR
U-Net predicts flow-dependent covariances for an Ensemble Kalman Filter in a quasi-geostrophic model, matching 3DVar and EnKF for ensemble sizes up to 80.
Data assimilationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARPrinceton University
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 seasonal and multi-annual tropical cyclone forecasting, including emerging ML/AI methods, ENSO-neutral predictability and landfall skill gaps relative to user needs.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARMet OfficeMétéo-France (incl. CNRM)Colorado State University (incl. CIRA)China Meteorological Administration (CMA)Japan Meteorological Agency (JMA)Bureau of Meteorology (Australia)
Neural networks with explainable AI identify decadal variability in tropically driven subseasonal precipitation skill in CESM2 and confirm the relationships in ERA5 reanalysis.
Sub-seasonal to seasonalNASANSF NCAR / UCARColorado State University (incl. CIRA)
CAMNet, a physics-informed neural operator trained on high-resolution simulations, accelerates 2-D gravity wave and secondary wave simulations on GPUs, capturing key dynamics.
L2 loss outperforms L1 for imbalanced precipitation downscaling, and a learned nonlinear pre-processing function further improves accuracy in climate downscaling networks.
Review of ML for climate variability modes, extreme weather precursors, observation-model integration, downscaling and bias correction, aimed at domain scientists.
Climate analysis & attributionNSF NCAR / UCARUniversity of California, BerkeleyLawrence Berkeley National Laboratory / NERSCOak Ridge National Laboratory
ML models using VIIRS retrievals and HRRR variables estimate dead fuel moisture content over CONUS, outperforming climatology by 24% in hourly RMSE and beating MODIS-based models.
Extremes & hazardsNSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Position paper on Earth Virtualization Engines that pair high-resolution physics-based climate models with machine learning and federated access to exabyte-scale climate data.
Software, scaling & compressionCSCS - Swiss National Supercomputing CentreETH ZurichMeteoSwissMassachusetts Institute of Technology (MIT)Max Planck Institute for MeteorologyNSF NCAR / UCARNVIDIAUniversity of Reading
Couples data assimilation with ML to optimise WRF land-surface states in the Heihe basin, improving temperature and humidity simulations and ET estimates.
Describes an AI2ES summer school teaching trustworthy and explainable AI for severe storms, tropical cyclones and space weather to 677 participants.
Explainability, uncertainty & physicsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARUniversity of WashingtonColorado State University (incl. CIRA)University of Oklahoma (AI2ES)
A deep learning atmospheric river tracker is applied to a high-resolution large-ensemble climate dataset and checked for consistency against established algorithms over the US West Coast.
Extremes & hazardsNVIDIANSF NCAR / UCARETH ZurichUniversity of OxfordUniversity of California, Berkeley
A U-Net postprocesses 4 km West-WRF precipitation reforecasts into censored shifted gamma distributions, beating analog, regression and meta-Gaussian benchmarks at all lead times.
A neural network classifies satellite cloud morphology, showing that closed-cell cloud changes contribute about 0.06 W/m²/K to global shortwave cloud feedback under CO2 quadrupling.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARUniversity of Washington
A machine learning framework trained on climate models attributes tropical tropospheric warming: forcing adds 0.25 K/decade, internal variability offsets 0.07, and an aerosol bias adds 0.04.
Climate analysis & attributionNSF NCAR / UCARUniversity of California, Los Angeles (UCLA)Colorado State University (incl. CIRA)
Deep learning detects weather fronts in high-resolution CESM simulations over CONUS, showing frontal extreme precipitation becomes less likely under climate change despite overall increases.
Neural network and random forest estimate surface momentum, heat and moisture fluxes from tower observations, outperforming Monin-Obukhov similarity parametrizations used in atmospheric models.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCAR
Supervised classifier trained on 18 observation and reanalysis datasets labels Central and Eastern Pacific El Niño types, then shows the observed CP increase lies within internal variability in large ensembles.
Climate analysis & attributionNSF NCAR / UCARMax Planck Institute for Meteorology
Replaces Monin-Obukhov surface-layer flux schemes with neural networks in large-eddy simulations, showing modified boundary-layer structure and highlighting risks of unphysical predictions.
Provides a recipe for building atmospheric-science ML benchmark datasets and lists domain-specific challenges and needed benchmarks for weather, climate and air quality.
Benchmarks, datasets & evaluationECMWFForschungszentrum Jülich / Jülich Supercomputing CentreNSF NCAR / UCARNVIDIAUniversity of Oklahoma (AI2ES)
Kalman filter with a trained linear inverse model performs strongly coupled assimilation, cutting SST analysis error over 20% and extending forecast skill to 50 days.
Data assimilationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARUniversity of Washington
Hybrid analog-ensemble and CNN post-processing of GEFS precipitation raises CRPSS about 10% over quantile-mapped baseline out to 7 days in British Columbia.
A deep autoencoder detects anomalous heavy precipitation events in Japan and attributes their increase since 1977 partly to anthropogenic forcing, with more expected this century.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)NSF NCAR / UCARPrinceton UniversityNanjing University of Information Science & Technology (NUIST)
Compares analog ensemble and CNN empirical-statistical downscaling for 10 m winds over complex terrain, finding the analog ensemble has smaller errors than the CNN.
Gradient boosting and analog ensembles extrapolate NWP wind-speed uncertainty from one year of ensemble runs to 20 years for offshore wind siting, with R2 above 0.75.
Deep-learning postprocessing turns deterministic West-WRF atmospheric river IVT forecasts into reliable probabilistic forecasts that rival calibrated GEFS out to 120 h.
Post-processing & calibrationNSF NCAR / UCARKarlsruhe Institute of Technology (KIT)
CNN trained on present-day WRF storms classifies strongly rotating thunderstorms in end-of-century climate simulations with skill, and explanations reveal learned physical features.
A variational autoencoder with a linear latent penalty separates circulation-driven daily precipitation at 12 km over Europe, enabling dynamical adjustment of high-resolution fields.
Oxford workshop position on AI post-processing for weather and climate, releasing five archived ML-ready datasets with baseline notebooks to encourage trustworthy, FAIR and physically informed methods.
Post-processing & calibrationThe Alan Turing InstituteKarlsruhe Institute of Technology (KIT)Met OfficeNSF NCAR / UCAR
Machine-learning bias correction of 2016 smartphone pressure data reduces bias, and dense analysis resolves convective structure in a Beijing hailstorm better than station data.
Observations & remote sensingNSF NCAR / UCARChina Meteorological Administration (CMA)
Neural-network emulators replace bin-microphysics warm-rain processes in a GCM, reproducing its climate behaviour at the control run's computational cost.
Hybrid physics-ML & parameterizationNSF NCAR / UCARUniversity of Oxford
ClimateNet releases an expert-labeled dataset of tropical cyclones and atmospheric rivers in CAM5.1 output, and a deep learning segmentation model that enables pixel-level extreme event analysis.
Extremes & hazardsColorado State University (incl. CIRA)ETH ZurichLawrence Berkeley National Laboratory / NERSCNSF NCAR / UCARNVIDIAUniversity of California, Berkeley
Stacked LSTM forecasts day-ahead hourly temperature at about 150 m resolution on NYC IoT sensors, beating ARIMA and FNN, with Weather Underground history improving accuracy.
Nest-UNet, a UNet variant with nested skip connections, downscales daily precipitation from 0.25° to 4 km over western North America, outperforming bias-correction baselines.
UNet-AE adds an elevation autoencoder branch to downscale daily TMAX/TMIN from 0.25° to 4 km, cutting MAE by over 10% in unseen regions.
A CNN trained on current-climate storms skillfully classifies strongly rotating, potentially severe convection in end-of-century climate simulations, generalizing to future conditions.
Machine-learned preconditioners for the semi-implicit linear solver in a shallow-water model are competitive with conventional preconditioners and generalize beyond the training range.
Hybrid physics-ML & parameterizationECMWFNSF NCAR / UCARUniversity of Oxford
Machine learning identifies three-dimensional drought events in tree-ring reconstructions and CMIP simulations, suggesting models underestimate the range of future hydroclimate states.
Climate analysis & attributionNSF NCAR / UCARColumbia University (LEAP, Lamont-Doherty)
Random forests and gradient-boosted regression trees using HRRR forecast features improve upper-level aviation turbulence (EDR) forecasts and reduce false alarms versus regression baselines.
CNNs using radar images and proximity soundings predict next-hour tornado occurrence with AUC above 0.9, with a GridRad critical success index of 0.31.
Extremes & hazardsNSF NCAR / UCARUniversity of Oklahoma (AI2ES)
Unsupervised clustering of 850-hPa circulation over the NE Pacific links synoptic regimes to marine boundary-layer cloud droplet properties and cloud radiative forcing.
Random forest cloud mask from GOES-16 radiances matches the operational GOES-R clear-sky product and aids solar irradiance forecasting.
NCAR's updated wind power system combines NWP with ML power conversion, analog ensembles, radar ramp forecasts and fuzzy-logic icing alerts for Xcel Energy.
Fuzzy-logic ALPHA algorithm fuses satellite, NWP and radar data to detect high ice water content for aviation, with AUC 0.85 against in situ measurements.
Observations & remote sensingNASANSF NCAR / UCARBureau of Meteorology (Australia)
A conditional VAE replaces the historical archive in analog ensemble wind speed correction for NAM, matching AnEn skill with up to 25x less memory.
Random forest postprocesses HREFv2 ensembles for hail, with isotonic calibration matching SPC outlook reliability for operational day-1 forecasts.
Post-processing & calibrationNSF NCAR / UCARUniversity of Oklahoma (AI2ES)
A CNN with cross-channel 3D convolution nowcasts convective storms from 3D Doppler radar in Beijing-Tianjin-Hebei, outperforming traditional ML baselines on 3.7 million samples.
Synthesizes ML interpretation methods such as saliency maps and permutation importance, demonstrated on tornado, hail and convective-mode applications.
Explainability, uncertainty & physicsNSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Deep generative models replace stored archives in the analog ensemble method, cutting memory needs by orders of magnitude for probabilistic NWP post-processing at stations.
Trains a GAN as a stochastic sub-grid parameterization for the Lorenz '96 model; some configurations beat a bespoke scheme at weather and climate timescales.
Hybrid physics-ML & parameterizationNSF NCAR / UCARUniversity of Oxford
A CNN outperforms logistic regression at predicting severe hail from convection-allowing NWP fields, and interpretation reveals learned storm and environment features consistent with hail physics.
CNN with a novel probability-to-object step detects synoptic warm and cold fronts in reanalysis grids, achieving 0.73 probability of detection at 250 km.
Climate analysis & attributionNSF NCAR / UCARUniversity of Oklahoma (AI2ES)
Feed-forward network on HWRF output beats operational intensity guidance at 24–57 h in cross-validation and quadruples rapid-intensification hits.
A CNN trained on satellite infrared and microwave data estimates tropical cyclone intensity with 8.39 kt RMS error, offering an objective alternative to the Dvorak technique.
Shows AI methods, combined with physical understanding, improve forecasts of storm duration, severe wind and hail, precipitation type, renewable energy and aviation turbulence.
Extremes & hazardsNSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Storm-based machine learning produces calibrated probabilistic severe hail forecasts from convection-allowing ensembles, achieving higher critical success index than existing hail forecasting methods.
Extremes & hazardsNSF NCAR / UCARUniversity of Oklahoma (AI2ES)
