101 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)
SamudrACE is a coupled atmosphere-ocean ML emulator giving stable centuries-long runs at 1 degree with realistic ENSO variability absent in uncoupled mode.
Climate emulationAllen Institute for AI (Ai2, ACE)Columbia University (LEAP, Lamont-Doherty)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)New York University (M2LInES)Princeton University
HRRRCast is a ResNet-based emulator of the HRRR convection-allowing model over CONUS, with DDIM ensembles that beat HRRR at the 20 dBZ reflectivity threshold.
Regional & km-scale modelsColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Couples the ACE2 emulator to a slab ocean and trains it across CO2 levels, reproducing equilibrium warming but violating energy conservation in transient runs.
Climate emulationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
ACE2 is a 450M-parameter emulator that conserves dry air and moisture, runs about 1500 simulated years per day, and reproduces El Niño responses over 80 years.
Climate emulationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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
Reservoir computers trained to enforce dynamical invariants gain longer, more stable forecasts with limited data on Lorenz 96 and a quasi-geostrophic atmosphere model.
Explainability, uncertainty & physicsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
U-Net nowcasts convection initiation and thunderstorms over the continental US within 60 min from GOES-16 imagery, with MRMS radar targets.
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
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)
U-Net++ and U-Net3+ networks emulate full shortwave and longwave RRTM radiation in GFSv16, running 7510 and 90 times faster with reliable heating rates.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Convolutional autoencoders with clustering classify 3D MRMS radar reflectivity profiles into 18 precipitation-structure clusters linked to HRRR environmental conditions.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
A physics-constrained deep learning radiative transfer model over oceans recovers correct Jacobians, enabling radiance sensitivities for satellite data assimilation.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
DownScaleBench uses an iterative super-resolution CNN to produce 300 m urban precipitation from 10 km GsMAP data over Austin, improving on cubic interpolation.
Downscaling & super-resolutionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Uses atmospheric circulation patterns as inputs to ANN models (NARX, PLANN) for monthly to 9-month sea level anomaly forecasts, outperforming post-processed NWP and climatology at US sites.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Consensus of classical ML models on HWRF predictors raises rapid-intensification detection to 56% POD, versus 10–30% for operational models.
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)
S2S models overestimate heatwave frequency over northern Nigeria at 30-day lead; deep-learning bias correction improves forecast reliability.
Extremes & hazardsECMWFNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural networks emulate the full shortwave and longwave RRTM radiation scheme for GFSv16, running 6579x and 96x faster than the original with near-zero bias.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
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)
Introduces six neural network uncertainty methods and evaluation scores for environmental science, demonstrated on dewpoint profiles and Himawari-8 convection prediction.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Probabilistic Output Extreme Learning Machine builds multi-model ensembles for Indian summer monsoon rainfall, aiming to avoid overconfidence and Gaussian assumptions. No quantitative skill score given.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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)
CNN corrects MRMS radar precipitation estimates over complex western US terrain, beating radar QPE on several atmospheric river events in a year-long evaluation.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
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
Modified cubed-sphere deep learning weather model predicts global daily precipitation from surface temperature, matching GFS skill at a 1-day lag.
Global medium-range NWPNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural network corrections trained on a 3 km storm-resolving model reduce a 200 km climate model's land temperature and precipitation pattern errors by 6-25%.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural networks learn FV3-GFS systematic error from data assimilation analysis increments and apply online corrections, improving 10-day forecast skill at all lead times.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)
A linear inverse model predicted the February 2021 cold air outbreak four weeks ahead, up to two weeks earlier than numerical models, and estimated it as a top-1% event.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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
Convection-permitting simulations with ML damage models project 128% higher typhoon losses for Southeast China under RCP8.5 warming, with 50-year losses up 20–170%.
Applications & impactsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Princeton University
A Random Forest precipitation estimator using GOES-16 ABI infrared channels and RAP predictors over CONUS beats SCaMPR, with correlation 0.46 and HSS 0.91 vs 0.19.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Machine-learned corrective tendencies from 25 km simulations improve a 200 km FV3GFS model's land precipitation pattern error by 8–28% across four SST-perturbed climates.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Iterative SRCNN downscales 10 km JAXA GsMAP satellite precipitation to 300 m over Austin, Texas, beating cubic interpolation on PSNR and mutual information.
Downscaling & super-resolutionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Random forests trained on GEFS reforecasts and severe weather reports give probabilistic US severe-weather forecasts for days 4-8, comparable to SPC outlooks at days 4-5.
Extremes & hazardsColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Machine-learning quality control for ABI water vapor radiances blends seven bands to reject surface-contaminated pixels, improving Hurricane Harvey track forecasts.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Institute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
Presents XCast, an open Python toolkit that applies scikit-learn-style methods gridpoint-by-gridpoint to gridded climate data, simplifying statistical and ML climate forecasting workflows.
Software, scaling & compressionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)
Compares DeepSD, ConvLSTM, U-Net and SR-GAN for precipitation downscaling against IMD station data; SR-GAN performs best.
Downscaling & super-resolutionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
ANN-based postprocessing of GEFS forecasts with short 60-day training windows improves probabilistic 24-h precipitation forecasts over CONUS, especially for heavy rainfall thresholds.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
UNet post-processing of CFSv2 cuts global precipitation bias from +5–7 mm/day to within ±1 mm/day over land at 1–3 day leads.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of California, Los Angeles (UCLA)
Advanced machine learning and regression show the QBO matters more than ENSO for stratospheric water vapor; CMIP6 and CCMI models underrepresent the QBO link.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Machine-learned corrective tendencies from nudged 25 km runs improve land precipitation pattern errors by 17-30% in a coarse FV3GFS across four climates.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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
LightningCast uses a CNN on GOES-16 ABI imagery to predict next-hour lightning over CONUS, giving about 20 min or more of lead time and transferring to GOES-West.
Neural network post-processing of ECMWF ensemble mean corrects Brazilian wind-speed bias to within ±0.1 m/s and cuts RMSE by 10–30% for 1–46 day lead times.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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)
SVM detects shifts in the Lorenz-63 variable distribution to switch between Gaussian and mixed Gaussian-lognormal 3DVar, reducing analysis error.
Data assimilationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Spatially enhanced loss functions using neighbourhood and spectral filters improve neural network convection forecasts, with spectral-filter models giving better-calibrated probabilities than pixelwise training.
Benchmarks, datasets & evaluationColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
XGBoost reconstructs hurricane surface wind fields from storm and ERA-Interim features, with errors below climatological variance, for synthetic risk applications.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)New York University (M2LInES)
Machine-learned nudging tendencies correct a 200-km FV3GFS model toward 3-km X-SHiELD, gaining 18 hours of 3-7 day skill and 30% better precipitation.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Washington
Fully connected network emulates the CRTM microwave radiative transfer model for ATMS brightness temperatures over ocean, with mean biases of hundredths of a kelvin for data assimilation.
A self-attention GRU nowcasts radar echoes for landfalling hurricanes and extreme convective rainfall in the southeast US, aiming to improve skill beyond the first hour.
NowcastingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Machine learning post-processing of subseasonal-to-seasonal forecasts improves prediction of extreme precipitation occurrence over the contiguous US.
Sub-seasonal to seasonalNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Fourier reservoir computing with localization extends skillful forecast horizons for a coupled atmosphere-ocean quasi-geostrophic model beyond standard reservoir computing.
Random forest and neural network models on GOES-ABI imagery improve low-cloud detection in multilayer scenes, raising probability of detection from 0.685 to 0.815 versus the CCL product.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Adding polarimetric radar variables KDP and ZDR to a deep-learning nowcasting model raises CSI by 13.2% at 30 min and 17.4% at 60 min.
NowcastingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Neural network ensembles compare marine low cloud fraction relationships in E3SM at two vertical resolutions against ERA5-CERES, showing shifts in meteorological drivers dominate differences.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
RNNs replace the forecast model, error covariance and adjoint in the DA cycle, with DA updating hidden states; the approach scales via localization and parallelization.
CNN bias correction of CMORPH satellite precipitation over US coastal mountains raises correlation from 0.55 to 0.88 against Stage IV gauge-radar data.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Knowledge-guided U-Net++ emulates the RRTM shortwave radiation scheme for NWP, running about 10^4 times faster with good tropical generalization.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Integrates observed cloud characteristics with machine learning to improve prediction of surface solar irradiance variability at a site.
Local & station forecastingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
A random forest learns nudging tendencies and is coupled to FV3GFS, reducing 500 hPa height forecast error and annual-mean precipitation RMSE by about 20% in year-long runs.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of WashingtonPrinceton University
Machine learning on polarimetric S-band radar storm cells predicts lightning flash rates up to 30 minutes ahead, with polarimetric gains mainly for short datasets.
NowcastingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
A machine-learning classifier on GOES-16 ABI channels with NWP inputs identifies precipitation occurrence with 97% accuracy and convective types at 76%.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
CSU-MLP random-forest excessive rainfall forecasts, built from ensemble reforecasts, were transitioned into operational use at NOAA's Weather Prediction Center.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
A deep neural network fuses NUCAPS, ABI and RTMA data to cut low-level temperature and moisture profile RMSE by over 30% versus ERA5.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Institute of Atmospheric Physics, Chinese Academy of Sciences
FogNet, a 3D CNN with attention, post-processes NAM forecasts to predict fog visibility at a US airport, outperforming HREF on eight metrics.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Interpretable machine learning methods inspired by the Dvorak technique estimate tropical cyclone intensity from satellite imagery; the abstract is unavailable, so details are limited.
CNNs estimate western North Pacific tropical cyclone intensity from Himawari-8 infrared images, reaching 88.9% accuracy and 4.62 m/s RMSE with focal loss and binary framing.
Shows shallow-network emulators of radiative transfer, built for an older GCM, stay stable and realistic when transplanted into a newer state-of-the-art GCM under AMIP-like runs.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Perspective proposing Neural Earth System Modelling, which fuses deep networks and Earth system models into interpretable hybrids that might eventually replace them.
Hybrid physics-ML & parameterizationColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Potsdam Institute for Climate Impact Research (PIK)Princeton UniversityUniversity of Washington
Neural networks post-process NCEP CFS week-3–4 precipitation and 2-m temperature forecasts, improving skill over multiple linear regression correction.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
MIIDAPS-AI deep-learning retrieval of temperature, moisture and cloud from microwave and infrared sounders runs about 100 times faster with comparable accuracy to operational methods.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Random forest on RUC pre-storm sounding data discriminates tornadic from nontornadic storms, with feature analysis showing pressure has little impact.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Machine-learning-constrained autoconversion and accretion parameterizations, fitted to Azores cloud-probe data, cut uncertainty to about 15% and 5% and reveal drizzle number's role.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of WashingtonUniversity of ReadingColorado State University (incl. CIRA)
A fully connected deep network emulates CRTM clear-sky VIIRS thermal radiances over global oceans, with mean biases near 0.01 K and standard deviations under 0.1 K.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural networks replace gas optics in the RRTMGP radiation scheme, running 1–6 times faster with heating-rate errors below 0.1 K/day, giving 3.5x faster longwave fluxes.
Hybrid physics-ML & parameterizationECMWFNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Random forests, gradient-boosted trees and logistic regression calibrate NOAA Warn-on-Forecast ensemble guidance for tornado, hail and wind, beating updraft-helicity baselines.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Neural networks postprocess ECMWF reforecasts into probabilistic 7-day California precipitation outlooks out to week 4, beating a state-of-the-art method, though week 3-4 skill stays low.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Random forests convert convection-allowing ensemble forecasts into day-1 severe weather probabilities, beating calibrated updraft helicity and SPC outlooks in Brier skill, especially for wind and hail.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Prospectus arguing that the end of Dennard scaling will push climate science toward combining machine learning pattern recognition with physical equations, reshaping how weather and climate are simulated.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Princeton University
Neural networks emulate the RRTMGP gas optics scheme, running up to 4x faster while keeping radiative flux errors within 0.5 W/m2.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Builds the first global nonlinear SVM and neural-network tropical cyclone intensity hindcast schemes; globally trained SVM improves skill by about 10% over regional linear schemes.
Stacked denoising autoencoder with logistic regression predicts thunderstorm occurrence up to 15 hours ahead over 400 km2 South Texas, beating shallow networks and forecasters.
Random forest on HRRR surface fields produces gridded CONUS probabilistic subfreezing road temperature forecasts, with a 2-h Brier skill score of 0.66.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Applies machine learning to satellite fire radiative power to improve fire representation in smoke and weather forecasting models; abstract not available.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Review arguing modern AI can augment or replace parts of satellite remote sensing and NWP processing chains, from observations to forecasts.
Observations & remote sensingColorado State University (incl. CIRA)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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)
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)
Random forest calibrates 12–36 h SREF and HREFv2 precipitation ensembles over CONUS, improving reliability and Brier skill over raw and smoothed probabilities.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
An MLP fuses geostationary and LEO satellite data with dual-polarization ground radar to improve precipitation estimates over Dallas-Fort Worth.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Coevolutionary evolutionary-programming postprocessor improves 72-h surface temperature forecasts and 60-min convection nowcasts over bias-corrected NWP and logistic regression.
Post-processing & calibrationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Random forests, boosted trees and elastic nets predict storm longevity in NWS quasi-operational testing, with lower error than human forecasters.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
Climatology of Red River Valley blizzard patterns from 1979–2018 reanalysis, with a self-organizing map grouping events into an 8-class map.
Climate analysis & attributionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural network combined with 1D-variational retrieval of FY-3C microwave temperature and humidity profiles during Hurricane Matthew, improving WRF data-assimilation track forecasts.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Colorado State University (incl. CIRA)
Neural network classifies typhoon low-altitude turbulence from Hong Kong terminal Doppler radar, validated against commercial aircraft turbulence measurements.
Observations & remote sensingNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
PHURIE uses machine learning on infrared satellite imagery to estimate hurricane intensity; abstract unavailable, classification based on title only.
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)
Discusses hybrid models combining deterministic physics and machine learning components for climate modeling and weather prediction; abstract unavailable.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Neural network emulates CAM longwave radiation 50-80 times faster with near-identical 10-year climate simulations, an early ML physics emulation.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
An early neural network predicts tornadoes from Doppler radar-derived storm attributes. Abstract not available; classification based on title.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES)
