39 papers in the archive.
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)
Nature Reviews review of deep learning's impact on weather and climate prediction; abstract absent, judged from title and venue.
Global medium-range NWPECMWFETH ZurichMax Planck Institute for MeteorologyUniversity of Oklahoma (AI2ES)
A deep neural network combining GOES cloud-top satellite data with reanalysis parameters estimates severe hail likelihood, reaching CSI 0.511 against radar MESH.
Deep learning detects cold, warm, stationary and occluded fronts from ERA5 over the US, reaching CSI 0.60 for cold fronts and 0.71 for front/no-front.
Federated learning with GAN-based oversampling is applied to imbalanced tabular weather data; gains are reported without numbers.
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)
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)
Neural networks forecast Nigerian weather parameters from 19 years of NASA data to assess solar PV siting, reaching 96% training correlation.
Local & station forecastingMassachusetts Institute of Technology (MIT)University of Oklahoma (AI2ES)
Self-organizing maps identify Eastern US atmospheric river types and show rising trends for Nor'easter and Gulf of Mexico ARs.
Climate analysis & attributionUniversity of Oklahoma (AI2ES)
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)
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)
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)
Uses CNN models on IMERG satellite precipitation to forecast near-real-time rainfall from hurricanes, targeting flood-relevant extreme rainfall.
Symmetry-preserving CNN subgrid closures for 2D Kraichnan turbulence respect translation, Galilean and rotation invariance and stay stable in coarse simulations.
Hybrid physics-ML & parameterizationUniversity of Oklahoma (AI2ES)
Title-only record for ML-inspired weather forecasting applied to solar PV potential; no abstract is available to confirm methods or results.
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)
Random forest trained on 150,000 ERA5 proximity soundings best classifies significant tornadoes and hail, with SHAP-derived thresholds for operational nowcasting.
Uses machine learning to model the economic impact of incoming tropical cyclones. The abstract is missing, so this classification is low-confidence.
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)
Deep neural networks on UAS orthomosaics detect and classify tornado damage, producing georeferenced damage heatmaps for post-event assessment after the 2018 Eureka tornado.
Random forests combining GOES-16 satellite, lightning and radar fields improve severe storm warning prediction over radar alone, identifying cloud-top divergence as most informative.
NowcastingNASAColorado State University (incl. CIRA)University of Oklahoma (AI2ES)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
An LSTM extrapolates cloud-top brightness temperature, then PERSIANN retrieves 0–6 h rainfall, beating RNN, optical flow and RAP over three US regions.
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)
An AMS 2018 conference abstract on applying machine learning to reduce tropical cyclone prediction errors. No abstract is available, so the assessment is low confidence.
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)
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)
