86 papers in the archive.
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)
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)
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
A CNN generates dynamically consistent counterfactual extreme heat events, estimating the 2023 southcentral North America heat was 1.18–1.42°C warmer due to warming.
Climate analysis & attributionColorado State University (incl. CIRA)Stanford University
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
A residual U-Net postprocesses RAP forecasts with satellite data, improving dewpoint profiles and CAPE by up to 40% with well-calibrated uncertainty for severe-weather forecasting.
Post-processing & calibrationColorado State University (incl. CIRA)
U-Net models estimate aviation turbulence intensity at three altitude layers from GOES-16 ABI imagery; U-Net3+ matches pilot reports best.
Observations & remote sensingColorado State University (incl. CIRA)
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)
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)
Attentional GAN with a spatial variability loss reduces the blurry effect in radar nowcasts and outperforms competing models on the test set and storm cases.
NowcastingTsinghua University (NowcastNet)Colorado State University (incl. CIRA)Institute of Atmospheric Physics, Chinese Academy of SciencesChina Meteorological Administration (CMA)
CNNs with partial dependence analysis reveal nonlinear soil moisture–temperature coupling across mid-latitude regions using reanalysis data.
Climate analysis & attributionStanford UniversityColorado State University (incl. CIRA)
Explainable neural networks trained on Earth system model output quantify climate distinguishability after stratospheric aerosol injection, showing slower change under SAI.
Climate analysis & attributionColorado State University (incl. CIRA)
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)
Boosted trees and random forests map satellite SIF to GPP to calibrate E3SM land photosynthesis parameters, reducing simulated GPP uncertainty after surrogate-based benchmarking.
Hybrid physics-ML & parameterizationOak Ridge National LaboratoryColorado State University (incl. CIRA)
ANNs trained on climate model output estimate observed timing of 1.5°C and 2°C warming, with a 2033–2035 central estimate for 1.5°C.
Climate analysis & attributionColorado State University (incl. CIRA)Stanford University
Explainable neural network on 100 MPI Grand Ensemble members identifies spring tripolar North Atlantic SST patterns as precursors of European summer heatwaves.
Climate analysis & attributionMax Planck Institute for MeteorologyColorado State University (incl. CIRA)
Builds a recurrent neural network fuel moisture model for WRF-SFIRE, initialized as a numerical ODE solver and trained on RAWS sensor and RTMA weather data to improve fire-weather inputs.
Logistic regression and neural networks show stratospheric aerosol injection signals in regional temperature emerge within a decade, while precipitation signals are harder to detect.
Climate analysis & attributionColorado State University (incl. CIRA)Princeton University
Deep CNNs with dense blocks estimate hourly rainfall from polarimetric WSR-88D radar, reaching MAE 1.58 mm and beating traditional Z-R relations.
Observations & remote sensingColorado State University (incl. CIRA)
Pixel-CRN nowcasts convective storms 30 and 60 min ahead from 3-D radar and reanalysis, and works with only a small training dataset.
Sinh-arcsinh-normal output distributions give neural networks asymmetric, heteroscedastic uncertainty, demonstrated on tropical cyclone intensity forecast errors.
Probabilistic & ensemble forecastingColorado State University (incl. CIRA)
CNNs predict daily maximum temperature from soil moisture and geopotential, and partial dependence reveals nonlinear soil moisture-temperature coupling across mid-latitude regions.
Climate analysis & attributionStanford UniversityColorado State University (incl. CIRA)
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)
Scattering-transform features with channel attention from multi-band satellite imagery estimate tropical cyclone intensity and predict lightning occurrence.
Probabilistic neural GPROF-NN retrieves GPM surface precipitation, with a 3D CNN variant, improving GMI mean absolute error from 0.079 to 0.059 mm/h.
Observations & remote sensingColorado State University (incl. CIRA)
Shows XAI attributions for a climate-signal network change substantially with the chosen baseline, using large-ensemble SSP3-7.0 simulations, so baseline choice must match the science question.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
Uncertainty-aware regression network in CESM2 finds low-uncertainty initial states for decadal sea surface temperature predictability linked to Atlantic and Pacific variability.
Climate analysis & attributionColorado State University (incl. CIRA)
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)
Trains a shallow neural network to identify which climate model produced Arctic temperature maps, then applies it to reanalysis, using attribution to reveal model-specific regional patterns.
Climate analysis & attributionColorado State University (incl. CIRA)
Explainable neural networks using layer-wise relevance propagation combine multivariate climate fields to detect forced climate change earlier than single fields.
Climate analysis & attributionColorado State University (incl. CIRA)
Neural networks quantify how warming alters midlatitude subseasonal skill from tropical precipitation in CESM2 large ensemble, linked to seasonal variability changes.
Sub-seasonal to seasonalColorado State University (incl. CIRA)
Neural network with explainability predicts decadal global warming slowdowns from ocean heat content in CESM2 large ensemble, linking them to Interdecadal Pacific Oscillation-like patterns and testing on observations.
Climate analysis & attributionColorado State University (incl. CIRA)
Shallow neural networks predict the real-time MJO index out to about 18 days in October–March, outperforming linear models and explained via attribution techniques.
Sub-seasonal to seasonalColorado State University (incl. CIRA)
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)
Uses a machine learning framework to observationally constrain 13 CMIP6 Earth system models, projecting higher socioeconomic wildfire exposure in Africa despite lower fire carbon emissions.
Extremes & hazardsOak Ridge National LaboratoryColorado 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)
Benchmarks XAI attribution methods on a CNN that counts atmospheric rivers in climate simulations, exposing fidelity issues such as gradient shattering and sign ambiguity.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
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)
An artificial neural network maps CubeSat TEMPEST-D microwave brightness temperatures to surface rainfall, reaching 0.78 structural similarity on independent storms against MRMS radar.
Observations & remote sensingColorado State University (incl. CIRA)
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)
Causal-predictive ML links tropical cyclone exposure to mortality and hospitalization in Medicare recipients, finding substantial respiratory hospitalization increases with wind speed as main driver.
Applications & impactsColumbia University (LEAP, Lamont-Doherty)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)
Systematic study of deep learning storm nowcasting design choices (history length, loss, predicted frames) from radar data, yielding models that outperform existing implementations.
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)
QPENet deep learning on dual-polarization radar, trained on 11 typhoons, beats DSD-based parametric QPE for rainfall estimation in South China.
Observations & remote sensingNanjing University of Information Science & Technology (NUIST)Colorado State University (incl. CIRA)China Meteorological Administration (CMA)
U-Net on five radar frames predicts convective reflectivity, matching TrajGRU and outperforming TREC with a simpler CNN.
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)
Random forests trained on convection-allowing WRF output probabilistically forecast excessive rainfall over CONUS, but they consistently underperform WPC's day-1 Excessive Rainfall Outlooks.
Residual convolutional encoder-decoder models for radar echo nowcasting match or beat operational systems and transfer across regions without performance decay.
CU-net, a CNN, corrects gridded ECMWF-IFS 24–240 h forecasts of four surface variables over North China, beating the ANO correction at all lead times.
Post-processing & calibrationColorado State University (incl. CIRA)China Meteorological Administration (CMA)
Layer-wise relevance propagation attributes regional temperature signals to aerosol versus greenhouse forcing in single-forcing large ensembles.
Climate analysis & attributionColorado State University (incl. CIRA)
Explainable neural networks with layerwise relevance propagation identify North Atlantic and North Pacific SST regions that drive decadal predictability of continental temperature in an Earth-system model.
Climate analysis & attributionColorado State University (incl. CIRA)
An MLP using SHIPS predictors beats operational statistical-dynamical models by 5-22% for Atlantic tropical cyclone intensity in 2019-2020 real-time tests.
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)
Neural network identifies subseasonal forecasts of opportunity over the North Atlantic at 22-day lead, with layer-wise relevance propagation revealing tropical predictors.
Sub-seasonal to seasonalColorado State University (incl. CIRA)
A convolutional network on one-minute GOES-16 visible and infrared imagery detects convection, trained against MRMS radar precipitation types as a proof of concept.
Observations & remote sensingColorado State University (incl. CIRA)
Synthetic attribution benchmark for XAI in generic geoscience regression problems; not specifically about weather or climate.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
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)
Interpretable neural networks forecast MJO convection several weeks ahead, using layer-wise relevance propagation to explain sources of predictability compared with dynamical models.
Sub-seasonal to seasonalColorado State University (incl. CIRA)
Transfers a CNN radar nowcaster trained near Beijing to Guangzhou via fine-tuning and MMD, improving skill with only small target-domain datasets.
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
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
Layer-wise relevance propagation applied to neural networks identifies atmospheric states associated with enhanced weather predictability, giving physical insight into predictability mechanisms.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
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 CNN called GREMLIN fuses GOES-R ABI and GLM data into synthetic radar reflectivity, proposed to feed convective-scale data assimilation and latent heating initialization.
Observations & remote sensingColorado State University (incl. CIRA)
CNNs infer airmass properties such as moisture and instability from GOES-R ABI imagery, offering an automated alternative to sparse radiosondes for NWP and forecasters.
Observations & remote sensingColorado State University (incl. CIRA)
Tutorial-style guidance on evaluating, tuning and interpreting neural networks for meteorological satellite images, covering receptive fields, relevance propagation and image-to-image translation.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
A neural network trained to predict the year from climate model temperature and precipitation maps, with relevance visualization, reveals indicator patterns of forced climate change.
Climate analysis & attributionColorado State University (incl. CIRA)
Practical guidance on evaluating, tuning and interpreting meteorological neural networks, covering effective receptive fields, relevant performance measures, and layer-wise relevance propagation for image classification and translation.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
Random forests trained on GEFS reforecasts give calibrated Day 1-3 severe weather outlooks that beat SPC at Days 2-3, and blending with SPC improves Day 1 outlooks.
A CNN converts GOES-R radiances and lightning into synthetic radar reflectivity, enabling convective radar assimilation with much better skill than pixel-wise methods.
Observations & remote sensingColorado State University (incl. CIRA)
Support vector machines and neural networks detect non-Gaussian error distributions in the Lorenz-63 model, informing relaxation of Gaussian assumptions in data assimilation.
Logistic regression with linear baroclinic model simulations shows the past 15 days of MJO activity improve midlatitude circulation predictability beyond 15 days ahead.
Sub-seasonal to seasonalColorado State University (incl. CIRA)
Applies backwards optimization and layerwise relevance propagation to neural networks, recovering scientifically meaningful patterns in climate variability data.
Explainability, uncertainty & physicsColorado State University (incl. CIRA)
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)
A neural network trained to predict year from temperature maps detects forced climate patterns without explicitly separating signal from noise.
Climate analysis & attributionColorado State University (incl. CIRA)
Evaluates a neural-network hurricane impact-level forecast over 2017 storms, finding best accuracy about 30 h before landfall and wind speed as the key driver.
Applications & impactsColorado State University (incl. CIRA)
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)
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)
Retrieves temperature and humidity profiles from NPP CrIS hyperspectral radiances using autoencoders and a self-normalizing network trained on ECMWF data.
Observations & remote sensingColorado State University (incl. CIRA)
Random forests trained on 11 years of GEFS reforecasts give better CONUS extreme precipitation exceedance forecasts than raw ensembles and ECMWF.
Post-processing & calibrationColorado State University (incl. CIRA)
Accessible introduction to constraint-based causal discovery for climate scientists, illustrated on boreal winter atmospheric low-frequency modes.
Climate analysis & attributionColorado State University (incl. CIRA)
