43 papers in the archive.
A CNN trained on SEVIRI satellite imagery and DARDAR lidar retrievals provides a geostationary observational constraint on cirrus and mixed-phase ice cloud microphysics.
Compares random forest and neural network emulators of ICON's ecRad radiation; neural nets show top-of-atmosphere bias, and a recurrent design with flux normalisation performs best.
Hybrid physics-ML & parameterizationETH ZurichSwiss Data Science Center (SDSC)
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)
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
GPT-4 grounded with IPCC AR6 content via retrieval; expert-scored answers show the hybrid chatClimate more accurate than GPT-4 alone.
Data-driven ML models forecast stratospheric sudden warmings better than ECMWF S2S beyond 25 days and boost ensemble skill by up to 20%.
Sub-seasonal to seasonalETH ZurichSwiss Data Science Center (SDSC)
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
Uses machine learning to map city-scale air temperature from sparse meteorological station data for urban climate adaptation. No abstract available; scope inferred from title.
Linear and random forest models forecast central European summer heatwaves 1–6 weeks ahead, matching ECMWF hindcast ensemble mean beyond two weeks lead.
Sub-seasonal to seasonalETH ZurichMassachusetts Institute of Technology (MIT)
Machine learning classifiers identify individual days of CMIP6 and ICON-Sapphire output versus observations, showing model differences appear at daily timescales.
Gradient boosting and attention-LSTM models predict cirrus ice water content and crystal number (R² = 0.49), with SHAP attributions linking dust aerosols to ice crystal changes.
Deep learning emulator downscales a global climate model to regional Antarctic surface mass balance; the imperfect-model training framework outperforms the perfect-model one.
EasyUQ converts single-valued model output into calibrated predictive distributions via isotonic regression, with a smooth variant and weather forecast examples; the method is general across models.
Post-processing & calibrationETH ZurichKarlsruhe Institute of Technology (KIT)
Neural network post-processing of Swiss surface forecasts enforces thermodynamic equations, yielding physically consistent temperature and humidity without losing accuracy, especially with scarce data.
A statistical GAM emulator predicts local surface temperature responses to tree cover change from ESM output, offering a cheap complement to greenhouse-gas emulators.
Climate emulationETH ZurichKNMI (Royal Netherlands Meteorological Institute)Max Planck Institute for BiogeochemistryMax Planck Institute for Meteorology
Coordinate-based neural networks compress weather and climate grids by 300x to 3000x, beating SZ3 on error, and a 790x compressed WeatherBench loader raises RMSE by under 2%.
Introduces ClimateBench, a CMIP-based benchmark with baseline ML emulators predicting temperature and precipitation responses to emissions scenarios.
Benchmarks, datasets & evaluationETH ZurichMET Norway (Norwegian Meteorological Institute)University of Oxford
Classification algorithms detect and predict Alpine foehn events from GNSS troposphere products at Altdorf, reaching 70-85% detection and alarm measures.
MESMER-X statistical emulator reproduces spatially resolved annual maximum temperatures of Earth system models using global mean temperature as the driver.
Machine learning attributes aerosol climate forcing largely to increased cloud cover. Abstract unavailable; judged from the title only.
Climate analysis & attributionNASAMet OfficeETH ZurichUniversity of Cambridge (Aardvark)
Bayesian inference on a stochastic energy balance model (ClimBayes) emulates global temperature variability and separates forced from internal variance across decadal to centennial scales.
Random forest classifiers predict Swiss avalanche danger levels from 20 years of station weather and snow simulations, reaching 72-78% accuracy.
Applications & impactsETH ZurichSwiss Data Science Center (SDSC)
MESMER-M extends an Earth system model emulator to monthly local temperatures, emulating 38 CMIP6 models with skewed, spatially correlated variability.
Chains the MAGICC global emulator with the MESMER spatial emulator to produce millions of local temperature projections per emission pathway, validated against CMIP6 ensembles.
Introduces ENS-10, a 20-year, 10-member perturbed NWP ensemble dataset at 0.5° for ML post-processing, with baselines for 48-hour correction and extremes.
An XGBoost model post-processes ECMWF EPS outputs to correct systematic negative biases in tropical cyclone maximum wind forecasts over the western North Pacific, 2016–2019.
ClimateBERT is a language model pretrained on climate-related text; it does not target the atmosphere or climate system itself.
Conditional GAN and dense networks postprocess COSMO-E and IFS cloud cover ensembles over Switzerland, with cGAN producing realistic scenarios without copula or shuffle templates.
XGBoost predicts Swiss foehn from synoptic fields in reanalysis and CESM simulations, projecting more spring south-foehn in Altdorf and summer north-foehn in Lugano under warming.
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.
Deep convolutional networks reconstruct high-resolution weather and climate fields from low-resolution simulations, and generalize to observed data for precipitation and other variables.
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
Quantile regression forest fuses 691 crowd-sourced citizen weather stations with open data to map Zurich urban temperature at 10 m resolution during a 2019 heatwave.
Philosophical framework arguing data-driven machine learning climate models can provide scientific understanding, illustrated with an atmospheric research case study.
A deep neural network post-processes a subset of ensemble trajectories, improving CRPS by over 14% and allowing fewer members to match the full ensemble.
MESMER statistically emulates grid-point land temperature from CMIP5 models, producing a 40-model superensemble at negligible computational cost.
Machine-learning-based detection indicates the climate change signal is identifiable from a single day of global weather; abstract unavailable, so classification is tentative.
Climate analysis & attributionETH ZurichSwiss Data Science Center (SDSC)
Deep 3D U-Net variants estimate weather forecast ensemble spread from a small set of numerical simulations, reducing the compute needed for uncertainty quantification.
Neural networks and quantile trees nowcast precipitation growth and decay in Swiss Alps radar archive, with persistence inputs cutting RMSE by 20-30% in mountains.
An optical instrument uses a random forest to classify cloud particle phase, achieving under 5% misclassification above 3 µm in lab tests.
Position paper surveying how machine learning can help climate change mitigation and adaptation across sectors such as energy and disaster management; not focused on weather or climate science.
Applications & impactsETH ZurichGoogle DeepMindGoogle ResearchLawrence Berkeley National Laboratory / NERSCMassachusetts Institute of Technology (MIT)New York University (M2LInES)Stanford UniversityUniversity of California, Berkeley
Uses machine learning on sub-national yield data to show climate extremes explain 18-43% of yield-anomaly variance, with temperature extremes dominating global crop losses.
Applications & impactsETH ZurichPotsdam Institute for Climate Impact Research (PIK)
Interactive visualization system helps users inspect recurrent convolutional network forecasts of temperature, pressure and wind trained on 25 years of climate data.
