19 papers in the archive.
Nature Communications review of AI for modeling and understanding extreme weather and climate events; abstract absent, judged from title and venue.
Extremes & hazardsInriaMax Planck Institute for BiogeochemistryTechnical University of Munich (TUM)
Unsupervised learning finds three propagation modes of Indo-Pacific rainfall extremes linked to Pacific SST, with early warning skill up to four weeks ahead.
Climate analysis & attributionTechnical University of Munich (TUM)Potsdam Institute for Climate Impact Research (PIK)
Physically constrained cGANs correct CMIP6-class Earth system model precipitation biases in both local distributions and spatial patterns, outperforming existing methods on extremes' spatial intermittency.
Post-processing & calibrationPotsdam Institute for Climate Impact Research (PIK)Technical University of Munich (TUM)
Argues that making Earth system models automatically differentiable enables objective calibration and hybrid ML components, targeting climate sensitivity, spatial biases and abrupt transitions.
Hybrid physics-ML & parameterizationPotsdam Institute for Climate Impact Research (PIK)Technical University of Munich (TUM)
A physically constrained GAN corrects Earth system model precipitation, fixing local distributions and the double-peaked ITCZ while preserving global precipitation sums under future scenarios.
Downscaling & super-resolutionPotsdam Institute for Climate Impact Research (PIK)Technical University of Munich (TUM)
Echo state network trained on low-frequency ENSO indices skilfully predicts El Niño up to 21 months ahead, with correlation above 0.5.
Sub-seasonal to seasonalTechnical University of Munich (TUM)Potsdam Institute for Climate Impact Research (PIK)
Extends WeatherBench with probabilistic metrics, an IFS ensemble baseline and three probabilistic ML models; Monte Carlo dropout underestimates uncertainty and no ML model matches IFS.
Benchmarks, datasets & evaluationTechnical University of Munich (TUM)
Applies neural networks and SHAP to predict European drought one month ahead from a 50-member regional climate large ensemble, with about 55–60% correct event classification.
Deep networks trained on an NWP ensemble with a frequency-weighted loss correct rainfall, improving extreme-event forecast skill by factors of two to above six.
Post-processing & calibrationPotsdam Institute for Climate Impact Research (PIK)Technical University of Munich (TUM)
Data-driven nonlocal turbulence model whose covariance kernel is partly a neural network, calibrated to 1968 Kansas field data and more accurate than the IEC standard.
Hybrid physics-ML & parameterizationTechnical University of Munich (TUM)
A residual CNN pretrained on climate model output and fine-tuned on reanalysis gives WeatherBench 5-day forecasts that outperform prior submissions and rival a physical baseline.
Compares CNN architectures and proposes DeepRU, a U-Net variant, to downscale ERA5-driven 100 m wind forecasts from 31 km to 9 km over the Alps.
Downscaling & super-resolutionECMWFTechnical University of Munich (TUM)
WeatherBench introduces an ERA5-derived benchmark with standard metrics and baselines (linear regression, deep learning, physical models) for data-driven medium-range global weather forecasting.
Benchmarks, datasets & evaluationECMWFTechnical University of Munich (TUM)University of Washingtoncode
WeatherBench provides an ERA5-derived benchmark with metrics and baselines for data-driven global medium-range forecasting, enabling direct model comparison.
Benchmarks, datasets & evaluationECMWFUniversity of WashingtonTechnical University of Munich (TUM)code
Architectural and loss-based constraints make neural emulators of convection conserve physical laws to machine precision without degrading accuracy.
Explainability, uncertainty & physicsColumbia University (LEAP, Lamont-Doherty)Technical University of Munich (TUM)
Proposes coupled online learning, where a neural parameterization runs alongside a high-resolution simulation with nudging, to reduce instabilities and biases; illustrated on Lorenz 96.
Hybrid physics-ML & parameterizationTechnical University of Munich (TUM)code
Crowd-labelled about 50,000 shallow cloud clusters in satellite images to train deep learning that maps four organization patterns and builds global climatologies.
Observations & remote sensingMax Planck Institute for MeteorologyTechnical University of Munich (TUM)
Compares neural networks, regression trees and Nexting for short-term local weather trend forecasts from historical data; no single method clearly wins, with Nexting good for slow signals.
Local & station forecastingTechnical University of Munich (TUM)
LSTM and feedforward networks downscale CMIP5 precipitation for the Mekong Delta, projecting 20-25% wet-season increases and dry-season decreases for 2036-2065.
Downscaling & super-resolutionTechnical University of Munich (TUM)
