17 papers in the archive.
Case study of Storm Ciarán finds FourCastNet, Pangu, and GraphCast capture synoptic structure but underestimate peak winds and miss fine warning-relevant features.
Lightweight deep learning detects tropical cyclone presence during simulations for online data reduction, reaching 92% recall on held-out ERA-Interim years.
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
SeaUnet, a fully convolutional network trained on CMIP TC tracks, gives skillful seasonal forecasts of Western North Pacific typhoon track density up to four months ahead.
Sub-seasonal to seasonalTsinghua University (NowcastNet)University of ReadingChina Meteorological Administration (CMA)
Implements a neural-network gas optics scheme in ECMWF's IFS, speeding radiation by about one third with a similar model climate in 1-year coupled runs.
Hybrid physics-ML & parameterizationECMWFUniversity of Reading
Regression trees, MLPs, CNNs and LSTMs estimate local Lyapunov exponents from trajectories of Lorenz-63 and Rössler systems, a feasibility study motivated by weather predictability.
A neural-network-driven stochastic cloud population model parameterizes cumulus convection in WRF, reproducing realistic precipitation statistics, MJO propagation and diurnal cycle in regional tests.
Neural networks correct the ecRad 1D radiation scheme for 3D cloud effects, recovering 3D signal with 20-30% error at about 1% extra runtime versus the 5x costlier SPARTACUS.
Hybrid physics-ML & parameterizationECMWFUniversity of Reading
A lightweight deep learning filter detects tropical cyclones in running simulations with 92% recall and 36% precision, so models save only relevant output.
Online sequential-learning combinations of NWP and statistical extended-range forecasts improve probabilistic skill by 5-15% over standard multimodel methods up to lead week 5.
Post-processing & calibrationColumbia University (LEAP, Lamont-Doherty)University of Reading
DGMR, a deep generative model of radar, produces skillful 5-90 minute precipitation nowcasts that Met Office experts ranked first in 88% of cases.
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)
Machine learning maps short-term climate model responses to long-term temperature change under forcing scenarios, enabling cheaper climate projections.
Ensemble Kalman filter estimates model error from sparse noisy observations to train a neural parametrisation, yielding hybrid forecasts with better skill on Lorenz and a coupled ocean-atmosphere model.
Data assimilationLawrence Berkeley National Laboratory / NERSCUniversity of Reading
Machine learning with PCA, ANN and quantile mapping downscales GCM output to 30 m sub-daily temperature, humidity and wind in complex terrain, improving on GCM accuracy.
Uses machine learning on Darwin C-band radar data to derive transition functions for a stochastic convective-stratiform cloud model aimed at global climate model parameterization.
Hybrid physics-ML & parameterizationUniversity of ReadingBureau of Meteorology (Australia)
Iterating an ensemble Kalman filter with a neural network emulates the chaotic Lorenz-96 model from sparse noisy observations, giving forecast skill up to two Lyapunov times.
