13 papers in the archive.
Aurora is a large Earth-system foundation model trained on over a million hours of data that beats operational systems for air quality, waves, cyclone tracks and high-resolution weather.
Foundation modelsThe Alan Turing InstituteMicrosoft Research (AI for Science)Silurian AIUniversity of Cambridge (Aardvark)
End-to-end model ingests raw observations to produce global and station forecasts, beating operational NWP on several variables using 8% of input data.
Data assimilationThe Alan Turing InstituteECMWFGoogle DeepMindMicrosoft Research (AI for Science)University of Cambridge (Aardvark)
A probabilistic physically informed recurrent network learns stochastic parameterizations in the Lorenz 96 atmosphere model, outperforming autoregressive and GAN baselines on temporal patterns.
Hybrid physics-ML & parameterizationThe Alan Turing InstituteUniversity of Cambridge (Aardvark)University of Oxford
CorrectorGAN jointly bias-corrects and super-resolves global ensemble precipitation forecasts over the US against radar, approaching an operational regional model in seconds.
Downscaling & super-resolutionThe Alan Turing InstituteUniversity of Oxford
Convolutional conditional neural processes provide off-grid multisite temperature and precipitation downscaling over Europe, beating existing methods and improving extreme precipitation representation.
Downscaling & super-resolutionThe Alan Turing InstituteUniversity of Cambridge (Aardvark)
Uses a Bayesian neural network to fuse chemistry-climate model output with sparse satellite and sonde data, producing a continuous vertically resolved stratospheric ozone record with uncertainty.
Observations & remote sensingUniversity of Cambridge (Aardvark)The Alan Turing Institute
Applies Gaussian process emulators and history matching to global parameter search in a climate model, framing uncertainty quantification as essential for model reproducibility.
Explainability, uncertainty & physicsThe Alan Turing Institute
Calibrates cloud geometry parameters for 3D radiation using ML surrogate emulators, reducing top-of-atmosphere and surface flux errors from about 10 to 5 W/m2.
Hybrid physics-ML & parameterizationECMWFMétéo-France (incl. CNRM)The Alan Turing Institute
Oxford workshop position on AI post-processing for weather and climate, releasing five archived ML-ready datasets with baseline notebooks to encourage trustworthy, FAIR and physically informed methods.
Post-processing & calibrationThe Alan Turing InstituteKarlsruhe Institute of Technology (KIT)Met OfficeNSF NCAR / UCAR
Gaussian process calibration tunes boundary-layer parameterization parameters against large-eddy simulations in single-column mode, before global model tuning.
Hybrid physics-ML & parameterizationMétéo-France (incl. CNRM)The Alan Turing Institute
Emulator-based calibration of an atmospheric model's parameterizations against large-eddy simulations matches hand-tuned global performance while keeping process-level behaviour physically sound.
Hybrid physics-ML & parameterizationMétéo-France (incl. CNRM)The Alan Turing Institute
Cumulo is a global dataset pairing one year of 1km MODIS imagery with CloudSat cloud labels, with a flow-based baseline for cloud classification.
Benchmarks, datasets & evaluationUniversity of OxfordThe Alan Turing Institute
Nonlinear causal discovery method estimates interdependency networks from large generic time series datasets.
Climate analysis & attributionThe Alan Turing InstitutePotsdam Institute for Climate Impact Research (PIK)University of Oxford
