22 papers in the archive · tier-1 watchlist.
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)
ACE2 produces skilful winter seasonal forecasts from SST-persisted initialisation, reaching NAO correlation 0.47 over 1993–2016, comparable to a physics-based model.
Sub-seasonal to seasonalAllen Institute for AI (Ai2, ACE)Met Office
A diffusion model (CPMGEM) emulates 2.2 km UK convection-permitting precipitation from 60 km GCM inputs, reproducing extremes up to about 100-year return times at much lower cost.
A deep-learning segmentation model with transfer learning from synthetic fields detects and characterises trapped lee waves hazardous to aviation, faster than spectral methods.
Neural network emulates the Met Office nonorographic gravity wave scheme using only two of six inputs, and reproduces QBO and ENSO-linked gravity wave variability in a climate model.
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)
Releases EUPPBench, a time-aligned forecast-observation dataset with evaluation methods and a first benchmark of 2 m temperature postprocessing.
Benchmarks, datasets & evaluationDeutscher Wetterdienst (DWD)ECMWFKarlsruhe Institute of Technology (KIT)Met OfficeMétéo-France (incl. CNRM)MeteoSwisscode
Surrogate-based statistical framework constrains UKESM1 aerosol parameters against MODIS observations on a cloud platform, using 3-hourly outputs for tighter constraints.
Climate analysis & attributionMet OfficeUniversity of Oxford
Diffusion models generate realistic high-resolution UK rainfall samples conditioned on low-resolution simulations, matching the high-resolution model's quantiles and time-mean.
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)
DGMR, a deep generative model of radar, produces skillful 5-90 minute precipitation nowcasts that Met Office experts ranked first in 88% of cases.
Neural networks predict full probability density functions for WeatherBench geopotential and temperature at 3 and 5 days, stacked into a combined forecast that beats some NWP models.
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
Ridge, Lasso, random forest and extra trees emulate UKESM1 tropical stratospheric ozone projections under SSPs, matching the model when trained on future simulations.
Climate emulationMet OfficeUniversity of Cambridge (Aardvark)
A Gaussian process emulator with an augmented bias term corrects Amazon climate biases in a land-surface model and identifies plausible parameter values despite structural error.
An artificial neural network estimates IASI cloud-top pressure and fraction closer to optimal values, cutting retrieval time and improving 1D-Var cloudy-sky assimilation with near-neutral forecast impact.
Data assimilationMet OfficeKorea Meteorological Administration (KMA)
Quantile regression forests post-process and combine multiple NWP models across lead times into calibrated probabilistic road surface temperature forecasts.
Review of statistical postprocessing methods for NWP forecasts and their challenges in big-data settings, with little explicit machine learning focus.
Post-processing & calibrationDeutscher Wetterdienst (DWD)ECMWFKarlsruhe Institute of Technology (KIT)KNMI (Royal Netherlands Meteorological Institute)MET Norway (Norwegian Meteorological Institute)Met OfficeMétéo-France (incl. CNRM)MeteoSwiss
Statistical emulators built from perturbed-physics ensembles iteratively tune weather@home parameters, cutting northwestern US warm biases by 74%.
Review of gust forecasting methods, comparing physical boundary-layer parametrizations on NWP fields with statistical and machine learning approaches trained on observations, and outlining challenges from higher resolution.
Neural network retrieves snow and liquid water path and water vapour from airborne millimetre radiometer data; submillimetre channels halve snow retrieval error.
AMS conference paper on opportunities for machine learning in weather forecasting; no abstract available, so content is inferred from the title.
