8 papers in the archive · tier-1 watchlist.
Graph-based stretched-grid model refines a global AI model to 2.5 km over the Nordics, beating the MEPS ensemble mean for 2 m temperature but underestimating extremes.
Regional & km-scale modelsECMWFMET Norway (Norwegian Meteorological Institute)
Evaluates Pangu-Weather against ECMWF and MEPS at 183 Norwegian stations; probabilistic post-processing helps global models most, though MEPS remains best.
Post-processing & calibrationMET Norway (Norwegian Meteorological Institute)
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
NeXtNow, a ResNeXt-based encoder-decoder, predicts future radar frames from Romanian and Norwegian data, improving CSI by 1–17% over XNow.
Slide deck on machine learning classification of cloud types in satellite observations and climate models; no abstract or quantitative results provided.
Observations & remote sensingMET Norway (Norwegian Meteorological Institute)
A tower network fusing multiple data streams gives 11% lower temperature forecast RMSE than yr.no, beating CNN and convLSTM baselines.
Local & station forecastingMET Norway (Norwegian Meteorological Institute)
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
Neural-network quantile function regression with Bernstein polynomials postprocesses 60 h ensemble wind speed at 125 Norwegian stations, improving quantile skill score by nearly 1%.
Post-processing & calibrationMET Norway (Norwegian Meteorological Institute)
