12 papers in the archive · tier-1 watchlist.
A deep generative nowcasting model with a balanced loss and temperature input extends skillful heavy-rain nowcasts over the Netherlands to 60-minute lead times.
Machine-learning post-processing of hourly HARMONIE and ERA5 forecasts predicts 1-minute irradiance variability for Dutch stations, with NWP radiation errors causing about half the inaccuracy.
Post-processing & calibrationKNMI (Royal Netherlands Meteorological Institute)
An explainable ANN corrects ECMWF extended-range European summer temperature exceedance probabilities and traces errors to western Pacific SST teleconnections, improving skill.
Post-processing & calibrationKNMI (Royal Netherlands Meteorological Institute)
Statistical ML seasonal forecasts of poor US soybean harvests from Pacific SST and soil moisture precursors reach 67% correct at three months before sowing.
Applications & impactsKNMI (Royal Netherlands Meteorological Institute)
A statistical GAM emulator predicts local surface temperature responses to tree cover change from ESM output, offering a cheap complement to greenhouse-gas emulators.
Climate emulationETH ZurichKNMI (Royal Netherlands Meteorological Institute)Max Planck Institute for BiogeochemistryMax Planck Institute for Meteorology
Random forests with explainable AI on ERA5 identify drivers of subseasonal high summer temperatures in Europe, with useful skill for warm anomalies at 15-day lead.
Sub-seasonal to seasonalKNMI (Royal Netherlands Meteorological Institute)Potsdam Institute for Climate Impact Research (PIK)
Combines GANs with extreme value theory (evtGAN) to model spatial dependence of temperature and precipitation extremes over western Europe, outperforming classical GANs using a 2000-year simulation.
Extremes & hazardsKNMI (Royal Netherlands Meteorological Institute)
Support vector regression corrects rain-affected Ku-band scatterometer winds over the tropics, reducing rain-induced bias against ASCAT as reference.
Observations & remote sensingKNMI (Royal Netherlands Meteorological Institute)
CNNs postprocess HARMONIE-AROME wind speed forecasts over the Netherlands using full spatial patterns, improving CRPS and Brier skill over fully connected networks and quantile regression forests at 48 h.
Post-processing & calibrationKNMI (Royal Netherlands Meteorological Institute)
A machine learning fit of a six-parameter Langevin model reproduces AMOC collapse trajectories from intermediate-complexity climate models under freshwater forcing.
Climate analysis & attributionKNMI (Royal Netherlands 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
Compares ELR, ZAGA and quantile regression forests for postprocessing HARMONIE-AROME hourly precipitation; QRF is most skillful for high thresholds.
Post-processing & calibrationKNMI (Royal Netherlands Meteorological Institute)
