6 papers in the archive.
Compares random forest and neural network emulators of ICON's ecRad radiation; neural nets show top-of-atmosphere bias, and a recurrent design with flux normalisation performs best.
Hybrid physics-ML & parameterizationETH ZurichSwiss Data Science Center (SDSC)
A random forest driven by weather-station data predicts wet-snow avalanche activity in the Swiss Alps, with F1 near 0.8 in nowcast and NWP-driven 24-hour modes.
Data-driven ML models forecast stratospheric sudden warmings better than ECMWF S2S beyond 25 days and boost ensemble skill by up to 20%.
Sub-seasonal to seasonalETH ZurichSwiss Data Science Center (SDSC)
WMO prize challenge winners used AI to post-process ECMWF S2S forecasts, beating bias-corrected references for weeks 3-6 precipitation and temperature.
Sub-seasonal to seasonalColumbia University (LEAP, Lamont-Doherty)Environment and Climate Change Canada (ECCC)ECMWFMax Planck Institute for MeteorologySwiss Data Science Center (SDSC)
Random forest classifiers predict Swiss avalanche danger levels from 20 years of station weather and snow simulations, reaching 72-78% accuracy.
Applications & impactsETH ZurichSwiss Data Science Center (SDSC)
Machine-learning-based detection indicates the climate change signal is identifiable from a single day of global weather; abstract unavailable, so classification is tentative.
Climate analysis & attributionETH ZurichSwiss Data Science Center (SDSC)
