3 papers
physics.ao-ph2025
Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning
Arthur Grundner, Tom Beucler, Julien Savre +3
Cloud-related parameterizations remain a leading source of uncertainty in climate projections. Although machine learning holds promise for Earth system models (ESMs), many data-dri…
quant-ph2025
Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models
Lorenzo Pastori, Arthur Grundner, Veronika Eyring +1
Long-term climate projections require running global Earth system models on timescales of hundreds of years and have relatively coarse resolution (from 40 to 160 km in the horizont…
physics.comp-ph2025
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
Tom Beucler, Arthur Grundner, Sara Shamekh +3
The added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large de…