67 citations · 96 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Data-Driven Equation Discovery of a Cloud Cover Parameterization
Arthur Grundner, Tom Beucler, Pierre Gentine +1
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output…
Deep Learning Based Cloud Cover Parameterization for ICON
Arthur Grundner, Tom Beucler, Pierre Gentine +3
A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-reso…