14 citations · 34 across the 16 of their papers we have counts for
9 papers · 1 filter
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional tempe…
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Jurij Schönfeld, Tom Beucler, Julien Savre +2
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, th…
Shadow models of a quantum model for cloud cover and the influence of finite sampling noise
Hedwig Keller, Mierk Schwabe, Veronika Eyring
Quantum computing is a quickly growing field that is promising various advantages compared to conventional computing. However, currently stand-alone quantum applications are scarce…
How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello +7
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalab…
From stable online coupling to decade-long climate simulations: A machine learning parameterization for cloud microphysics in ICON
Ellen Sarauer, Mierk Schwabe, Philipp Weiss +3
The representation of cloud microphysics and its nonlinear character and scale-dependence is a remaining source of uncertainty in Earth system models (ESMs). Here, we develop and c…
PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
Emma Kasteleyn, Timo Maier, Axel Lauer +3
Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-b…
Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
Elias Haslauer, Mierk Schwabe, Andreas Dörnbrack +4
State-of-the-art Earth system models (ESMs) cannot explicitly resolve many small-scale atmospheric processes such as atmospheric gravity waves, and thus must represent, or paramete…
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
Kevin Debeire, Aytaç Paçal, Pierre Gentine +3
Regional climate information at kilometer scales is essential for assessing the impacts of climate change, but generating it with global climate models is too expensive due to thei…
Quantum-Enhanced Convergence of Physics-Informed Neural Networks
Nils Klement, Veronika Eyring, Mierk Schwabe
Partial differential equations (PDEs) form the backbone of simulations of many natural phenomena, for example in climate modeling, material science, and even financial markets. The…