14 citations · 34 across the 16 of their papers we have counts for
8 papers · 1 filter
Quantum Bayesian Optimization for the Automatic Tuning of Lorenz-96 as a Surrogate Climate Model
Paul J. Christiansen, Daniel Ohl de Mello, Cedric Brügmann +6
In this work, we propose a hybrid quantum-inspired heuristic for automatically tuning the Lorenz-96 model -- a simple proxy to describe atmospheric dynamics, yet exhibiting chaotic…
Quantum Machine Learning for Climate Modelling
Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea +1
Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability th…
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Katharina Hafner, Sara Shamekh, Guillaume Bertoli +4
Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributi…
Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model
Helge Heuer, Tom Beucler, Mierk Schwabe +3
Persistent systematic errors in Earth system models (ESMs) arise from difficulties in representing the full diversity of subgrid, multiscale atmospheric convection and turbulence.…
Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher…
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…
Opportunities and challenges of quantum computing for climate modelling
Mierk Schwabe, Lorenzo Pastori, Inés de Vega +6
Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This…