9 citations · 15 across the 7 of their papers we have counts for
3 papers · 1 filter
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning
Philipp Hess, Michael Aich, Baoxiang Pan +1
Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computa…
Deep learning for bias-correcting CMIP6-class Earth system models
Philipp Hess, Stefan Lange, Christof Schötz +1
The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropoge…
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Philipp Hess, Markus Drüke, Stefan Petri +2
Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can impr…