9 citations · 15 across the 7 of their papers we have counts for
8 papers
NeuralCrop: Combining physics and machine learning for improved crop yield projections
Yunan Lin, Sebastian Bathiany, Maha Badri +6
Global gridded crop models (GGCMs) are crucial to project the impacts of climate change on agricultural productivity and assess associated risks for food security. Despite decades…
Generating time-consistent dynamics with discriminator-guided image diffusion models
Philipp Hess, Maximilian Gelbrecht, Christof Schötz +4
Realistic temporal dynamics are crucial for many video generation, processing and modelling applications, e.g. in computational fluid dynamics, weather prediction, or long-term cli…
Generating realistic global precipitation fields from modelled atmospheric circulation
Michael Aich, Sebastian Bathiany, Philipp Hess +2
Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and…
A Generative Machine Learning Approach for Improving Precipitation from Earth System Models
Philipp Hess, Niklas Boers
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce…
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Michael Aich, Philipp Hess, Baoxiang Pan +3
Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe socioeconomic damage, accurate high-resolution simulation of precip…
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…