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Found 5 papers, 0 papers with code
Conditional diffusion models for downscaling & bias correction of Earth system model precipitation
Traditional statistical bias correction and downscaling methods fall short in improving spatial structure, while recent deep learning methods lack controllability over the output and suffer from unstable training.
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Foundation Models
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 computationally too expensive.
Deep learning for bias-correcting CMIP6-class Earth system models
The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming.
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation.
Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall
The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models.
