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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.
Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative.
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework
We provide the long-term and fine-grained nighttime light observations to promote research on human activities.
Improving seasonal forecast using probabilistic deep learning
We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system.
