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Found 3 papers, 3 papers with code
Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
We benchmark it against the GAN-based Deep Generative Models of Rainfall (DGMR) and a statistical model, PySTEPS.
Physics-constrained deep learning postprocessing of temperature and humidity
Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error.
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network
The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications.
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