Search Results for author:
Found 4 papers, 3 papers with code
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
