SimCast‑S2S is a generative latent‑diffusion framework designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion‑based generative pipeline for uncertainty quantification, operates in a compact latent space learned by VAEs for efficient large‑ensemble generation, and employs transfer learning with LoRA to overcome limited training data. On reanalysis data, it outperforms deep‑learning baselines and competes with or surpasses state‑of‑the‑art operational systems such as ECMWF‑S2S.
By Hiep V. Dang, Antonios Mamalakis
arXiv:2606. 14570v1 Announce Type: cross Abstract: Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function.
By Mikel N. Legasa, Antoine Doury, Achille Gellens, Redouane Lguensat, Clara Naldesi, Soulivanh Thao, Mathieu Vrac
The paper presents a probabilistic post‑processing framework that uses a conditional Variational Autoencoder (cVAE) with a non‑parametric decoder to adjust seasonal Arctic sea‑ice forecasts. By replacing the Gaussian decoder with a generator and optimizing with Continuous Ranked Probability Score (CRPS), the method produces larger, bias‑corrected ensembles that are better calibrated, more consistent with observations, and exhibit higher resolution and sharper spectral power than standard cVAEs. The approach demonstrates improved forecast skill and reduced errors compared to benchmark predictions.
By Parsa Gooya, Reinel Sospedra-Alfonso
arXiv:2512.22814v2 Announce Type: replace
Abstract: Accurate long-range weather forecasting remains a major challenge for AI models, both because errors accumulate over autoregressive rollouts and be...
By Scott A. Martin, Noah Brenowitz, Dale Durran, Michael Pritchard
This study introduces the first controlled benchmark of generative models for weather data assimilation using real station observations from 11,849 NOAA MADIS stations across the U.S. It evaluates key design choices—diffusion vs. flow matching, pixel vs. latent-space formulations, and inference-time conditioning strategies—against a classical 3D-Var baseline. The benchmark finds that learned generative priors and full-gradient guidance improve RMSE over ERA5, while other design variations offer minimal benefit, especially under sparse observation conditions.
By Ruizhe Huang, Qidong Yang, Jonathan Giezendanner, Sherrie Wang
arXiv:2601.21151v3 Announce Type: replace
Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...
By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard