arXiv:2605. 14285v2 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and climate science.
arXiv:2608. 05103v1 Announce Type: new Abstract: Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data.
arXiv:2608. 05103v2 Announce Type: replace Abstract: Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data.
The paper introduces STORM, a one‑stage generative AI framework that reformulates Earth system data assimilation as diffusion‑based Bayesian posterior sampling, replacing costly PDE ensemble forecasts with scalable AI inference. STORM employs a spatiotemporal transformer with a global‑attention algorithm that reduces computational complexity from quadratic to linear, enabling high‑resolution, long‑context modeling. The system scales to 74,400 GPUs on Frontier, achieving 96–99 % strong‑scaling efficiency and up to 6 ExaFLOPs sustained BF16 throughput, while supporting 32,768‑member ensembles for uncertainty quantification in just 34 seconds on 4,096 GPUs, and demonstrates improved hurricane tracking and climate reanalysis accuracy.
arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.
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.
arXiv:2606. 07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators.
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.
SimCast‑S2S is a generative latent‑diffusion model designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion pipeline to capture uncertainty, operates in a compact latent space to enable efficient large‑ensemble generation, and leverages transfer learning with low‑rank adaptation to train on limited reanalysis data after pretraining on climate simulations. The model outperforms deep‑learning baselines and competes with, or surpasses, operational systems such as the ECMWF‑S2S baseline without requiring extensive post‑processing.
arXiv:2608.30795v1 Announce Type: cross Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the nume...
arXiv:2602.23188v2 Announce Type: replace Abstract: We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while...
The paper introduces Pangu‑Bayes, a probabilistic forecasting hierarchy that separates atmospheric‑state uncertainty from learned‑model uncertainty as distinct stochastic variables, allowing cross‑flow perturbations of the evolving state with Bayesian parameter samples. In tests on 90 held‑out 2023 tropical cyclones, Pangu‑Bayes reduces track, pressure, and wind errors by 54.2%, 17.2%, and 24.9% respectively, and improves rapid‑intensification detection. The study finds that atmospheric‑state variability more consistently improves track prediction, while learned‑model variability more often enhances intensity prediction, demonstrating how model‑defined uncertainty resolution can be linked to target‑dependent value and dynamical interpretation.
arXiv:2605.16929v2 Announce Type: replace Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....