arXiv:2606. 26361v1 Announce Type: new Abstract: ML foundation models are able to emulate atmospheric dynamics accurately and efficiently but operate as opaque ``black boxes''.
arXiv:2607. 05658v1 Announce Type: cross Abstract: Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals.
Aurora 1. 5 adds 22 more variables, hourly temporal resolution, and probabilistic ensemble forecasting to the Aurora foundation model, making it more useful for real-world weather, climate, and energy applications.
A deep‑learning model that converts dynamical circulation forecasts into precipitation estimates predicts a dry anomaly over central China in summer 2026, with consistent signals from March to May. Retrospective tests show the model performs best in analogue years marked by sustained central equatorial Pacific warming, which promotes a cyclonic circulation that drives northerly winds and moisture divergence, suppressing rainfall. Layer‑wise relevance propagation identifies these northerly winds as the key driver, and perturbation tests confirm that removing them eliminates the dry anomaly, demonstrating a physically interpretable link between AI predictions and climate dynamics.
A deep learning model that converts dynamical circulation forecasts into precipitation estimates predicts a dry anomaly over central China in the summer of 2026, with consistent signals from March to May. Retrospective tests show the model performs best in analogue years marked by sustained central equatorial Pacific warming, which promotes a cyclonic circulation that drives northerly winds and moisture divergence, suppressing rainfall. Layer‑wise relevance propagation identifies these northerly winds as the key driver, and perturbation tests confirm that removing them eliminates the predicted dry anomaly, providing a physically interpretable explanation for the AI forecast.
arXiv:2511. 23043v2 Announce Type: replace-cross Abstract: We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length.
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
arXiv:2607. 20778v1 Announce Type: new Abstract: Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.
arXiv:2510. 16031v2 Announce Type: replace-cross Abstract: Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms.
arXiv:2606. 28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost.
arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.
arXiv:2607. 13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions.