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''.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2605.24038v3 Announce Type: replace-cross Abstract: Aurora visibility at a given location requires two physically distinct conditions to hold at once: aurora occurring overhead, governed by sol...
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.