{"ID":2878752,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18486","arxiv_id":"2508.18486","title":"Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction","abstract":"Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state-of-the-art NWP competitors. In this work, we propose Huracan, an observation-driven weather forecasting system which combines an ensemble data assimilation model with a forecast model to produce highly accurate forecasts relying only on observations as inputs. Huracan is not only the first to provide ensemble initial conditions and end-to-end ensemble weather forecasts, but also the first end-to-end system to achieve an accuracy comparable with that of ECMWF ENS, the state-of-the-art NWP competitor, despite using a smaller amount of available observation data. Notably, Huracan matches or exceeds the continuous ranked probability score of ECMWF ENS on 75.4% of the variable and lead time combinations. Our work is a major step forward in end-to-end data-driven weather prediction and opens up opportunities for further improving and revolutionizing operational weather forecasting.","short_abstract":"Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial...","url_abs":"https://arxiv.org/abs/2508.18486","url_pdf":"https://arxiv.org/pdf/2508.18486v1","authors":"[\"Zekun Ni\",\"Jonathan Weyn\",\"Hang Zhang\",\"Yanfei Xiang\",\"Jiang Bian\",\"Weixin Jin\",\"Kit Thambiratnam\",\"Qi Zhang\",\"Haiyu Dong\",\"Hongyu Sun\"]","published":"2025-08-25T20:55:46Z","proceeding":"physics.ao-ph","tasks":"[\"physics.ao-ph\",\"cs.LG\"]","methods":"[]","has_code":false}