{"ID":2841460,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10894","arxiv_id":"2511.10894","title":"DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting","abstract":"This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightweight probabilistic head is attached to a pre-trained satellite vision encoder (DINOv3-SAT493M) to map encoder tokens into a discrete empirical CDF (eCDF) over 4-hour accumulated rainfall. The projector-head is optimized end-to-end over the Ranked Probability Score (RPS). As an alternative, 3D-UNET baselines trained with an aggregate Rank Probability Score and a per-pixel Gamma-Hurdle objective are used. On the Weather4Cast 2025 benchmark, the proposed method achieved a promising performance, with a CRPS of 3.5102, which represents $\\approx$ 26% in effectiveness gain against the best 3D-UNET.","short_abstract":"This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightweight probabilistic head is attached to a pre-trained satellite vision encoder (DINOv3-SAT493M) to map encoder tokens into a discrete empiric...","url_abs":"https://arxiv.org/abs/2511.10894","url_pdf":"https://arxiv.org/pdf/2511.10894v2","authors":"[\"Luciano Araujo Dourado Filho\",\"Almir Moreira da Silva Neto\",\"Anthony Miyaguchi\",\"Rodrigo Pereira David\",\"Rodrigo Tripodi Calumby\",\"Lukáš Picek\"]","published":"2025-11-14T02:14:08Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}