DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911275851710464 |
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| author | Filho, Luciano Araujo Dourado Neto, Almir Moreira da Silva Miyaguchi, Anthony David, Rodrigo Pereira Calumby, Rodrigo Tripodi Picek, Lukáš |
| author_facet | Filho, Luciano Araujo Dourado Neto, Almir Moreira da Silva Miyaguchi, Anthony David, Rodrigo Pereira Calumby, Rodrigo Tripodi Picek, Lukáš |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting Filho, Luciano Araujo Dourado Neto, Almir Moreira da Silva Miyaguchi, Anthony David, Rodrigo Pereira Calumby, Rodrigo Tripodi Picek, Lukáš Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.10894 |