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arXiv · 2511.10894

DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting

Luciano Araujo Dourado Filho·Almir Moreira da Silva Neto·Anthony Miyaguchi·Rodrigo Pereira David·Rodrigo Tripodi Calumby·Lukáš Picek
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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.

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Luciano Araujo Dourado Filho, Almir Moreira da Silva Neto, Anthony Miyaguchi, Rodrigo Pereira David, Rodrigo Tripodi Calumby, Lukáš Picek. 2025-11-14. DINOv3 as a Frozen Encoder for CRPS-Oriented Probabilistic Rainfall Nowcasting. https://arxiv.org/abs/2511.10894

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