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

TL;DR

This paper introduces a novel probabilistic rainfall nowcasting method using a pre-trained DINOv3 encoder with a lightweight transformer head optimized for the Ranked Probability Score, achieving significant accuracy improvements.

Contribution

It presents a new approach combining a frozen satellite encoder with a specialized probabilistic head, optimized end-to-end for better rainfall prediction accuracy.

Findings

Achieved a CRPS of 3.5102 on Weather4Cast 2025 benchmark.

Gained approximately 26% effectiveness over the best 3D-UNET baseline.

Demonstrated computational efficiency and competitive performance.

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.