[
    {
        "id": "osp-20636",
        "type": "article-journal",
        "title": "NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters",
        "author": [
            {
                "family": "Xu",
                "given": "Haoran"
            },
            {
                "family": "Guo",
                "given": "Xingzhuo"
            },
            {
                "family": "Zhang",
                "given": "Yuchen"
            },
            {
                "family": "Zhong",
                "given": "Jincheng"
            },
            {
                "family": "Wang",
                "given": "Jianmin"
            },
            {
                "family": "Long",
                "given": "Mingsheng"
            }
        ],
        "URL": "https://omanscience.com/en/articles/nowcastdit-diffusion-transformers-are-effective-precipitation-nowcasters",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability. Diffusion models are well suited to modeling complex precipitation distributions, yet existing approaches often introduce increasingly specialized designs, leaving the capability of a standard diffusion architecture underexplored. We show that a standard Diffusion Transformer already provides a simple and scalable foundation for precipitation nowcasting, with domain-specific requirements accommodated naturally within its design space. Based on this principle, we develop NowcastDiT and instantiate this flexibility through two complementary adaptations: a dynamics-aware noise prior for temporally coherent forecasts, and end-to-end reinforcement learning with timestep-aware rewards for meteorological skill. Experiments on SEVIR and MRMS benchmarks show that NowcastDiT achieves state-of-the-art performance in both perceptual quality and meteorological skill. These results suggest that standard DiT can serve as an effective foundation for precipitation nowcasting."
    }
]