[
    {
        "id": "osp-17355",
        "type": "article-journal",
        "title": "S$^3$N: A Spherical Spiral Scanning Network for Weather Forecasting",
        "author": [
            {
                "family": "Yan",
                "given": "Fan"
            },
            {
                "family": "Hui",
                "given": "Chen"
            },
            {
                "family": "Lin",
                "given": "Weisi"
            },
            {
                "family": "Zhu",
                "given": "Haiqi"
            },
            {
                "family": "Jiang",
                "given": "Feng"
            },
            {
                "family": "Kung",
                "given": "Sun-Yuan"
            },
            {
                "family": "Zhang",
                "given": "Wei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/s-3-n-a-spherical-spiral-scanning-network-for-weather-forecasting",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Machine learning-based weather prediction (MLWP) has achieved strong performance in global weather forecasting. Recent Hierarchical Equal Area isoLatitude Pixelation (HEALPix)-based methods use the HEALPix (HP) grid to avoid area distortion near the poles of conventional latitude-longitude (LL) grids. However, existing HP-based approaches often use pointwise mapping methods and process HP pixels within separate base faces or local windows. Consequently, the mapping may introduce reconstruction errors and cross-face communication depends on handcrafted boundary handling or shifted windows. We propose the Spherical Spiral Scanning Network (S$^3$N) to address both limitations. First, L2Proj provides a bidirectional method for mapping atmospheric fields between the LL and HP grids through an $L^2$ projection of their continuous finite-element representations. Second, the Attention-Guided Quad-Spiral State-Space Scanning (AQSS) block uses cross-latitude attention to guide selective state-space updates along four global pole-to-pole spiral paths. This design enables continuous information propagation across HP base-face boundaries without additional boundary-processing mechanisms. Experiments show that S$^3$N achieves better results at 4-, 7-, and 10-day lead times, and exhibits slower error growth in long-range forecasting."
    }
]