[
    {
        "id": "osp-17218",
        "type": "article-journal",
        "title": "FlexCast: Adaptive Weather Forecasting from Arbitrary Field Sets",
        "author": [
            {
                "family": "Zhang",
                "given": "Yuang"
            },
            {
                "family": "Hui",
                "given": "Chen"
            },
            {
                "family": "Lin",
                "given": "Weisi"
            },
            {
                "family": "Zhu",
                "given": "Haiqi"
            },
            {
                "family": "Wang",
                "given": "Xiulai"
            },
            {
                "family": "Kung",
                "given": "Sun-Yuan"
            },
            {
                "family": "Jiang",
                "given": "Feng"
            }
        ],
        "URL": "https://omanscience.com/en/articles/flexcast-adaptive-weather-forecasting-from-arbitrary-field-sets",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Most deep learning weather models assign a fixed set of variables and pressure levels to predefined channels, limiting transfer across atmospheric field configurations. This dependence on a fixed field set limits the transferability of trained models across atmospheric field configurations. We propose FlexCast, a field-adaptive weather forecasting model that uses a single set of parameters to produce identity-aligned forecasts for variable-cardinality subsets drawn from a 69-field ERA5 registry. Specifically, a metadata-conditioned adapter the first encodes variable identity, pressure level, and field type and combines them with spatial features. Then, shared rank-16 projec?tions are modulated by metadata-dependent gates to produce field?specific features, while masked set fusion aggregates the available fields into a fixed-width representation. Subsequently, a multiscale U-Transformer processes the fused atmospheric features, while an identity-aware query decoder produces forecasts for the requested fields. Finally, FlexCast learns a standardized six-hour increment and applies it recursively to generate forecasts at longer lead times. Experiments on the 2020 ERA5 test set demonstrate that FlexCast operates across varying field configurations. Compatible cross-field context is associated with lower forecast errors, whereas mismatched context increases them."
    }
]