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Life sciences · Preprint
arXiv · August 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
VeinCast is a novel physics-informed graph neural network architecture for global weather forecasting that combines predefined atmospheric relations with learned state-dependent edges and graph-conditioned latent fusion. The model was evaluated on the ERA5 1.5° reanalysis benchmark across 69 meteorological fields out to 14-day lead times and reported competitive performance relative to five named competing models, with ablations supporting the contribution of its two main components.
Algorithmic development with single-benchmark evaluation. Intervention: VeinCast: physics-guided dynamic field graph with graph-conditioned latent fusion, jointly forecasting 69 meteorological fields. Compared with: FuXi, Pangu-Weather, GraphCast, FengWu, and ARROW on 1.5° ERA5 benchmark.
VeinCast forecasts 69 surface and upper-air fields jointly at lead times up to 14 days Performance reported as competitive with FuXi, Pangu-Weather, GraphCast, FengWu, and ARROW on the 1.5° ERA5 benchmark Ablation studies confirm complementary gains from Physics-Guided Dynamic Field Graph and Graph-Conditioned Latent Fusion modules
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A novel machine-learning architecture for weather forecasting tested on a single benchmark dataset with no clinical or operational validation, peer review status unknown, and no comparison of forecast accuracy against operational meteorological standards or human forecasters.
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Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields. Existing data-driven models largely learn these interactions implicitly, whereas equation-level physical constraints may inherit approximation and model-form biases. We present VeinCast, a physics-guided dynamic field graph and graph-conditioned fusion framework that jointly forecasts 69 surface and upper-air fields. Within each local window, its Physics-Guided Dynamic Field Graph combines predefined atmospheric relations with state-dependent Top-K residual edges and adapts Earth-window attention using the resulting graph context. Graph-Conditioned Latent Fusion further employs graph context and source-node centrality to guide field-to-latent aggregation, while bounded feedback preserves field-specific information. On the $1.5^\circ$ ERA5 benchmark, VeinCast demonstrates competitive forecasting performance across all 69 meteorological fields at lead times of up to 14 days, compared with representative global weather forecasting models including FuXi, Pangu-Weather, GraphCast, FengWu, and ARROW. Ablations confirm that the two modules provide complementary gains, demonstrating the effectiveness of relational-level physical guidance for data-driven weather forecasting.
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