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arXiv Machine Learning By Xiao Wang, Changjian Chen, Rongwen Li, Hongwu Liu, Kun Fang, Zhuo Tang

StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting

Read the original on arXiv Machine Learning →

StationPDE is a station-oriented surface PDE learning model designed for multi-station multivariate weather forecasting. It builds a terrain-aware continuous surface field from discrete station observations and separates its physical evolution into surface wind transport and upper-air inference, the latter approximating missing upper-air effects via learnable horizontal diffusion. The model also includes a data-driven diffusion branch and an adaptive router to combine forecasts, achieving a 9.6% average MSE reduction over state-of-the-art baselines on Weather2K and MeteoNet datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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