StationPDE：面向站点地表偏微分方程学习的多站点多变量天气预报

中文摘要 AI 辅助

多站点多变量天气预报旨在根据历史地表观测数据预测多个气象站点的未来天气变量。现有的站点预报模型学习离散站点之间的统计依赖性，但缺乏明确的物理演化过程。与此同时，基于偏微分方程（PDE）的天气模型提供了可解释的物理动力学，但需要连续场和地表站点数据中不可用的高空变量。为弥合这一差距，我们提出了StationPDE，一种面向站点的地表PDE学习模型。StationPDE从离散站点观测构建地形感知的连续地表场，并将其物理演化分解为地表风输运和高空推断。地表风输运显式演化可观测的天气变量，而高空推断使用可学习的水平扩散来近似缺失的高空变量的影响。一个并行的数据驱动扩散分支捕获互补的运动模式，自适应路由器整合两个预测以进行站点级多变量预测。在Weather2K和MeteoNet上的实验表明，StationPDE持续优于最先进的基线，与最强基线相比，平均均方误差（MSE）降低约9.6%。代码和实现细节可在该HTTPS URL获取。

英文摘要

Multi-station multivariate weather forecasting aims to forecast future weather variables at multiple weather stations from historical surface observations. Existing station forecasting models learn statistical dependencies among discrete stations, but lack explicit physical evolution. Meanwhile, PDE-based weather models provide interpretable physical dynamics, yet require continuous fields and upper-air variables unavailable in surface station data. To bridge this gap, we propose StationPDE, a station-oriented surface PDE learning model. StationPDE constructs a terrain-aware continuous surface field from discrete station observations and decomposes its physical evolution into surface wind transport and upper-air inference. Surface wind transport explicitly evolves observable weather variables, while upper-air inference uses learnable horizontal diffusion to approximate the missing influence of unavailable upper-air variables. A parallel data-driven diffusion branch captures complementary motion patterns, and an adaptive router integrates the two forecasts for station-level multivariate forecasting. Experiments on Weather2K and MeteoNet show that StationPDE consistently outperforms state-of-the-art baselines, reducing MSE by about $9.6\%$ on average compared with the strongest baseline.

发表机构

• Hunan University（湖南大学）

机构由 AI 辅助整理，请以论文原文为准。