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Due to insufficient local area information, numerical weather prediction(NWP) may yield biases for specific areas. Previous studies correct biasesmainly by employing handcrafted features or applying data-driven methodsintuitively, overlooking the complicated dependencies between weather factorsand between areas. To address this issue, we propose WeatherGNN, a local NWPbias-correction method that utilizes Graph Neural Networks (GNNs) to exploitmeteorological dependencies and spatial dependencies under the guidance ofdomain knowledge. Specifically, we introduce a factor GNN to capturearea-specific meteorological dependencies adaptively based on spatialheterogeneity and a fast hierarchical GNN to capture dynamic spatialdependencies efficiently guided by Tobler's first and second laws of geography.Our experimental results on two real-world datasets demonstrate that WeatherGNNachieves the state-of-the-art performance, outperforming the best baseline withan average of 4.75 \% on RMSE.

WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction