  Numerical weather prediction (NWP) may be inaccurate or biased due to
incomplete atmospheric physical processes, insufficient spatial-temporal
resolution, and inherent uncertainty of weather. Previous studies have
attempted to correct biases by using handcrafted features and domain knowledge,
or by applying general machine learning models naively. They do not fully
explore the complicated meteorologic interactions and spatial dependencies in
the atmosphere dynamically, which limits their applicability in NWP
bias-correction. Specifically, weather factors interact with each other in
complex ways, and these interactions can vary regionally. In addition, the
interactions between weather factors are further complicated by the spatial
dependencies between regions, which are influenced by varied terrain and
atmospheric motions. To address these issues, we propose WeatherGNN, an NWP
bias-correction method that utilizes Graph Neural Networks (GNN) to learn
meteorologic and geographic relationships in a unified framework. Our approach
includes a factor-wise GNN that captures meteorological interactions within
each grid (a specific location) adaptively, and a fast hierarchical GNN that
captures spatial dependencies between grids dynamically. Notably, the fast
hierarchical GNN achieves linear complexity with respect to the number of
grids, enhancing model efficiency and scalability. Our experimental results on
two real-world datasets demonstrate the superiority of WeatherGNN in comparison
with other SOTA methods, with an average improvement of 40.50\% on RMSE
compared to the original NWP.

Machine Learning