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.

提出了一种利用图神经网络的 NWP 偏差校正方法 WeatherGNN，在两个真实数据集上展示了与其他方法相比的显著优势，平均 RMSE 提高了 40.50%。

WeatherGNN：利用复杂关系进行数值天气预报偏差修正

WeatherGNN: Exploiting Complicated Relationships in Numerical Weather  Prediction Bias Correction

Climate models often require post-processing in order to make accurate
estimates of local climate risk. The most common post-processing applied is
bias-correction and spatial resolution enhancement. However, the statistical
methods typically used for this not only are incapable of capturing
multivariate spatial correlation information but are also reliant on rich
observational data often not available outside of developed countries, limiting
their potential. Here we propose an alternative approach to this challenge
based on a combination of image super resolution (SR) and contrastive learning
generative adversarial networks (GANs). We benchmark performance against NASA's
flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our
model successfully reaches a spatial resolution double that of NASA's product
while also achieving comparable or improved levels of bias correction in both
daily precipitation and temperature. The resulting higher fidelity simulations
of present and forward-looking climate can enable more local, accurate models
of hazards like flooding, drought, and heatwaves.

在对气候模型进行本地气候风险准确估计时，我们提出了一种基于图像超分辨率和对比学习生成对抗网络的方法，该方法可以在实现偏差校正的同时将空间分辨率提高一倍，并且可用于更精确的洪水、干旱和热浪等灾害的本地准确建模。