Recently, multiple data-driven models based on machine learning for weather
forecasting have emerged. These models are highly competitive in terms of
accuracy compared to traditional numerical weather prediction (NWP) systems. In
particular, the Pangu-Weather model, which is open source for non-commercial
use, has been validated for its forecasting performance by the European Centre
for Medium-Range Weather Forecasts (ECMWF) and has recently been published in
the journal "Nature". In this paper, we evaluate the compatibility of the
Pangu-Weather model with several commonly used NWP operational analyses through
case studies. The results indicate that the Pangu-Weather model is compatible
with different operational analyses from various NWP systems as the model
initial conditions, and it exhibits a relatively stable forecasting capability.
Furthermore, we have verified that improving the quality of global or local
initial conditions significantly contributes to enhancing the forecasting
performance of the Pangu-Weather model.

最近，基于机器学习的多个数据驱动模型在天气预报方面取得了重要突破，与传统的数值天气预报系统相比，这些模型在准确性方面更具竞争力。该论文评估了开源且非商业使用的 Pangu-Weather 模型与多种常用数值天气预报操作分析的兼容性，并通过实例研究表明，Pangu-Weather 模型作为模型初始条件与不同数值天气预报系统的操作分析兼容且具有相对稳定的预测能力。此外，研究还验证了改善全球或本地初始条件的质量对提高 Pangu-Weather 模型的预测性能起到了显著贡献。