The Compatibility between the Pangu Weather Forecasting Model and
  Meteorological Operational Data
Wencong Cheng Yan Yan Jiangjiang Xia Qi Liu Chang Qu Zhigang Wang
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.