Over the past year, data-driven global weather forecasting has emerged as a
new alternative to traditional numerical weather prediction. This innovative
approach yields forecasts of comparable accuracy at a tiny fraction of
computational costs. Regrettably, as far as I know, existing models exclusively
rely on regression losses, producing forecasts with substantial blurring. Such
blurring, although compromises practicality, enjoys an unfair advantage on
evaluation metrics. In this paper, I present Kunyu, a global data-driven
weather forecasting model which delivers accurate predictions across a
comprehensive array of atmospheric variables at 0.35{\deg} resolution. With
both regression and adversarial losses integrated in its training framework,
Kunyu generates forecasts with enhanced clarity and realism. Its performance
outpaces even ECMWF HRES in some aspects such as the estimation of anomaly
extremes, while remaining competitive with ECMWF HRES on evaluation metrics
such as RMSE and ACC. Kunyu is an important step forward in closing the utility
gap between numerical and data-driven weather prediction.

在本论文中，作者介绍了 Kunyu 模型，它是一个全球数据驱动的天气预测模型，通过整合回归和对抗学习，生成更清晰和真实的预测，优于 ECMWF HRES 在一些方面并与其在 RMSE 和 ACC 等评估指标上竞争力保持一致，是在缩小数值和数据驱动天气预测之间的实用性差距方面迈出的重要一步。