Kilometer-scale modeling of global atmosphere dynamics enables fine-grained
weather forecasting and decreases the risk of disastrous weather and climate
activity. Therefore, building a kilometer-scale global forecast model is a
persistent pursuit in the meteorology domain. Active international efforts have
been made in past decades to improve the spatial resolution of numerical
weather models. Nonetheless, developing the higher resolution numerical model
remains a long-standing challenge due to the substantial consumption of
computational resources. Recent advances in data-driven global weather
forecasting models utilize reanalysis data for model training and have
demonstrated comparable or even higher forecasting skills than numerical
models. However, they are all limited by the resolution of reanalysis data and
incapable of generating higher-resolution forecasts. This work presents
FengWu-GHR, the first data-driven global weather forecasting model running at
the 0.09$^{\circ}$ horizontal resolution. FengWu-GHR introduces a novel
approach that opens the door for operating ML-based high-resolution forecasts
by inheriting prior knowledge from a pretrained low-resolution model. The
hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to
the IFS-HRES. Furthermore, evaluations on station observations and case studies
of extreme events support the competitive operational forecasting skill of
FengWu-GHR at the high resolution.

FengWu-GHR 是第一个以 0.09° 水平分辨率运行的基于数据驱动全球天气预报模型，该模型通过继承来自深度学习预训练低分辨率模型的先前知识实现了高分辨率的天气预测，2022 年的天气预测回测结果表明 FengWu-GHR 优于 IFS-HRES，并且对站点观测数据和极端事件的案例研究支持了 FengWu-GHR 在高分辨率下的竞争性操作预报技能。

凤舞 - GHR：学习千米尺度中程全球天气预报

FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather  Forecasting

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。