FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
  Forecasting
Tao Han Song Guo Fenghua Ling Kang Chen Junchao Gong Jingjia Luo Junxia Gu Kan Dai Wanli Ouyang Lei Bai
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.