标题： FengWu-GHR：学习千米尺度中程全球天气预报 显示英文标题

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

摘要： 千米尺度的全球大气动力学建模使得天气预报更加精细，并降低了灾难性天气和气候活动的风险。 因此，构建千米尺度的全球预报模型是气象领域持续追求的目标。 过去几十年，国际上一直在努力提高数值天气模型的空间分辨率。 然而，由于计算资源的大量消耗，开发更高分辨率的数值模型仍然是一个长期存在的挑战。 数据驱动的全球天气预报模型最近取得了进展，它们利用再分析数据进行模型训练，并显示出与数值模型相当甚至更高的预报能力。 然而，它们都受到再分析数据分辨率的限制，无法生成更高分辨率的预报。 本工作提出了 FengWu-GHR，第一个在0.09$^{\circ}$水平分辨率下运行的数据驱动全球天气预报模型。 FengWu-GHR引入了一种新方法，通过从预训练的低分辨率模型中继承先验知识，为运行基于机器学习的高分辨率预报打开了大门。 2022年天气预测的回测表明，FengWu-GHR优于IFS-HRES。 此外，对站点观测的评估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业务预报能力。 显示英文摘要

摘要： 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.