标题： FastNet机器学习天气预测模型的技术概述和架构，版本1.0 显示英文标题

标题： Technical overview and architecture of the FastNet Machine Learning weather prediction model, version 1.0

摘要： 我们介绍了FastNet版本1.0，这是一个基于图神经网络架构的数据驱动中程数值天气预报（NWP）模型，由艾伦·图灵研究所和气象局联合开发。 FastNet使用编码-处理-解码结构，生成长达10天的确定性全球天气预测。 该架构与空间分辨率无关，我们已在1$^{\circ}$和0.25$^{\circ}$分辨率下训练了模型，时间步长为六小时。 FastNet在处理器中使用多级网格，能够捕捉大气空间结构中的短程和长程模式。 该模型首先在ECMWF的ERA5再分析数据上进行预训练，然后在额外的自回归滚动步骤上进行微调，这提高了长时间范围内的准确性。 我们使用2022年作为保留年份，在1.5$^{\circ}$分辨率下评估模型性能，并与气象局全球模型进行比较，发现FastNet在多个大气变量上使用多种评估指标超越了当前气象局全球模型NWP系统的能力。 我们的结果表明，我们的1$^{\circ}$和0.25$^{\circ}$FastNet模型均优于当前的全球模型，并且其预测技能接近于在0.25$^{\circ}$ERA5数据上训练的其他数据驱动模型的结果。 显示英文摘要

摘要： We present FastNet version 1.0, a data-driven medium range numerical weather prediction (NWP) model based on a Graph Neural Network architecture, developed jointly between the Alan Turing Institute and the Met Office. FastNet uses an encode-process-decode structure to produce deterministic global weather predictions out to 10 days. The architecture is independent of spatial resolution and we have trained models at 1$^{\circ}$ and 0.25$^{\circ}$ resolution, with a six hour time step. FastNet uses a multi-level mesh in the processor, which is able to capture both short-range and long-range patterns in the spatial structure of the atmosphere. The model is pre-trained on ECMWF's ERA5 reanalysis data and then fine-tuned on additional autoregressive rollout steps, which improves accuracy over longer time horizons. We evaluate the model performance at 1.5$^{\circ}$ resolution using 2022 as a hold-out year and compare with the Met Office Global Model, finding that FastNet surpasses the skill of the current Met Office Global Model NWP system using a variety of evaluation metrics on a number of atmospheric variables. Our results show that both our 1$^{\circ}$ and 0.25$^{\circ}$ FastNet models outperform the current Global Model and produce results with predictive skill approaching those of other data-driven models trained on 0.25$^{\circ}$ ERA5.