AI 中文总结
该研究提出可业务化运行的全球MLWP模型AICON，采用GNN架构，依托ICON-DREAM数据集训练，在中短期近地面变量预报上性能优于业务化ICON模型。
AI 中文摘要
我们推出AICON，这是一种全球机器学习天气预报（MLWP）模型，采用13公里空间分辨率、3小时时间步长生成预报，在高分辨率非静力的ICON-DREAM数据集上进行训练。自2026年3月2日起，AICON已在德国气象局（Deutscher Wetterdienst）全面投入业务运行。该模型采用具有编码器-处理器-解码器结构的图神经网络（GNN）架构，其中节点更新通过图注意力机制完成。AICON的一项关键特性是其使用源自ICON模型原生网格的二十面体多网格，确保与训练数据保持一致性。ICON的地形追随垂直SLEVE坐标是其与现有模拟器的主要区别之一。AICON的训练策略通过在训练过程中避免自回归多步展开和更长预报时效，优先保证小尺度保真度，这一设计选择的动机是该方法可保留在针对长程预报优化的模型中常被削弱的精细尺度特征。我们描述了用于训练的预报变量和诊断变量、用于加速收敛的迁移学习协议，以及模型在一系列评估指标上的性能。广泛的评估包括针对观测数据的常规验证、热带气旋案例研究和频谱分析，揭示了其在不同尺度大气变异性表征方面的优势与局限性。针对观测数据的常规验证表明，AICON相较于业务化ICON模型具有相当的预报技巧，尤其在中短期预报范围内的近地面变量上表现突出。
英文摘要
We introduce AICON, a global machine learning weather prediction (MLWP) model which generates forecasts at 13 km spatial resolution with a 3-hour time step, trained on the high-resolution, non-hydrostatic ICON-DREAM dataset. AICON is in full operational use at Deutscher Wetterdienst since 2nd of March 2026. The model employs a graph neural network (GNN) architecture with an encoder-processor-decoder structure, where node updates are performed using a graph attention mechanism. A key feature of AICON is its use of an icosahedral multi-mesh derived from the native grid of the ICON model, ensuring consistency with the training data. ICON's terrain-following vertical SLEVE coordinate is one of the major distinctions from existing emulators. AICON's training strategy prioritizes small-scale fidelity by avoiding autoregressive multi-step rollout and longer forecast horizons during training, a design choice motivated by the hypothesis that this approach preserves fine-scale features often damped in models optimized for longer-range forecasts. We describe the prognostic and diagnostic variables used for training, the transfer learning protocol employed to accelerate convergence, and the model's performance across a range of evaluation metrics. An extensive evaluation, including routine verification against observation, a tropical cyclone case and spectral analysis reveal the strengths and limitations in the representation of atmospheric variability across scales. Routine verification against observations demonstrates competitive skill relative to the operational ICON model, particularly for near-surface variables in the short to medium forecast range.
Comments41 pages, 20 figures
