比较统计与机器学习方法对气象预报进行空间插值的性能
Statistical versus machine learning-based spatial interpolation of post-processed ensemble weather forecasts
- 用统计和机器学习模型处理无观测点的气象预报插值
- 多模型在不同条件下表现各异，无统一最优方法
- 新提出的海拔感知线性组合法在无观测点提升显著
统计后处理可提升集合气象预报精度，但在无观测站点处生成校准预测仍具挑战。本研究对比了基于EMOS、分布回归网络、Transformer和图神经网络的统计与机器学习方法，在德国有观测与无观测站点上对ECMWF 2米气温和10米风速预报进行后处理。考虑了有限与扩展预测因子设置，并针对温度引入线性组合方法，提出海拔感知线性池（ALP）。结果表明，后处理在多数情况下优于原始集合预报，但无单一方法在所有变量、站点组和评估指标上均最优。所提ALP在无观测站点上相较标准线性池实现小而显著的改进。
原文摘要 · Abstract (English)
Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challenging. This study compares statistical and machine-learning-based methods for post-processing ECMWF 2-m temperature and 10-m wind speed forecasts at observed and unobserved stations in Germany. We consider EMOS-based approaches, distributional regression networks, Transformers, and graph neural networks under both limited and extended predictor settings. For temperature, we also investigate linear forecast combinations and propose an altitude-aware linear pool (ALP). The results show that post-processing improves upon the raw ensemble in most settings, but no single method performs best across all variables, station groups, and evaluation metrics. The proposed ALP provides a small but significant improvement over the standard linear pool at unobserved locations.
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