用混合模型提升天气预报误差预测，尤其在复杂垂直大气变化时更准。
A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
- 结合地表观测与垂直大气结构，用LSTM与视觉变压器融合建模。
- 降水误差预测技能提升近一倍，对对流驱动的误差捕捉更精准。
- 适合气象预报员评估模型偏差和预报可信度，尤其在边界层活跃期。
高分辨率数值天气预报（NWP）系统中的预报误差常与未解析的行星边界层（PBL）过程、对流、地形诱导环流及其他垂直结构大气现象相关。先前研究显示，长短期记忆（LSTM）网络可利用地面站观测成功预测高分辨率快速刷新（HRRR）模型的预报误差，但性能下降与复杂垂直大气演变时期有关。为此，我们提出一种混合LSTM-视觉变压器（LSTM-ViT）框架，融合地表观测的时间序列学习与纽约州地面站探空仪网络提供的大气垂直结构信息。该框架用于预测单个地面站上HRRR模型的逐小时降水量、10米风速和2米温度预报误差。在所有三个预测变量中，引入探空仪数据显著提升了误差预测能力，尤其在短预报提前期和边界层活动增强期改善最明显。降水误差预测性能提升约两倍，能更好捕捉对流驱动的误差演变，并缓解由边界层过程引起的性能退化。结果表明，将时间序列学习与垂直感知注意力机制结合，为改进业务化NWP系统中的预报误差预测提供了一条物理上有意义的路径。本研究为预报员提供了关于模型偏差和预报置信度的增强指引。
原文摘要 · Abstract (English)
Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memory (LSTM) networks can successfully predict forecast errors in the High-Resolution Rapid Refresh (HRRR) model using mesonet observations, but we believe performance degradation is linked to periods of complex vertical atmospheric evolution. To address this limitation, we develop a hybrid LSTM-Vision Transformer (LSTM-ViT) framework that combines temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network. The LSTM-ViT framework is trained to predict HRRR hourly precipitation, 10 m wind speed, and 2 m temperature forecast errors at individual mesonet stations. Across all three predictors, incorporation of profiler-derived atmospheric structure improves forecast error prediction skill relative to the baseline LSTM architecture, with the largest gains occurring at shorter forecast lead times and during periods of enhanced PBL activity. Improvements are particularly pronounced for precipitation forecast error, where the LSTM-ViT framework achieves approximately a twofold increase in predictive skill relative to the baseline LSTM while better capturing convectively driven error evolution and reducing degradation associated with PBL processes. These results demonstrate that combining temporal sequence learning with vertically informed attention mechanisms provides a physically meaningful pathway for improving forecast error prediction in operational NWP systems. Our research offers forecasters enhanced guidance regarding model bias and forecast confidence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。