A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
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Computer Science > Machine Learning
arXiv:2606.19026 (cs)
[Submitted on 17 Jun 2026]
Title:A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
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Abstract: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.
| Comments: | This manuscript is a preprint and has been submitted for peer review to the Artificial Intelligence for the Earth Systems journal. The content is subject to change based on the outcome of the peer review process and should not be considered final or definitive. Copyright in this Work may be transferred without further notice |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2606.19026 [cs.LG] |
| (or arXiv:2606.19026v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19026
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: David Aaron Evans [view email][v1] Wed, 17 Jun 2026 12:51:46 UTC (8,165 KB)
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