发表机构
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute; Universidad Carlos III de Madrid (UC3M)（弗劳恩霍夫电信研究所，海因里希·赫兹研究所; 马德里卡洛斯三世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于Vision Transformer的残差学习模型，在物理单位下预测欧洲S2S土壤湿度，超越基线并可靠检测干旱，但闪电干旱触发预测仍具挑战。
AI 中文摘要
尽管短中期天气预报取得了显著进展，预测闪电干旱等高影响事件仍是早期预警业务和基于物理的亚季节到季节（S2S）预测系统面临的关键挑战。我们证明，在欧洲S2S土壤湿度预报中，预测技巧既取决于预测问题的表述方式，也取决于预测模型本身。采用基于Vision Transformer的架构，具有双路径时间和空间注意力，我们表明残差学习对于超越持续性预测至关重要。这一优势仅在以物理单位而非标准化异常预测根区土壤湿度时实现，揭示目标表示本身限制了可预测性。通过分位数头微调的概率扩展进一步提供了校准良好的预测分布。与2021-2022年期间的深度学习和业务ECMWF S2S基线相比，我们的模型在所有提前期实现了最高的确定性和概率性技巧，并可靠地检测异常干燥的根区状态（低于第20百分位）。然而，闪电干旱的触发，定义为多旬强化标准，仍然是所有当前S2S系统共有的根本挑战。这些发现推进了数据驱动的S2S土壤湿度预报，同时强调了预测快速干旱发展的剩余挑战。
英文摘要
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Here we demonstrate that, for S2S soil-moisture forecasting over Europe, forecast skill depends as much on how the prediction problem is formulated as on the forecasting model itself. Using a Vision Transformer-based architecture with dual-pathway temporal and spatial attention, we show that residual learning is essential to outperform persistence. This advantage is realized only when forecasting root-zone soil moisture in physical units rather than standardized anomalies, revealing that the target representation itself constrains predictability. A probabilistic extension via quantile-head fine-tuning further provides well-calibrated predictive distributions. Benchmarked against deep-learning and operational ECMWF S2S baselines over 2021-2022, our model achieves the highest deterministic and probabilistic skill at all lead times and reliably detects anomalously dry root-zone states (below the 20th percentile). Yet flash drought onset, defined by multi-pentad intensification criteria, remains a fundamental challenge shared across all current S2S systems. These findings advance data-driven S2S soil-moisture forecasting while highlighting the remaining challenge of predicting rapid drought development.
Comments27 pages, 8 figures, 5 tables. Accepted for publication in npj Hydrosphere. Supplementary information available with the published version
