沙漏：一种用于全球和区域天气预报的概率数据驱动时间降尺度方法
HourGlass: A probabilistic data-driven temporal downscaler for global and regional weather forecasting
浏览论文内容
中文总结 AI 辅助
研究针对天气预报高频需求与现有系统6小时分辨率的差距，提出概率数据驱动的时间降尺度方法HourGlass，通过特定训练保留小尺度变异性并鼓励时间一致性，能生成现实概率预测，弥合了6小时预测与每小时产品的差距。
中文摘要 AI 辅助
许多预测应用需要高频时间分辨率，但大多数先进的数据驱动天气预报系统以6小时分辨率运行。直接每小时预测存在误差累积和时间不一致问题。我们引入了沙漏（HourGlass），一种概率数据驱动的时间降尺度方法，用于重建预测状态之间的演变。它使用连续排序概率得分（CRPS）的变体进行训练，保留小尺度空间变异性并鼓励时间一致性。与现有确定性时间降尺度方法不同，HourGlass生成现实的概率预测。在预测轨迹上训练避免了先前方法使用的数据集中的时间不一致性。我们在两种设置下评估HourGlass：全球应用于欧洲中期天气预报中心（ECMWF）的AIFS - 单模型和AIFS - 集合预报系统的AIFS - HourGlass，以及区域应用于挪威气象研究所（MET Norway）的高分辨率拉伸网格集合模型Bris的Bris - HourGlass。验证结果表明，两个模型在产生具有现实小尺度变异性的时间连贯每小时预测时，都保留了其基础预报系统的技能。案例研究表明在快速发展的天气事件中具有物理上一致的演变。每小时降水仍然具有挑战性：HourGlass提高了降水场的空间真实性，但仍低估了最强烈的极端情况，这是数据驱动天气预报模型的常见限制。这些结果表明，HourGlass有效地弥合了6小时数据驱动预测与区域和全球业务预报所需的每小时产品之间的差距。
英文摘要
Many forecast applications require high frequency temporal resolution, yet most state-of-the-art data-driven weather forecasting systems operate at 6-hourly resolution. Although direct hourly forecasting is possible, it suffers from error accumulation and temporal inconsistency. We introduce HourGlass, a probabilistic data-driven temporal downscaling method that reconstructs the evolution between forecast states. HourGlass is trained using variants of the continuous ranked probability score (CRPS) preserving small-scale spatial variability while encouraging temporal consistency. Unlike existing deterministic temporal downscaling approaches, which tend to produce overly smooth fields, HourGlass generates realistic probabilistic forecasts. Training on forecast trajectories rather than reanalysis or analysis data also avoids the temporal inconsistencies present in datasets used by previous methods. We evaluate HourGlass in two settings: AIFS-HourGlass, applied globally to ECMWF's AIFS-Single and AIFS-ENS forecast systems, and Bris-HourGlass, applied regionally to MET Norway's high-resolution stretched-grid ensemble model, Bris. Verification against observations shows that both models retain the skill of their underlying forecasting systems while producing temporally coherent hourly forecasts with realistic small-scale variability. Case studies demonstrate physically consistent evolution during rapidly developing weather events, including extratropical cyclones and organised convection. Hourly precipitation remains challenging: HourGlass improves the spatial realism of precipitation fields but still underestimates the most intense extremes, a common limitation of data-driven weather forecasting models. These results demonstrate that HourGlass effectively bridges the gap between 6-hourly data-driven forecasts and the hourly products required for operational regional and global forecasting.
