用环境数据增强雷达预测，提前12小时更准预报强对流天气。
FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting
- 融合雷达与三维大气预报，环境条件驱动深度学习模型。
- 在华东区4-7月测试中，反射率和降水预测优于现有方法。
- 显式保留强对流信号，提升初始生成与持续性预测能力。
强对流天气常引发局部灾害，往往需在雷达回波完全显现前预警。由于对流初生前信号可能缺失且强回波在预报中迅速衰减，仅依赖雷达的临近预报面临挑战。本文提出FuXi-Nowcast，一种结合高分辨率观测与三维大气预报的环境条件深度学习系统，可提前12小时预测复合反射率、降水、阵风及地表变量。2024年4月至7月在华东地区的评估显示，该模型在反射率与降水预测上超越了业务数值模型、持续性及外推基准。案例分析、诊断与消融实验表明，大气湿度信息及对强对流信号的显式保留，显著提升了对流初生与维持的预测能力。结果证明，环境条件调节能有效缓解仅靠雷达预报在高影响对流天气中的关键失败模式。
原文摘要 · Abstract (English)
Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense convection remain challenging for radar-only nowcasting because pre-convective signals may be absent from recent radar observations and strong echoes often decay rapidly in forecasts. Here we present FuXi-Nowcast, an environment-conditioned deep learning system that combines high-resolution observations with three-dimensional atmospheric forecasts to predict composite reflectivity, precipitation, wind gusts, and surface variables up to 12 h ahead. In April--July 2024 evaluations over East China, FuXi-Nowcast outperforms operational numerical, persistence and extrapolation baselines for reflectivity and precipitation. Case studies, diagnostics, and ablation experiments suggest that atmospheric moisture information and explicit preservation of strong convective signals contribute to forecasts of convective initiation and maintenance. These results show that environmental conditioning can mitigate important failure modes of radar-only nowcasting for high-impact convective weather.
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