Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.

降水临近预报对于洪水应急和河流管理至关重要，几十年来一直具有挑战性，尽管深度生成建模 （DGM） 的最新发展表明了改进的可能性。田纳西河谷管理局等河流管理中心一直在使用数值天气预报 （NWP） 模型进行临近预报，但他们一直在努力解决即使是一流的 NWP 模型也能漏报的问题。虽然几十年来的研究在平流和局部进化之外取得了有限的改进，但最近的尝试表明，所谓的无物理机器学习 （ML） 方法取得了进展，物理嵌入的 ML 方法甚至取得了更大的改进。用于临近预报的 DGM 开发人员将他们的方法与光流（平流的一种变体）和气象学家的判断进行了比较，但没有与 NWP 模型进行比较。此外，他们尚未与水资源和河流管理者进行独立的共同评估。在这里，我们展示了最先进的物理嵌入式深度生成模型，特别是 NowcastNet，优于最新一代 NWP 的高分辨率快速刷新 （HRRR） 模型，以及平流和持久性，特别是对于强降水事件。因此，对于超过 16 mm/h 的网格单元极端值，NowcastNet 的中位临界成功指数 （CSI） 为 0.30，而 HRRR 的中位 CSI 为 0.04。然而，尽管 NowcastNet 的逐点预报在水文方面进行了相关改进，但需要注意的是，在较长的提前期内高估了空间集合降水。我们与 ML 开发人员、水文学家和河流管理者的共同评估表明，改进洪水应急响应和水电管理的可能性。