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混合物理-AI优于极端降雨的数值天气预报,现在预测
Puja Das1, August Posch1,2, Nathan Barber3
1Sustainability and Data Sciences Laboratory, Northeastern University, Boston, MA USA.
概括
像NowcastNet这样的深度生成模型显示了降水预测的前景,超过了传统的数值天气预测 (NWP) 模型的强降雨事件. 这一进步可以显著改善洪水和河流管理战略.
科学领域:
- 水文和气象学
- 人工智能和机器学习
背景情况:
- 降水现状预测对于洪水和河流管理至关重要,但仍然是一个重大挑战.
- 现有的数值天气预报 (NWP) 模型难以准确检测,尤其是极端事件.
- 深度生成建模 (DGM) 和物理嵌入式机器学习 (ML) 的最新进展提供了潜在的改进.
研究的目的:
- 为了评估一个最先进的物理嵌入式DGM,NowcastNet的性能,与NWP模型进行降水现在预测.
- 与水资源和河流管理者进行共同评估,以评估实际适用性.
- 在水文预测中识别先进ML模型的优点和局限性.
主要方法:
- 将NowcastNet与高分辨率快速更新 (HRRR) NWP模型进行比较. 转向和持久性.
- 使用诸如严重降水事件 (极端超过16毫米/小时) 关键成功指数 (CSI) 等指标进行评估.
- 联合评估涉及ML开发人员,水文学家和河流管理人员.
主要成果:
- 现在播放网络显著优于HRRR,偏向和持久性,达到0.30的中位数CSI,而HRRR的0.04为极端降水.
- 该模型在点对点预测中显示了水文相关的改进.
- 洞穴包括在较长的预期时间中高估空间总量降水的倾向.
结论:
- 像NowcastNet这样的物理嵌入式DGM代表了与当前NWP模型相比,在降水现在预测方面取得的重大进步,特别是在极端事件方面.
- 这些发现表明了加强洪水应急响应和水电管理的潜力.
- 需要进一步的研究和验证,以解决总体降水预测的局限性.
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