DownScaleBench用于开发和应用基于深度学习的城市气候缩放 - - 首次结果为高分辨率的城市降水气候在德克萨斯州奥斯的城市降水气候
Manmeet Singh1,2,3, Nachiketa Acharya4, Sajad Jamshidi5
1Jackson School of Geosciences, The University of Texas at Austin, Austin, 78712 TX USA.
Computational urban science
|June 5, 2023
概括
城市缩小规模为城市的弹性提供了关键的气候数据. 一个新的深度学习工具,DownScaleBench,生成高分辨率的降水数据,改善气候适应规划.
科学领域:
- 气候科学 气候科学
- 城市气象城市气象学
- 人工智能的人工智能
背景情况:
- 城市需要精细的气候数据来实现弹性基础设施和适应战略.
- 现有的气候产品缺乏必要的分辨率 (1-10公里城市规模,0.1-1公里社区规模).
- 传统的缩放方法是计算密集和复杂的.
研究的目的:
- 为城市气候数据缩小规模开发一种新,计算效率高的方法.
- 创建一个通用深度学习工具,DownScaleBench,用于城市缩小规模.
- 为城市气候分析生成高分辨率的降水数据集.
主要方法:
- 开发了一个深度学习运算符,DownScaleBench,使用一个代超分辨率卷积神经网络 (Iterative SRCNN).
- 对现场和卫星/再分析网格数据的工具进行了通用化.
- 应用了DownScaleBench来缩小10公里的卫星降水产品 (JAXA GsMAP) 到300米分辨率.
主要成果:
- 为城市地区生成了高分辨率 (300米) 格式的降水产品.
- 验证了缩小规模的数据与位于德克萨斯州奥斯的暴雨事件的现场观测.
- 与较粗的数据集和立方位互插基线相比,显示出明显的改善.
结论:
- DownScaleBench提供了一种强大而高效的方法,用于生成高分辨率的城市气象数据集.
- 该工具通过提供必要的细度气候信息,有助于规划适应气候的城市.
- 这种方法对各种城市气候应用和决策过程有影响.
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