通过公民科学雨量计和深度学习来改善多普勒雷达降雨预测
Marshall Rosenhoover1, John Rushing1, John Beck1
1Information Technology and Systems Center, University of Alabama in Huntsville, Huntsville, AL 35899, USA.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
公民科学雨量表通过使用深度学习来改善多普勒雷达降雨估计. 这个框架纠正了偏差,提高了实时降水预测的准确性,特别是在复杂的地形上.
科学领域:
- 水文学的水文学
- 气象学 天气学
- 数据科学数据科学数据科学
背景情况:
- 多普勒雷达降雨估计在复杂的地形中具有挑战性,原因是垂直光束采样,大气影响和雷达质量的偏差.
- 准确的实时降水数据对于操作预测和水文应用至关重要.
研究的目的:
- 开发一个深度学习框架,以使用公民科学雨量计数据纠正雷达衍生地表降水率偏差.
- 提高实时降雨估计的准确性,特别是在具有挑战性的地理区域.
主要方法:
- 公民科学雨量计数据使用两级时间和空间一致性过器进行了验证.
- 创建了零碎线性降雨积累功能,以调整仪表和雷达数据,生成高质量的瞬时降雨率标签.
- 经过调整的ResNet-101深度学习模型被训练,从雷达降水率序列中对降雨强度进行分类.
主要成果:
- 与NOAA的操作雷达产品相比,深度学习模型显著提高了降水分类准确性,显示了精度,回忆和F1分数的提高.
- 该框架在纠正基于实时雷达的降雨估计偏差方面表现出有效性.
- 对未见过的地区的概括更具挑战性,特别是在高强度降雨的情况下,尽管在低强度降雨方面有所改善.
结论:
- 结合公民科学观测和物理知情深度学习,可以显著提高实时雷达降雨估计.
- 开发的框架为改善复杂地形的运营天气预报提供了一个有希望的方法.
- 这项研究强调了在气象研究中整合各种数据源和先进的计算方法的价值.
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