在没有气象参数的情况下建立和评估大气水蒸气逆转模型,基于机器学习
Ning Liu1, Yu Shen1, Shuangcheng Zhang1
1College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
一个新的深度学习模型准确地估计了沉式水蒸气 (PWV),而不需要加权平均温度 (Tm). 这种先进的模型提高了准确性,并且与台风等极端天气事件有很强的相关性.
科学领域:
- 大气科学 大气科学
- 地质物理学 地质物理学
- 遥感 遥感 遥感 遥感
背景情况:
- 降水蒸气 (PWV) 对于了解大气中的水含量至关重要.
- 基于地面的GNSS提供高分辨率的PWV,但受到Tm参数准确度的限制.
- 现有的模型通常依赖于加权平均温度 (Tm),影响PWV反转精度.
研究的目的:
- 为PWV反转开发一种新的深度学习模型,消除了对Tm参数的需求.
- 使用GNSS数据提高PWV估计的准确性和可靠性.
- 调查模型在捕捉极端天气事件中的表现.
主要方法:
- 利用了来自香港的17个地面GNSS站和再分析产品的数据.
- 开发了一个用于PWV检索的深度学习模型,不包括Tm参数.
- 对传统方法和无线电探测器数据验证了模型.
- 分析了台风引起的暴雨事件期间的PWV变化.
主要成果:
- 这种新模型比传统的依赖于Tm的模型更准确,BIAS,MAE和RMSE的平均改善率为38%.
- 无线探测器验证证实了该模型的高精度,显示BIAS仅为0.8毫米.
- 该模型的准确性与LSTM相似,但具有更大的普遍性.
- 新型号检索的PWV在台风期间急剧增加,随后下降,与极端降雨有很强的相关性.
结论:
- 开发的深度学习模型为PWV反转提供了更准确,更可靠的方法.
- 该模型有效地捕捉了极端天气事件期间大气水蒸气的动态变化.
- 这种方法为气象监测提供了有价值的工具,并增强了天气预报能力.
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