基于滴筒探测器和卫星观测数据的气温数据的评估
Xin Wei1, Qiong Liu1, Yonghang Chen1
1College of Environmental Science and Engineering, Donghua University, Songjiang District, Shanghai 201620, China.
The Science of the total environment
|September 6, 2023
概括
机器学习模型将卫星和现场数据结合起来,用于连续的垂直空气温度 (AT) 地图. 随机森林模型在中国实现了最佳准确性,在估计AT时空分布方面超过了其他算法.
科学领域:
- 大气科学 大气科学
- 数据科学数据科学数据科学
- 遥感 遥感 遥感 遥感
背景情况:
- 连续的垂直空气温度 (AT) 数据对于大气研究至关重要.
- 卫星数据提供空间覆盖,但缺乏垂直分辨率.
- 现场观测提供垂直数据,但仅限于单个点.
研究的目的:
- 开发一种方法,以大规模实现空间连续的垂直AT数据.
- 通过机器学习将现场观测与多卫星数据合并.
- 为了估计纵向AT在中国的时空空间分布.
主要方法:
- 使用的机器学习算法:随机森林 (RF),支持向量回归 (SVR),人工神经网络 (ANN) 和反复神经网络 (RNN).
- 利用了AT预测的预训和微调过程.
- 在常规和台风条件下使用射电探测器观测验证模型准确性.
主要成果:
- 在全国范围内,RF模型表现出优异的性能,R=0.9992,MAE=0.70°C,RMSE=1.04°C.
- SVR和ANN模型也显示出具有竞争力的准确性.
- 在台风条件下,RNN表现良好,这表明了对特定天气事件分析的潜力.
- 更大的样本大小并不总是保证减少错误.
结论:
- 机器学习有效地融合了各种数据源,以进行连续的垂直AT估计.
- 射频模型对于全国范围的AT映射具有高度准确性.
- 需要进一步研究ML模型在不同天气现象中的适用性.
- 数据量和错误减少并不总是直接相关的.
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