论文标题
关于从卫星高度测定海平面异常的潜力，随机森林回归
On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression
论文作者
论文摘要
卫星高度测定的海平面观测值的特征是稀疏的时空覆盖范围。因此，沿轨道数据通常会插值为每日网格。后者在时间和空间中非常平滑，并使用最佳的插值例程生成，需要几个预处理步骤和协方差表征。在这项研究中，我们评估了随机森林回归估计每日海平面异常的潜力。从2004年开始的沿轨道海平面数据用于构建一个训练数据集，其预测因素是相邻观察结果。验证是基于与潮汐计的每日平均值的比较。与哥白尼常用产品相比，生成的数据集与潮汐量规记录的相关性平均高10％。尽管后者更优化用于检测空间介质，但我们展示了这项研究的方法如何提高海平面变异性的特征。
The sea level observations from satellite altimetry are characterised by a sparse spatial and temporal coverage. For this reason, along-track data are routinely interpolated into daily grids. The latter are strongly smoothed in time and space and are generated using an optimal interpolation routine requiring several pre-processing steps and covariance characterisation. In this study, we assess the potential of Random Forest Regression to estimate daily sea level anomalies. Along-track sea level data from 2004 are used to build a training dataset whose predictors are the neighbouring observations. The validation is based on the comparison against daily averages from tide gauges. The generated dataset is on average 10% more correlated to the tide gauge records than the commonly used product from Copernicus. While the latter is more optimised for the detection of spatial mesoscales, we show how the methodology of this study has the potential to improve the characterisation of sea level variability.
