Daily sea level anomalies from satellite altimetry with Random Forest Regression

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.

卫星测高仪所获取的海平面观测数据，其空间和时间覆盖度较为稀疏。鉴于此，沿轨迹数据通常被插值到每日网格中。这些数据在时间和空间上均经过强烈平滑处理，并采用最优插值程序生成，该程序需要经过多个预处理步骤和协方差特征描述。在本研究中，我们评估了随机森林回归在估计每日海平面异常方面的潜力。2004年的沿轨迹海平面数据被用于构建训练数据集，其预测因子为邻近观测值。验证基于与潮汐站每日平均值的比较。生成的数据集与常用的哥白尼卫星观测产品相比，平均相关度提高了10%。尽管后者在检测空间中尺度方面进行了优化，但我们展示了本研究方法在提高海平面变化特征描述方面的潜力。