On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression

TL;DR

This study evaluates the use of Random Forest Regression to estimate daily sea level anomalies from satellite altimetry data, showing improved correlation with tide gauge records compared to existing products.

Contribution

It introduces a novel application of Random Forest Regression for sea level anomaly estimation, enhancing variability characterization over traditional interpolation methods.

Findings

10% higher correlation with tide gauges

Potential to improve sea level variability analysis

Demonstrates effectiveness of machine learning in satellite data processing

Abstract

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.