  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.
