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.




sea level anomalies, satellite altimetry, spatiotemporal interpolation, machine learning, random forest regression, north sea


61N, 50S, -4E, 12W


The daily sea level anomalies are estimated using the CMEMS Level 3 (i.e.,along-track) sea level anomalies (SLA), reference number: SEALEVEL_GLO_PHY_L3_REP_OBSERVATIONS_008_062. We recall that the SLA is defined as the sea level above the mean, corrected for atmospheric and tidal effects. A list of all applied corrections is available in Taburet et a. (2019), https://doi.org/10.5194/os-15-1207-2019



Daily sea level anomalies: text files containing unstructured grids spaced by 0.125 degrees in latitude and longitude
7 MoPlain Text DocumentProcessed data
Auxiliary file: tide gauge data from (GESLA-v3) https://gesla787883612.wordpress.com/, corrected for atmospheric effects and tides
26 MoNetCDFProcessed data
How to cite
Passaro Marcello, Juhl Marie-Christin (2022). Daily sea level anomalies from satellite altimetry with Random Forest Regression. SEANOE. https://doi.org/10.17882/89530

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