Abstract's details
On the potential of mapping sea level anomalies from satellite altimetry with Random Forest Regression
CoAuthors
Event: 2023 Ocean Surface Topography Science Team Meeting
Session: Coastal Altimetry
Presentation type: Type Poster
Contribution: PDF file
Abstract:
The sea level observations from satellite altimetry are characterized by sparse spatial and temporal coverage. For this reason, along-track data are routinely interpolated into daily grids provided by the Copernicus Marine Service. These are strongly smoothed in time and space and are generated using an optimal interpolation routine requiring several pre-processing steps and covariance characterization.
In this study, we assess the potential of Random Forest Regression to estimate daily sea level anomalies. One-year-long records of along-track sea level are used to build a training dataset whose predictors are the neighboring 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. We compare using Root Mean Square Error against tide gauge data, which we use as ground truth. Moreover, improvements in the temporal characterization of the sea level variability will be shown by means of a coherence analysis to be spread over all sub-annual periods. While the current Copernicus daily sea level anomalies are more optimized for the detection of spatial mesoscales, we show how the methodology of this study can improve the characterization of sea level variability, particularly in the coastal zone.
Our study fits into coastal studies in the context of pan-European coastal zone monitoring since this innovative machine-learning-based technique is validated along the coast of the North Sea. The publication of this study is freely available: https://doi.org/10.1007/s10236-023-01540-4
In this study, we assess the potential of Random Forest Regression to estimate daily sea level anomalies. One-year-long records of along-track sea level are used to build a training dataset whose predictors are the neighboring 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. We compare using Root Mean Square Error against tide gauge data, which we use as ground truth. Moreover, improvements in the temporal characterization of the sea level variability will be shown by means of a coherence analysis to be spread over all sub-annual periods. While the current Copernicus daily sea level anomalies are more optimized for the detection of spatial mesoscales, we show how the methodology of this study can improve the characterization of sea level variability, particularly in the coastal zone.
Our study fits into coastal studies in the context of pan-European coastal zone monitoring since this innovative machine-learning-based technique is validated along the coast of the North Sea. The publication of this study is freely available: https://doi.org/10.1007/s10236-023-01540-4