Accounting for shelf width in selecting altimetry observations for coastal sea level variability improves its agreement with tide gauges

TL;DR

A new bathymetry-informed algorithm improves satellite-based coastal sea level measurements by better selecting observations, leading to higher correlation with tide gauges across diverse coastal regions.

Contribution

Introduces a dynamically varying search radius algorithm that leverages bathymetry to enhance satellite observation selection for coastal sea level validation.

Findings

Higher median correlation with tide gauges across tested stations.

Lower median NRMSE indicating improved accuracy.

Enhanced performance of low-resolution products to match high-resolution data.

Abstract

A novel dynamically varying search radius algorithm is developed that takes advantage of bathymetry information to choose satellite observations that represent coastal sea level variability better. The algorithm is successfully tested at 155 tide gauge stations around the globe and demonstrates broader applicability across different coastal regimes compared to existing validation methods. This is supported by consistently higher median correlation and lower median Normalized Root Mean Square Error (NRMSE). Furthermore, the new algorithm improves the efficacy of the low-resolution product, X-TRACK SLA L2P v2022 (XTRACK), and makes it comparable to the high-resolution (20Hz) coastal products: Along-track sea level anomalies and trends v2.3. Using the algorithm at over 267 stations, XTRACK data shows improved agreement with tide gauges for both linear and non-linear trends. In some regions, such as tidally-dominated estuaries and the Eastern Australian coast, lower correlation and higher RMSE for residual signals are reported, which are discussed.