Determining Undersampled Coastal Tidal Harmonics using Regularized Least Squares

TL;DR

This paper introduces a regularized least squares method to accurately determine tidal harmonics from satellite altimetry data, overcoming sampling limitations and noise, aiding coastal tide change analysis.

Contribution

A novel regularized least squares approach that incorporates regional tidal amplitude priors to improve tidal harmonic estimation from sparse and noisy satellite data.

Findings

High accuracy tidal amplitude estimation in synthetic tests

Effective application to Jason-3 satellite data

Potential for improved coastal tide monitoring

Abstract

Satellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area, but the sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel Regularized Least-Square approach is developed to relax the limitation to the range of satellite operating conditions. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes and phases of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason-3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea-level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.