Determining Undersampled Coastal Tidal Harmonics Using Regularized Least Squares

Ruo Qian Wang, Behzad Golparvar, Morgan Mark

Research output: Contribution to journalArticlepeer-review


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 its 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 sampling interval limit to the range of 9–11 days, reaching the revisit time of the existing satellites. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes 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.

Original languageEnglish (US)
Article numbere2023EA002885
JournalEarth and Space Science
Issue number8
StatePublished - Aug 2023

All Science Journal Classification (ASJC) codes

  • Environmental Science (miscellaneous)
  • Earth and Planetary Sciences(all)


  • coastal process
  • harmonic analysis
  • machine learning
  • tidal process


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