Accurate extraction of ocean tidal constituents from coastal satellite altimeter records - Fu, Yanguang; Wang, Panlong; Peng, Fukai; Feng, Yikai; Khaki, Mehdi; Mi, Xiaolong (Frontiers in Marine Science, 2025)
Extracting tidal constituents in coastal regions remains a major challenge due to complex bathymetry, nonlinear shallow-water effects, and land contamination in satellite altimetry measurements. While tide gauges provide high-precision tidal observations, their sparse spatial coverage limits their utility for global coastal studies. Global tidal models, though improved by data assimilation, often suffer from reduced accuracy in coastal zones due to limited spatial resolution and insufficient nearshore constraints. To address these limitations, we utilize the newly released International Altimetry Service 2024 (IAS2024) dataset, which is derived from reprocessed Jason-1/2/3 satellite altimetry data covering the period 2002–2022. We extract ten primary tidal constituents (Q1, O1, P1, K1, N2, M2, S2, K2, Sa, and Ssa) in global coastal waters using this dataset. The accuracy of IAS2024 tidal extractions is assessed through comparative analysis with four state-of-the-art global tidal models (DTU16, EOT20, FES2014, and FES2022) and 164 tide gauge records. IAS2024 achieves accuracy levels comparable to EOT20 and superior to FES2014 and FES2022, with performance closely matching that of DTU16. For the eight major tidal constituents, the root sum square error of IAS2024 is 11.26 cm, aligning closely with DTU16 (11.23 cm), EOT20 (11.68 cm), and FES2022 (11.26 cm). Relative errors against tide gauge records are 14.16% (O1), 16.6% (M2), 15.4% (K1), and 17.7% (S2), demonstrating competitive accuracy. Notably, IAS2024 significantly outperforms traditional models in resolving long-period constituents, with amplitude correlation coefficients of 0.924 for Sa and 0.701 for Ssa, markedly surpassing EOT20 and FES2022. IAS2024 shows strong performance within 10 km of the coast—where conventional altimetry is often unreliable—highlighting its potential for coastal applications. Its enhanced ability to resolve long-period tidal variations makes it particularly valuable for coastal sea level research, tidal energy assessments, and hydrodynamic modeling. These findings underscore the strengths of IAS2024 in nearshore tidal extraction and its importance as a dataset for advancing both global and regional tidal studies.