Abstract
GNSS Interferometric Reflectometry (GNSS-IR) provides an independent and cost-effective approach for monitoring coastal sea level, but current methods still have important limitations. The classical height-rate correction relies on uniform weighting, which neglects amplitude decay effects, while the WinLSP framework applies bisquare weights directly to unscaled residuals, leading to inconsistent behavior across datasets. We introduced two methodological refinements, a damping-aware height-rate correction that accounts for amplitude decay caused by sea surface roughness and a normalized iteratively reweighted least squares (IRLS) strategy that stabilizes regression through robust residual scaling. Simulation experiments showed that the damping-aware correction reduced systematic errors by up to 50% under strong damping conditions. Real data analyses using one-month datasets from three coastal stations (ALTG, BRST, SHRN) confirmed the improvements. RMS errors decreased from 47.5 to 21.6 cm at ALTG, from 21.9 to 15.5 cm at BRST, and from 24.2 to 17.0 cm at SHRN, while correlations with tide gauges exceeded 99%. Overall, the results demonstrate the effectiveness of the refinements in reducing errors and improving GNSS-IR sea level retrievals across different coastal environments.