Spline-Based Nonparametric Estimation of the Altimeter Sea-State Bias Correction

Abstract

This letter presents a new nonparametric approach, based on spline (SP) regression, for estimating the satellite altimeter sea-state bias (SSB) correction. Model evaluation is performed with models derived from a local linear kernel (LK) smoothing, the method which is currently used to build operational altimeter SSB models. The key reasons for introducing this alternative approach for the SSB application are simplicity in accurate model generation, ease in model replication among altimeter research teams, reduced computational requirements, and its suitability for higher dimensional SSB estimation. It is shown that the SP- and LK-based SSB solutions are effectively equivalent within the data-dense portion, with an offset below 0.1 mm and a rms difference of 1.9 mm for the 2-D (wave height and wind speed) model. Small differences at the 1-5-mm level do exist in the case of low data density, particularly at low wind speed and high sea state. Overall, the SP model appears to more closely follow the bin-averaged SSB estimates.