Estimation of the sea state bias error budget for pulse-limited satellite altimetry

Abstract

Using an empirical, non-parametric sea state bias (SSB) modeling method, which was developed as a tool for SSB error analysis (Putnam, Alexa Forthcoming), we provide an error budget for overall SSB error, as well as the contributing sources of this error budget. The error analysis compares methods used to derive SSB models from observed altimeter measurements, collinear differences of measurements from adjacent repeat cycles, and methods using both collinear and crossover differences of measurements. Our error analysis reveals systematic error caused by ionosphere correction uncertainty in SSB models obtained from direct measurements, and wet troposphere correction uncertainty in SSB models generated using difference measurements. Results also expose a correlation to altimeter measurement error, with the backscatter coefficient accounting for over 20% of the SSB evaluation error and SWH accounting for approximately 50-60%. The error analysis presented here suggests SSB errors are lower than the often-used approximation of SSB error as 1% of SWH, except at SWH values less than 2 m where errors are likely larger. We find that increasing the pulse repetition frequency of the altimeter reduces SSB errors. The future for improving empirical, nonparametric SSB estimation primarily depends on improving measured SWH.