Abstract's details

Estimating the sea state bias for TOPEX

Alexa Putnam (University of Colorado Boulder, United States)


Shailen Desai (Jet Propulsion Laboratory, United States); R. Steven Nerem (University of Colorado Boulder, United States)

Event: 2020 Ocean Surface Topography Science Team Meeting (virtual)

Session: Instrument Processing: Propagation, Wind Speed and Sea State Bias

Presentation type: Type Forum only

Contribution: PDF file


A nonparametric approach for modeling the sea state bias (SSB) has been applied to create Ku- and C-band SSB models for the recently reprocessed TOPEX data. Our nonparametric approach, here referred to as the least squares interpolation (LSQI) model, is analogous to prior nonparametric approaches. However, our approach implements inverse bilinear interpolation of significant wave height and wind speed to solve for the unknown sea state bias model using least squares without constraints where there are a sufficient number of observations. Given that the sea state bias remains the largest error for estimating sea level using satellite radar altimetry, our approach was originally developed to provide a direct means of generating an SSB error budget while maintaining at least the same level of precision as previous SSB models. The model observable consists of a weighted combination of both crossover and collinear measurements in order to utilize the high temporal and spatial resolution provided by crossover and collinear measurements, respectively.

The LSQI model is developed in four steps, the first of which produces a raw solution. The raw solution is then leveled in the second step, and smoothed and extrapolated in the third to provide the final SSB model. An optional fourth step has also been developed to account for the downstream impact of the Ku- and C-band SSB models on the ionosphere correction, but is not necessarily linked to the determination of the SSB model itself. In this step, we introduce a dual-frequency ionosphere correction calibration bias derived from the relative relationship between Ku- and C-band range+SSB. This enables the dual-frequency ionosphere correction to be calibrated in such a way to ensure that the majority of the ionosphere corrections reflect the physically-required positive Total Electron Content (TEC), with negative TEC values effectively resulting from noise in the range measurements.
Alexa Putnam
University of Colorado Boulder
United States