Understanding the level of error within sea state bias models

Alexa Putnam (University of Colorado, Colorado Center for Astrodynamics Research, United States)

CoAuthors

Shailen Desai (Jet Propulsion Laboratory, USA); Robert Steven Nerem (University of Colorado, Colorado Center for Astrodynamics Research, USA)

Event: 2019 Ocean Surface Topography Science Team Meeting

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

Presentation type: Type Poster

The sea state bias remains the largest error for estimating sea level using satellite radar altimetry. Empirical approaches using in-flight observations of sea surface height are typically used to determine models for the sea state bias. These observations contain signals such as dynamic ocean topography, orbit errors, as well as ionosphere, troposphere, tide and mean sea surface modeling errors, which all contribute to errors in the sea state bias estimates. In this presentation we investigate the relative contribution of these error sources to provide an error assessment of the empirical sea state bias models.

In an effort to evaluate errors in the sea state bias estimates, we have executed a series of validation methods that test various estimation approaches. These methods examine the correlation between components within sea level anomaly measurements and the model variables, year-to-year model variation, the dependency on the span of data used for estimation, as well as the impact that model differences have on the estimated global mean sea level. The presented results will summarize the estimation and validation methods, and results from the error analysis.

 

Poster show times:

Room Start Date End Date
The Gallery Tue, Oct 22 2019,16:15 Tue, Oct 22 2019,18:00
The Gallery Thu, Oct 24 2019,14:00 Thu, Oct 24 2019,15:45
Alexa Putnam
University of Colorado, Colorado Center for Astrodynamics Research
United States
alexa.putnam@colorado.edu