Bayesian Wind Speed Estimation Conditioned on Significant Wave Height for GNSS-R Ocean Observations

Abstract

paceborne GNSS-Reflectometry observations of the ocean surface are found to respond to components of roughness forced by local winds, and to longer wave swell that is only partially correlated with the local wind. This dual sensitivity is largest at low wind speeds. If left uncorrected, the error in wind speeds retrieved from the observations is strongly correlated with the Significant Wave Height (SWH) of the ocean. A Bayesian wind speed estimator is developed to correct for the long wave sensitivity at low wind speeds. The approach requires a characterization of the joint probability of occurrence of wind speed and Significant Wave Height (SWH), which is derived from archival reanalysis sea state records. The Bayesian estimator is applied to spaceborne data collected by the TechDemoSat-1 satellite, and is found to provide significant improvement in wind speed retrieval at low winds, relative to a conventional retrieval that does not account for SWH. At higher wind speeds, the wind speed and SWH are more highly correlated and there is much less need for the correction.