An OSSE evaluation of the GNSS-R altimetry data for the GEROS-ISS mission as a complement to the existing observational networks

Abstract

Simulated signals from Global Navigation Satellite Systems (GNSS), reflected off the sea surface and received aboard low Earth orbiting satellites, have been used to derive sea surface height (SSH) and assimilated into an ocean model in an Observing System Simulation Experiment (OSSE). The experimental approach is named GNSS Reflectometry (GNSS-R), which was proposed for the International Space Station (ISS). This scientific experiment was conducted in the frame of the ESA mission called “GNSS REflectometry, Radio Occultation and Scatterometry aboard the International Space Station” (GEROS-ISS). In this study, three sources of uncertainties of the planned GNSS-R altimeter are considered by the GNSS-R simulator: the troposphere, the ionosphere, and a noise term. An ensemble optimal interpolation (EnOI) data assimilation system is set up for an eddy-resolving HYbrid Coordinate Ocean Model (HYCOM) of the South China Sea (SCS), and two data assimilation runs are performed from the 18th June to the 31st July 2014 with and without GNSS-R. In the run assimilating GNSS-R, the measurements come in addition to traditional Sea Level Anomalies (SLA) from present-day altimeters. In spite of the lower precision of individual GNSS-R retrievals, the results obtained in July show an overall improvement of the Root Mean Squared Difference (RMSD) by 14%, compared to traditional altimeter data only. Considering the crossing of Typhoon Rammasun through the SCS, the GNSS-R data improve the realism of the three largest eddies. The temperature sections along the typhoon track show large differences in the upper 200 m depths in excess of 1 °C near the shelf break. Finally, diagnostics of Degree of Freedom for Signal (DFS) provide a quantitative Impact Factor (IF) of the GNSS-R altimetry data over the conventional altimeter data. On average in July, the IF is low (<5%), but for the period of the typhoon it reaches values over 20%. This indicates the complementary of the GNSS-R altimetry data to the present observing system, especially in filling the gaps of the traditional altimeters.