Quantifying the Uncertainty in Ground-Based GNSS-Reflectometry Sea Level Measurements

Global Navigation Satellite System reflectometry (GNSS-R) tide gauges are a promising alternative to traditional tide gauges. However, the precision of GNSS-R sea-level measurements when compared to measurements from a colocated tide gauge is highly variable, with no clear indication of what causes the variability. Here, we present a modeling technique to estimate the precision of GNSS-R sea-level measurements that relies on creating and analyzing synthetic signal-To-noise-ratio (SNR) data. The modeled value obtained from the synthetic SNR data is compared to observed root mean square error between GNSS-R measurements and a colocated tide gauge at five sites and using two retrieval methods: spectral analysis and inverse modeling. We find that the inverse method is more precise than the spectral analysis method by up to \text{60}\% for individual measurements but the two methods perform similarly for daily and monthly means. We quantify the contribution of dominant effects to the variations in precision and find that noise is the dominant source of uncertainty for spectral analysis whereas the effect of the dynamic sea surface is the dominant source of uncertainty for the inverse method. Additionally, we test the sensitivity of sea-level measurements to the choice of elevation angle interval and find that the spectral analysis method is more sensitive to the choice of elevation angle interval than the inverse method due to the effect of noise, which is greater at larger elevation angle intervals. Conversely, the effect of tropospheric delay increases for lower elevation angle intervals but is generally a minor contribution.