## 5 Discussion  
The combination of GNSS-R data retrievals and computer simulations of ocean wave height and sea surface roughness supported our knowledge of the significance in identifying the characteristics of sea states by measuring GNSS reflected signals. For surface winds up to 20 m/s, the trace followed a close-to-Gaussian power density distribution. The associated variances based on the running mean value of the trace lead to the retrieval of ocean wave heights and surface roughness that are close to the theoretical values calculated from the equations and simulations. The changes in the variances of the reflected trace for higher wind fields seems to be driven by horizontal wind directional changes. This effect was partly supported by wind field data from an NWP model. But other phenomena as troposphere turbulence may also be a candidate for the changes, which occurs within minutes.  
One of the advantages of the measurement setup is that the ocean reflection zone is observed for periods of up to 30 min, primarily due to the geometry of the experiment for each GNSS satellite in view of the antenna. Having more antennas, looking in different directions, and receiving signals from several global satellite systems would increase the knowledge of winds, ocean wave height spectra, surface heights (altimetry), and sea surface roughness over a larger area of the ocean. The GNSS-R observations could further be an important contribution to long-term variations of the ocean mean height, as well as the monitoring of ocean mesoscale eddies (with spatial features of 100 km and temporal variability of the order of several days), which result in sea height changes much larger than the accuracy of the GNSS-R technique.  
The number of measurements in each wind speed bin is not equal. Most of the measurements lie in the wind velocity bins from 2 m/s to 10 m/s. Thus, the goodness of fit of the spectral information content results in skewed errors for the higher wind fields.  
The paper also presented the theory and models needed to describe the interaction between the electromagnetic field emitted from a GNSS satellite and ocean waves. The interaction was modeled through a mixed Fourier transform and an Earth impedance concept [27]. Models of both the neutral atmosphere and the dispersive ionosphere were also included to further improve the simulations. These models play an important part in the simulations [20; 27]. Especially the bending and the spectral broadening of the GNSS signal for grazing angle measurements impact the received signal at the receiver.  
Simulations and GNSS-R measurements of the bistatic system (consisting of the transmitting GPS satellite, reflections from the ocean, and the GPS receiver) were performed for a total of 95 datasets. The slopes of the obtained power spectra were directly related to the sea states and, hence, the ocean wave-heights [27]. The simulations also pointed to the fact that the estimates of wave heights and roughness parameters are a function of the spectral domain used for the retrievals. Here, the calculations were done for the frequency range from 90 Hz to 100 Hz, which require an instrument capable of high sampling rates. The instrument in the experiments used an open-loop technique with a sampling rate of 1000 Hz. The advantage of this approach is very detailed \(I\) and \(Q\) measurements that is not hampered by internal instrumental clock errors or the chosen phase-locked loop approach in the receiver. At the same it is possible to analyze atmospheric modulation and GNSS co-channel interference from other GNSS satellites. The Doppler model guiding the open-loop measurements proved to be accurate enough for obtaining the needed spectral resolution that can distinguish the direct and the ocean reflected GNSS signals. To minimize clock errors in the instrument an atomic clock was applied to steer the oscillators of the receiver. Thus, ocean surface measurements can be dependent on the type and the quality of the applied GNSS-R receiver.