# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Sensitivity to Significant Wave Height  
The sensitivity of GNSS-R signals to the long wave portion of the sea state roughness spectrum is discussed in Section 2.1.1. Wind speed retrieval algorithms can attempt to correct for this sensitivity in different ways. With the FDS product, an additive correction is applied after the GMF mapping from Level 1 observables to wind speed using a lookup table that is indexed by the Significant Wave Height (SWH) [41]. With the NOAA product, the GMF itself is indexed by both the L1 observable and the SWH to correct for the sensitivity. The effectiveness of these corrections is examined here. First, to illustrate the importance of the correction, Figure 16a shows a density scatterplot of the error in FDS wind speed vs. SWH if no correction is applied. The error shown is the difference between the FDS wind without its lookup table correction applied and coincident ERA5 matchup wind speeds. In the figure, it can be seen that there is a large positive dispersion of CYGNSS winds at lower SWH values and a large negative bias in the FDS winds at higher SWH values. Figure 16b shows a similar density scatterplot of FDS error after the SWH correction has been applied. Both the low-SWH dispersion and high-SWH bias have been largely mitigated. A similar density scatterplot of the error in the NOAA product is shown in Figure 16c. A small positive dispersion is evident at low SWH values, as is a small positive bias at high SWH values, but both errors are much smaller than those seen in Figure 16a when no correction for SWH has been made.  
For comparison purposes, the dependence of wind speed error on SWH is shown in Figure 17 for wind products produced by ASCAT-B and GMI. ASCAT-B, a C-band scatterometer, is sensitive only to the capillary wave portion of the surface roughness spectrum, which is largely wind driven. GMI, being a microwave radiometer, is primarily sensitive to the fractional foam coverage on the ocean surface above 7 m/s [31]. In both cases, there  
Figure 16: Dependence of CYGNSS wind speed error on SWH: (**a**) FDS without SWH correction, (**b**) FDS with SWH correction, and (**c**) NOAA with SWH correction.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **TD** & **TS** & **TY/HU** \\ \hline Wind fields with no R34 values reported & 97\% & 81\% & 33\% \\ \hline Wind fields with at least one R34 value reported that report only 1 quadrant & 74\% & 33\% & 12\% \\ \hline Wind fields with at least one R34 value reported that report 2 quadrants & 12\% & 27\% & 17\% \\ \hline Wind fields with at least one R34 value reported that report 3 quadrants & 9\% & 15\% & 16\% \\ \hline Wind fields with at least one R34 value reported that report all 4 quadrants & 6\% & 24\% & 54\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: R34 MRG temporal and spatial coverage statistics for storms occurring August 2018–November 2023, broken down by the corresponding Best Track storm category of the wind fields.  
is no significant dependence of the error on SWH. This represents a significant difference between GNSS-R and other remote sensing modalities for wind speed determination.