# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 4 Discussion  
The anemometer surface-based analysis supports the findings from [32] that NOAA performs better than FDS. FDS v3.0 previously over-estimated values around 10 m/s [19]; however, these appear in Figure 2a,d to be corrected in v3.2 by more accurately considering swell [56]. The higher RMSD's and lower correlations in the Pacific than Australia could be attributed to a variety of factors. One reason is because the anemometers in the Pacific dataset are closer to landmasses than those in the Australian dataset. Instead, using a maximum of 1 min gust data as opposed to an hourly average is likely to overestimate the station's wind speed and create the negative biases seen in Figure 4d-f. We are unable to claim that CYGNSS represents hourly averages more accurately than maximum 1 min sustained wind because neither Australian nor the Pacific dataset contained both values. While this could be an avenue of future research, we expect the hourly averages to be more closely aligned with CYGNSS as the GMF is trained on hourly wind estimates from ERA5 [31]. Although we did not have sufficient site information to estimate a value for surface roughness, there are some other options for bias correction. One possibility is to correct the gauge measurements to ERA5 reanalysis by bias or PDF shifting to remove local variationscaused by topography or surrounding land use. This would create an anemometer value that is more similar to ERA5-based GMF in CYGNSS products, at the risk of introducing errors associated with the reanalysis product. Future studies should still adjust their anemometer values if they have environmental data (ocean temp, salinity, downward radiation, etc.) and determine whether it improves performance. The results from extreme events in Figures 3 and 4 suggest that GNSS-R still lacks the sensitivity to potentially detect rapid intensification (RI) events. The relatively small number of satellites in CYGNSS (currently 7 compared to 90+ in GNSS radio-occultation constellations [57]) and large RMSD errors (4+ m/s) suggest that substantial work still needs to be done to improve the reliability of CYGNSS. A specific study focusing on CYGNSS's ability to detect RI events is planned to follow this study and provide specific recommendations. The use of a level 3 gridded version of FDS is expected to have shown improved performance due to QC algorithms and 1 h averaging that reduces noise. The performance of the Merged product in TCA being similar to NOAA is credit to the CYGNSS science team. A future study using the near real-time Merged product will be useful to compare to NOAA. The bias between Merged and SMAP was observed previously at \(-\)5.17 m/s [30], which is a stronger bias than that (\(-\)1.78 m/s) recorded here. This is likely due to the collocation requirements with SAR reducing the number of SMAP values above 40 m/s. NOAA's track-wise bias correction based on ERA5 seems to improve this result while increasing latency. The geophysical model function (GMF) in Merged is based upon HWRF, which results in a good correlation but high RMSE, and an alternative SMAP-based GMF is likely to improve performance with SAR, in particular to remove the strong negative bias in Merged above 17 m/s. The bias at speeds above 17 m/s is possibly caused by the storm-centred gridded product underestimating winds compared to SMAP and SAR. It was previously noted in comparisons to IBTrACS best-track datasets [55], and with SMAP [30], that a storm-centred gridded product underestimates high winds due to the low spatial resolution and 12 h-temporal averaging. Results from comparing multiplicative and additive error models in Table 3 were different to those found in [46], which used precipitation data and showed correlations from the multiplicative being \(\sim\)0.1 lower than in the additive. Our study identified equal or higher correlations in the multiplicative error model, which would be expected as the logarithmic transformation emphasises retrieval accuracy at lower speeds where there are more measurements.