# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 4 Discussion
The higher correlations of the multiplicative than the additive error suggest that the multiplicative error model may yield results closer to the truth. To confirm which error model is applicable for near-surface ocean winds, a study similar to [45] examining error model accuracy is required. It is difficult to compare the results of the TCA, as there have been no earlier studies published TCA for CYGNSS TC ocean winds. The only other study CYGNSS TCA for TCs did not share the results of their correlations, instead using the results as inputs into their machine learning algorithm [29]. We hope that future GNSS-R papers using TCA with other reference datasets will soon follow to verify each products performance. SAR and SMAP's high correlation of errors to ERA5 (R = 0.57, Figure A2c) suggests that an alternative dataset might improve the robustness of the results. The future NISAR mission provides an alternative L-band sensor for TC winds validation [58], which could replace the SAR product. Even after the switch to NISAR, the correlation between SMAP and NISAR will still be high if ERA5 maintains a systematic negative bias in TCs. Demonstrating dataset independence of errors though a TC specific reference dataset, such as HWRF [59], would have provided a more robust assessment. Extended collocation analysis using GNSS-R and more than three other datasets is becoming possible with the ever-increasing number of meteorology remote sensing satellites. Using surface observations, reanalysis, and satellites together could provide an even more rigorous analysis of GNSS-R wind retrievals. One of the strengths of extended collocation analysis is that it allows for a level of inter-dependency between some of the datasets [60]. For example, a reanalysis product that ingests satellite or anemometer data can be included as well as those original satellite and anemometer datasets. The current CYGNSS wind-speed algorithms are based on values derived from only the brightest 9 out of the total 187 pixels in the cropped delay-Doppler map, with an even larger array available in the full delay-Doppler map mode. This increase in information has shown superior accuracy when used in a machine-learning wind-speed retrieval algorithm [61], and could provide information about local storm-development features that can improve the precision of GNSS-R. Fundamental limitations in both datasets of low numbers of samples, low signal-noise ratio, and long latency may be mitigated in future constellations with a greater number of satellites and higher gain antenna [62]. Overall, the performance at lower wind speeds can enable rapid intensification monitoring. The large latency of several months and 12 h temporal averaging in Merged means an alternative algorithm is required for rapid-intensification detection. A product is needed with the short latency as FDS but the accuracy of NOAA. Already, the NOAA algorithm has dependencies on ERA5 that increase the latency to 7 days. A future analysis validating the performance of Merged near-real-time (NRT) will provide further insights into rapid intensification detection. There is an increasing awareness of the multi-hazard nature of TCs, where the coastal flooding intensity has a greater impact on communities than maximum wind speed [63]. As GNSS-R directly measures ocean roughness and then estimates a wind speed, there may be value in focussing on the ocean roughness itself to estimate the severity of a storm surge. More research to determine if CYGNSS correlates to storm surge intensity would be beneficial.