# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 2 Materials and Methods
### Surface-Based Analysis Methodology  
The locations of the anemometers used in analysis are presented in Figure 1.  
CYGNSS measurements for FDS and NOAA products were within 1 h and 0.25\({}^{\circ}\) of the closest anemometer. Linear interpolation was used to estimate the anemometer wind speed when a CYGNSS satellite passed between hourly readings. Pearson R, bias, and root mean square difference (RMSD) are calculated for each station to justify their use as a reference dataset. Then, 2D histogram density plots are calculated to see the variation across all wind regimes.  
Data were quality controlled to remove any negative, NaN, or blank values. According to a wide amount of existing literature, the anemometer winds should be adjusted to match wind-stress, the force that winds exert across the ocean surface [32; 37]. Two methods of adjusting anemometer winds were attempted for the smaller Pacific dataset, firstly using the COARE v3.6 algorithm [38], and then a simplified approach for height adjustment to 10 m winds with different values of surface roughness power-law following [39]. We used an approximate roughness value for the Monin-Obukhov similarity theory [38], as we were unable to estimate surface roughness given the diversity of anemometer sites from coral reefs to lighthouses. As CYGNSS SDR v3+ products are trained on real surface winds from ERA5, there is no need to correct the anemometer data to neutral stability [32]. Unexpectedly, adjusting the anemometer winds produced worse results with higher RMSD and bias than with using the original values. Overall, anemometer values were kept as original values and were not adjusted to 10 m neutral stability.  
The idea of using weighting for measurements at greater temporal or spatial distance from the surface measurement may further improve the results from CYGNSS [32]. However, as GNSS-R signals can have a very large glistening zone, quite noisy wind-estimates, and a low number of samples in NOAA's case, we chose to include all measurements equally.  
Storm events, defined as a period of a minimum of three hours with wind speeds above 17 m/s in the anemometer data, were extracted from the whole dataset for creating timeseries and scatterplots. A threshold of 17 m/s (~34 knots) was chosen, as it is the threshold used by ECMWF for a tropical storm [40], and it allows for more storm events to be considered than the CYGNSS mission's threshold of 20 m/s [23]. A temporal threshold  
Figure 1: Locations of the 22 weather stations used for surface-based analysis. Pacific 5 locations (yellow), Australia 17 locations (red).  
of three hours was chosen, as it limited the analysis to severe events and is the median revisit time for CYGNSS measurements [31]. Four TC events met these thresholds (Ana, Kevin, Niran, and Jasper). Measurements 24 h before and after the storm events were included to investigate CYGNSS's ability to monitor antecedent and preceding conditions. Individual events created timeseries visualisations, and collectively all the measurements were combined to calculate R, RMSD, and bias.