# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Intercomparisons with Reference Wind Speeds  
#### 3.1.1 Buoy Intercomparison  
In this section, comparisons are presented between surface winds measured by CYGNSS and by buoys.  
RMSD and Bias Assessment Using Buoys  
Figure 5 compares aggregated CYGNSS retrieved surface winds at buoy locations depicted in Figure 2. The retrieved winds closely align with in situ measurements, with correlation coefficients of 0.84 (FDS) and 0.88 (NOAA). However, above \(\sim\)12 m/s, the collocated CYGNSS samples for both FDS and NOAA exhibit increased dispersion and variability. For FDS, the mean bias is 0.03 m/s, while NOAA winds show a mean bias of \(-\)0.22 m/s. These mean biases may result from compensatory effects, where positive biases in low winds counterbalance negative biases in high winds [3]. The Root Mean Square Difference (RMSD) with respect to the buoys is 1.36 m/s (FDS) and 1.21 m/s (NOAA). The trackwise correction and additional spatial averaging applied to NOAA winds may be responsible for its lower RMSD. The RMSD values account for approximately 57% and 50% of the standard deviation of buoy winds in FDS and NOAA, respectively. Given the  
Figure 4: Distribution of the number (#) of data samples in each wind speed bin for the four data products in the storm matchup dataset.  
tropical buoy wind accuracy of 3% and assuming negligible matchup uncertainty, the upper bounds of CYGNSS retrieval uncertainty for wind speeds (<20 m/s) are approximately 1.33 m/s for FDS and 1.17 m/s for NOAA.  
Precipitation Dependence  
While the influence of rain on the L-band CYGNSS measurements is generally expected to be negligible, intense rainfall conditions may potentially affect the bias and RMSD of CYGNSS wind measurements. To explore the rain effect further, we divide our analysis into two categories based on wind speed (following [4]): \(\leq\)6 m/s and >6 m/s. Prior to the rain effect analysis, we eliminate the dependency of CYGNSS residuals on wind following [4].  
Figure 6 illustrates the impact of rain on wind residuals at wind speeds below (Figure 5a,b) and above (Figure 6c,d) 6 m/s. The analysis indicates a slight positive bias in CYGNSS measurements at lower wind speeds (Figure 6b), which increases with higher rainfall rates, consistent with prior findings [4; 20]. For lower wind speeds, the RMSD values (Figure 6a) in both the CYGNSS products show greater variability during moderate (i.e., R > 0.2 mm/h) and heavy (R > 1.0 mm/h) rain conditions as compared with periods of minimal or no rain (R < 0.1 mm/h). However, the heavy rain scenario also introduces higher sampling uncertainties. The observed positive correlation between precipitation rate and wind RMSD and bias at low wind speeds can be attributed to the anticipated effects of weak diffuse scattering models for winds below approximately 5-6 m/s [36]. Furthermore, the current calibration does not include adjustments for rain-induced signal attenuation [37]. On the other hand, at wind speeds above 6 m/s, the effect of rain on RMSD and bias is relatively low (Figure 6c,d). Note that the NOAA product shows higher error levels than FDS. However, differences in performance between these datasets do not achieve statistical significance.  
Figure 5: Density scatterplots comparing (**a**) CYGNSS FDS and (**b**) NOAA wind speed products with near-coincident buoy reference winds. ‘Slope’ represents the best-fit linear regression value, and ‘Corr.’ denotes its correlation coefficient. Correlation coefficients with a significance level (\(p\)-value < 0.05) are marked with an asterisk (*).