## 3 Results
### Comparison to Satellite and Model Salinity  
Figure 4 shows the temporal plots of salinity for each saildrone versus the three satellite salinity datasets described earlier. The SMAP SSS values are represented as points and the saildrone and HYCOM values are represented as lines. This is to account for and represent the variations in time between the saildrone and SMAP measurements. L2 SMAP data from the JPL, RSS70 and RSS40 were able to capture the main salinity features detected by the saildrones. SSS from the saildrones showed 3 fresh tongues during the 45-day cruise. These are characterized by a 1.5 psu decrease in salinity on 5 February, a 1 psu decline on 7 February and the most significant decline of 2 psu during 16-19 February (this is discussed further in Section 3.4). The saildrone and remotely sensed data were in general agreement, with differences that are not temporally dependent. Consistent with the earlier discussion, RSS40 displays the greatest variability. All satellite SSS clearly measure the strong fresh tongue, but the HYCOM model did not detect the fresh tongue. To further analyze the relative performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vehicle** & \multicolumn{3}{c}{**SBE37-RBR Salinity (psu)**} \\ \hline  & **Mean** & **STD** & **RMSE** & **Number of Samples** \\ \hline
1026 & 0.052 & 0.002 & 0.052 & 66,240 \\
1060 & 0.057 & 0.002 & 0.058 & 66,240 \\
1061 & 0.059 & 0.0004 & 0.061 & 66,240 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean difference, standard deviation difference (STD) and root mean squared error (RMSE) between each saildrone’s SBE and RBR instruments used to retrieve salinity from the western Tropical Atlantic, between 17 January–2 March 2020. Figure 4: Salinity time series from SBE37, JPL, RSS40 and RSS70 for (**a**) saildrone 1026, (**b**) saildrone 1060 and (**c**) saildrone 1061 from 20 January–3 March 2020.  
of the SMAP and HYCOM salinity products, statistical comparisons along the entire saildrone track, for the duration of the cruise, are shown in Table 2.  
Table 2 provides the mean difference, standard deviation difference (STD) and the Spearman correlation coefficient for JPL, RSS40, RSS70 and HYCOM SSS data as compared to the SSS data from each saildrone. All repeat observations have been removed and observed salinity data is from the SBE37 instrument on each saildrone. For both the 8-day L3 Salinity and the Orbital L2 Salinity, the 3 h HYCOM product was collocated and interpolated to the times and locations of each of these datasets. These collocated HYCOM values are compared to their corresponding saildrone values to calculate the statistics displayed in Table 2.  
For each of the L2 and the L3 salinity SMAP datasets, the saildrone observations were fresher than the satellites and model SSSs. The L3 data are consistently more saline than the L2 data. This could be because the L3 dataset consisted of an average of high salinity data points as compared to the individual L2 data points. Averaging in L3 products smooths a transient fresh tongue event that may be present in only 1 or 2 days of the 8-day average. However, in the individual data points in the L2 dataset, the fresh tongue would be more prominently represented. For the L2 values, the RSS40 dataset has the highest STD values overall, HYCOM had the second highest STD values, followed by JPL and then RSS70. For the L3 STD values, HYCOM had the highest STD, followed by the RSS40, then RSS70 and then the JPL. The L3 STD values are smaller than the orbital L2 STD values, which is most likely due to the reduced noise in the L3 dataset because of the inclusion of averaged data. The Spearman correlation coefficients for the L3 data were higher and therefore more closely correlated to the saildrone data than the orbital data, which suggests that the linear variability in saildrone data is closely related to the linear variability in L3 data versus orbital data. This is likely due to the reduced noise in the L3 dataset.