## 3 Results
### Comparison to Satellite and Model Salinity
For both datasets, the RSS70 showed moderate positive correlation with saildrone 1060, whereas the JPL showed the highest positive correlation to data from saildrones 1026 and 1061. For all three saildrones, HYCOM had the second lowest correlation coefficient and RSS40 had the lowest as it is a higher resolution and noisier dataset. The other variations in STD and Spearman coefficient values will be discussed further in Section 4. Figure 5 explores salinity dependence in the satellite retrievals. RSS70 (Figure 5a) has the least spread of all the datasets above 35 psu indicating good agreement but appears to be too salty in the fresh tongue as reflected by the preponder \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{2}{c}{**Product**} & \multicolumn{4}{c}{**8-Day L3 Salinity**} & \multicolumn{4}{c}{**Orbital L2 Salinity**} \\ \hline  & & **Mean** & **STD** & **Spearman** & **Number of** & **Mean** & **STD** & **Spearman** & **Number of** \\  & **(psu)** & **(psu)** & **Correlation** & **Samples** & **(psu)** & **(psu)** & **Correlation** & **Samples** \\ \hline \multirow{4}{*}{1026} & JPL & \(-0.166\) & \(0.241\) & \(0.859\) & \(208\) & \(-0.113\) & \(0.460\) & \(0.631\) & \(160\) \\  & RSS70 & \(-0.273\) & \(0.270\) & \(0.812\) & \(206\) & \(-0.187\) & \(0.398\) & \(0.593\) & \(210\) \\  & RSS40 & \(-0.279\) & \(0.323\) & \(0.759\) & \(206\) & \(-0.188\) & \(0.658\) & \(0.432\) & \(210\) \\  & HYCOM & \(-0.127\) & \(0.520\) & \(0.560\) & \(208\) & \(-0.105\) & \(0.522\) & \(0.544\) & \(160\) \\ \hline \multirow{4}{*}{1060} & JPL & \(-0.173\) & \(0.257\) & \(0.835\) & \(222\) & \(-0.105\) & \(0.545\) & \(0.584\) & \(169\) \\  & RSS70 & \(-0.298\) & \(0.261\) & \(0.857\) & \(221\) & \(-0.210\) & \(0.388\) & \(0.672\) & \(212\) \\  & RSS40 & \(-0.298\) & \(0.312\) & \(0.791\) & \(221\) & \(-0.159\) & \(0.666\) & \(0.481\) & \(212\) \\  & HYCOM & \(-0.183\) & \(0.509\) & \(0.487\) & \(222\) & \(-0.172\) & \(0.528\) & \(0.475\) & \(169\) \\ \hline \multirow{4}{*}{1061} & JPL & \(-0.158\) & \(0.246\) & \(0.868\) & \(196\) & \(-0.012\) & \(0.449\) & \(0.662\) & \(160\) \\  & RSS70 & \(-0.273\) & \(0.261\) & \(0.836\) & \(194\) & \(-0.201\) & \(0.391\) & \(0.601\) & \(209\) \\ \cline{1-1}  & RSS40 & \(-0.271\) & \(0.320\) & \(0.771\) & \(194\) & \(-0.198\) & \(0.664\) & \(0.411\) & \(209\) \\ \cline{1-1}  & HYCOM & \(-0.131\) & \(0.534\) & \(0.541\) & \(196\) & \(-0.099\) & \(0.519\) & \(0.541\) & \(160\) \\ \hline \end{tabular}
\end{table} 
Table 2: Mean difference and standard deviation difference (STD) for saildrone minus satellite and HYCOM data, as well as Spearman correlation coefficient from saildrone and satellite/HYCOM between 17 January and 2 March 2020.  
less than 0. RSS40 (Figure 5c) data points are the most spread, which corresponds with the high noise content of the dataset. Additionally, all datasets appear to show a negative salinity bias which suggests that the model/satellite tends to overestimate salinity. The JPL appears to have the smallest negative bias out of the satellite datasets, and it was found that 56.8% of all JPL measurements for all three saildrones were overestimating SSS, as compared to 73% of RSS70 measurements and 61.6% of RSS40 measurements. The HYCOM data (Figure 5d) is the least spread dataset of all, which is an indication of the lack of noise in the data due to the model's SSS relaxation The majority of the HYCOM SSS values were recorded within 1 psu difference of the saildrone SSS except for a few outliers which presumably were the values recorded within the fresh tongue. HYCOM SSS appeared to be at maximum 2 psu greater than the saildrone SSS in this area. The linear relationship between saildrone and HYCOM SSS implies that the model's SSS relaxation to GDEM4 climatology limits its salinity values to a certain range.