# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Differences at Different Quality Levels  
SST data are available for a range of quality levels defined in Table 4. Here, we have concentrated on data with quality level 3 to quality level 5 while noting that the current recommendation is that only quality level 5 (best quality) data should be used for accurate analyses. Table 3 shows the statistics for all algorithms/quality levels and shows a similar pattern to the quality level 5 plots in that there are algorithm dependent biases/standard deviations of varying sizes. In particular, the N2 retrieval has an increasingly large bias for quality levels 3 and 4 compared to the other retrieval algorithms. Lastly, as the quality level decreases (from 5 to 3), the standard deviations show an increase for all retrieval algorithms which indicates that there is likely to be a missing uncertainty component related to the quality level itself. We also note that the SLSTR-B GHRSST data includes an SSES bias term which is designed to correct for some of the retrieval coefficient errors. Table 5 shows the SLSTR-A/SLSTR-B SSES bias terms. In the data provided, there was no difference in the bias values between SLSTR-A and SLSTR-B because the data were taken during commissioning where no updated estimates of the SLSTR-B retrieval coefficients or of the SSES values were available. The SLSTR-B values shown in Table 5 are therefore taken from a file taken on 2 July 2019 when SLSTR-B SSES values were available. It should also be understood that the SSES values for quality levels 3 and 4 are not well determined due to a lack of matchup data and that the recommendation is that they should only be trusted for quality level 5 data. Applying the SSES bias values to the mean values from the Tandem data (Table 3) would then give SSES bias corrected means for quality level 5 pixels differences of N2: \(-0.056\), N3: \(-0.023\), D2: \(-0.03\) and D3: \(-0.041\) which are, apart form the D3 case, closer to a zero difference than was the case for uncorrected values. It can also be seen, however, that the SSES bias will not improve most of the means for the lower quality levels, reinforcing the recommendation not to use them for quality levels 3 and 4. One last point to make about the SSES bias term is that there can be significant differences in the bias between quality levels. As we will show below, the Tandem data have cases where the quality level itself changes between SLSTR-A and SLSTR-B observations so the application of the SSES bias term to the data would then lead to spurious jumps in the values. For this reason, and the reason that \begin{table}
\begin{tabular}{c l c l} \hline \hline
**Quality Level** & **QL Comment** & \(\mathbf{P_{\text{Cloudy}}}\) & **Threshold** & **Additional Factors** \\ \hline
0 & No data & \(<\)0 & No data \\
1 & Cloud contaminated data & \(>\)0.5 & \(SST<SST_{\text{min}}\) and/or missing meteorology data \\
2 & Worst quality of usable data & \(>\)0.2 & Zenith Angle \(>\) 55\({}^{\circ}\) \\
3 & Low quality of usable data & 0.1–0.2 & Twilight, inland water \\
4 & Acceptable quality of usable data & \(<\)0.1 & Aerosol detected \\
5 & Best quality of usable data & \({}^{\circ}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Criteria for different quality levels defined for SLSTR (based on private communication, [PERSON], ACRI) and range of \(P_{\text{Clear}}\) per quality level in data.