# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Uncertainties  
Along with the SSTs, the SLSTR L2 data come with two forms of uncertainty. The first is the GHRSST uncertainty parameterized in the SSES (Single Sensor Error Statistic) standard deviation. For each algorithm and quality level, there is a single value which provides a very coarse estimate of what the SST uncertainty really is. In addition, included with the SLSTR L2 is an estimate of a theoretical uncertainty based on a combination of instrumental (L1) uncertainty terms (of order 0.05 K see Figure 4) as well as other, more systematic terms related to possible retrieval biases such as the pseudo-random uncertainty (of order 0.07 K, [3]). Figure 11 compares the SSES values to the theoretical uncertainties and shows that there are distinct differences between the SSES value per algorithm and the theoretical estimates. For N3/D2, it looks like there may be some small, missing components to the theoretical uncertainty budget, but for N2 and D3 the difference is far larger. For N2, the uncertainty-normalised distribution standard deviation looks like the retrieval is significantly over estimating the theoretical uncertainty, while for D3 the uncertainties look to be significantly underestimated. Of course, the SSES values are themselves uncertain due to to representivity issues (including spatial errors, time errors and geolocation errors). For the purpose of this study, we have concentrated on the theoretical uncertainties primarily because they more closely approximate a metrologically based uncertainty. However, it must be remembered that the systematic components of SST uncertainty that are covariant between SLSTR-A and B are likely to be much larger than was the case for the L1 analysis so the uncertainty-normalised distribution standard deviation should be less than 1. In fact, we expect the standard deviation to be closer to 0.5 given the relative size of the random and systematic components included in the uncertainty model (see above). Figure 12 shows the uncertainty-normalised distribution for each algorithm and indicates again that the theoretical uncertainty will need revision. As discussed previously, comparing the SSES to the N2 theoretical uncertainties implied that many were  
Figure 10: Left-hand plot shows the mean SST difference between SLSTR-A and SLSTR-B as a function of cloud probability for quality level 5 data together with the standard error on the mean where the data has been normalised to the zero cloud probability case. Different colours show different retrieval algorithms (N2—Orange (+), N3—Green (\(\times\)), D2—Brown (\(\bigtriangledown\)), D3—Purple (\(\triangle\))). Right-hand plot shows the same normalised data plotted as a function of the cloud probability difference. overestimates relative to the SSES values, whereas the D3 theoretical uncertainties seem to be significantly underestimated. We note that, from [3], the N2 theoretical uncertainty model for the pseudo-random component includes a sec\((\theta)\) term based on an AATSR model which is not included for the other retrievals. Given that SLSTR has a much wider swath than the AATSR, the use of an AATSR based model may be causing issues with the SLSTR N2 uncertainty model. For D3, it looks like there are significant missing uncertainty components the origin of which will need to be further investigated. In the case of the N3 and D2 retrievals, the uncertainty-normalised distribution width is more in line with the expectations assuming the values of the random and systematic uncertainties quoted above. Looking at the other quality levels, Table 7 shows a similar pattern to that shown in Figure 12. It can also be seen that the standard deviations in general are larger for the lower quality SSTs implying that, as discussed above, there should be an extra component of uncertainty related to quality level. In part, this may be related to the bias trend seen in the data related to the probability of cloud shown above which will, of course, have its own component of uncertainty.