# Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
## 3 Results
### CryoSat-2 SSH Calibration  
Some of the CryoSat-2 altimeter passes overfly the Senetosa or Ajaccio facilities (or both). These passes are listed on Figure 1 (with their relative orbit number). In this study, we analyzed all these overflights with the same methodology as used for Sentinel-3A. In terms of our data set, we used the SARvatore service [19], which is part of the European Space Agency (ESA) grid processing on demand (G-POD) service. G-POD allows us to select an area and then process all of the L1a (FBR, full bit rate) data available to provide L2 products. It is possible to choose different options, but we have left all the options as default values. G-POD uses the SAMOSA2 retracker as default. However, another retracking approach tailored for coastal zones [11; 12] was also available (SAMOSA+, SAR altimetry mode studies and applications) and we generated a separate set of data using this option. The different processing schemes were then used to analyze their impact on the SSH bias: SAMOSA2 for one set and SAMOSA+ for the other. The details on the data sets are given Section 0. The configurations of the overflights are different from one CryoSat-2 ground track pattern to another (see Figure 1 right) but most of them have data very close to the coast (a few hundred meters, see Table 3). Descending pass #2426 is particularly interesting because the closest data is at \(\sim\)1400 m from the M4/M5 tide gauges at Senetosa. Figure 8 illustrates the quality of the data for cycle 1 and the improvement for some data when using SAMOSA+ retracking. For the data before the point of closest approach (PCA), the SSH from SAMOSA+ decreases and appears more homogeneous with the rest of the time series (marked by green arrows in Figure 8a,b): one measurement at only 400 m across-track from the coast has been clearly improved even with about 37% of the footprint on land (marked by red arrows in Figure 8). However, most importantly, all the data immediately after the coastline (in time after the PCA, because it is a descending track) are of very good quality (marked by blue arrows in Figure 8) and do not look contaminated by land effects even with the classic SAMOSA2 retracker. This is clearly linked to the small area of the footprint for SAR in comparison with LRM. In this example, the footprint is a rectangle with a width of 300 m along-track and a length across-track of 3000 m (see white rectangle area in Figure 8c). Figure 7: Average of Senetosa and Ajaccio Sentinel-3A SSH biases for both SAR and PLRM (upper panel). The lower panel shows the differences between SAR and PLRM for the “orbit-range” (green circles) and the total SSH bias (blue squares). Table 3 provides the details of the SSH calibration for each pass and for the two retrackings used, and Figure 9 illustrates the time series of these SSH biases. Two passes in the Ajaccio area were not used because too few data were in the geoid area (#3563 and #5308, see Figure 1). The standard deviations of the individual mean SSH bias were respectively 14.6 mm and 14.0 mm for SAMOSA2 and SAMOSA+. This shows a small improvement for SAMOSA+, but if the same computation is done only for the \"coastal\" passes (#4077, #4794 and #2426), the standard deviations are this time respectively 15.2 mm and 12.5 mm. SAMOSA+ clearly improved the SSH when very close to the coast, avoiding some outliers even in the time series (see data circled in red in Figure 9 for pass #4794 at Ajaccio). The mean SSH bias for Senetosa and Ajaccio was \(-\)746 mm for both SAMOSA2 and SAMOSA+, but the overall standard deviation of the time series was improved by \(\sim\)13 mm with SAMOSA+ (\(\surd\)(29\({}^{2}\)-26\({}^{2}\))).