# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 4 Discussion  
In this study Level-2 and Level-3 sea level altimetry products were validated using different datasets (GNSS, geoid model, buoy, tide gauges). This provided a good opportunity to cross-estimate the validation results and thereby give more reliability to the findings. The results (Tables 4 and 5, Figure 10, Figure 11a, Figure 12a) indicated a good accuracy of the Level-2 product which also confirmed the findings of the previous studies [10; 11]. Additionally, the results of validation gave an overview of the quality of different products (Level-2 and Level-3) on the coast of the Baltic Sea. In fact, Level-3 product is already validated and designated for the final user. The Level-2 product was calculated by using OCOG retracker (see Section 2.4) for this study by the authors. The validation results for both products were similar and the RMSE of the altimetry products remained around decimetre level for the sea coast. It confirmed that accuracy of both products was on the same level. The differences were found between the results of validation at the sea-coast and at the lakes. Accuracy of altimetry in terms of RMSE was almost three times lower in the lakes; this was observed in validation with GNSS and geoid as well. Compared with marine conditions, the worse performance on the lakes could be attributed to several factors, such as vegetation coverage near shore and the hooking effect from the surrounding land [37]. Furthermore, the geophysical corrections (Equation (1)) for continental waters were arguably not as accurate as for the open sea [38]. Additionally, in winter, the results are also affected by the sea/lake ice [9]. From January to March, both studied lakes are covered by ice and snow up to 0.4 m thick. Hummock ice frequently occurred in the coastal areas. Thus, the average residual differences between the altimetry products in the lakes were 0.14 \(\pm\) 0.16 m and 0.13 \(\pm\) 0.27 m, respectively (Table 5). The results showed a larger residual in the lakes in winter months (Figure 9). Therefore, in the future, more emphasis should be placed on the validation of altimetry products at the lakes in the presence of ice. Additionally, correlation analysis between in situ measurements and reanalysis model NEMO output were carried out. The good correlation between the SLA measurements and the \(\mathit{SLA}_{\mathit{NEMO}}\) (Figures 11b and 12b) proves that the reanalysis model NEMO calculates water level dynamics very well. This is in accordance with the accuracy of the CMEMS reanalysis product in the relatively dynamic coastal sea area [32]. However, the RMSE in the current study is even larger than in the model product quality analysis (0.07 m) [32]. The higher RMSE value is clearly influenced by the bias (ME = 0.26 m). The large RMSE can be seen on the frequency distribution of water level anomalies in Figure 13. The maximum frequency distribution of model results is around zero water level, whereas it is around +0.2 m for \(\mathit{SLA}_{\mathit{TG}}\) measurements. However, the small STD (\(\pm\)0.10) shows a good fit of the model with the observations when bias is removed and is comparable with the RMSE found in [32]. Figure 12: Comparison of buoy-based sea-level measurements (\(\mathit{SLA}_{\mathit{busy}}\)) with (**a**) altimetry CMEMS Level 3 sea-level anomaly (\(\mathit{SLA}_{\mathit{LS}}\)) and (**b**) NEMO reanalysis model (\(\mathit{SLA}_{\mathit{NEMO}}\)) in the Gulf of Riga (green circle in Figure 2). The red line is the linear regression between the two datasets. The STD of differences calculated between \(\mathit{SLA}_{\mathit{TG}}\) measurements and \(\mathit{SLA}_{\mathit{L3}}\) (Figure 11a) was higher than that of the model outcome comparison (Figure 11b).