# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 5 Results
### Inter-Comparisons of SA-Based ASL Trends
#### 5.4.3 Gridded vs. Instantaneous SA-Derived ASL Trends  
Gridded SA data are frequently utilized for sea-level trend estimations ([29; 45; 46; 83]; etc.). Gridded data are quite convenient, as they average data from various missions, bridging temporal gaps between them and allowing for mutual usage with other data products. This study has focused on using along-track data for sea-level trend estimates. The results have shown that, for the long-term sea-level trends, the SA-based estimates perform reasonably well when compared with hourly TG-derived trends, yielding RMSEs that vary between 1.2 and 1.5 mm/yr. Recall that the long-term (1995-2022) TG sea-level \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Station** & **TG** & **SA1** & **SA2** \\ \hline
1 & Dirhami & 3.12 \(\pm\) 0.33 & **4.33 \(\pm\) 0.23** & 5.02 \(\pm\) 0.28 \\
2 & Ristna & 4.09 \(\pm\) 0.19 & 6.26 \(\pm\) 0.19 & **5.05 \(\pm\) 0.22** \\
3 & Ventspils & 1.89 \(\pm\) 0.18 & **7.79 \(\pm\) 0.20** & 8.09 \(\pm\) 0.24 \\
4 & Ustka & 1.22 \(\pm\) 0.19 & 7.25 \(\pm\) 0.20 & **4.49 \(\pm\) 0.25** \\
5 & Kolobrzeg & 1.57 \(\pm\) 0.19 & 5.63 \(\pm\) 0.22 & **4.90 \(\pm\) 0.45** \\
6 & Swinoujiscie & 2.91 \(\pm\) 0.19 & 9.25 \(\pm\) 0.26 & **4.39 \(\pm\) 0.26** \\
7 & Sassnitz & 1.54 \(\pm\) 0.17 & 6.39 \(\pm\) 0.19 & **3.12 \(\pm\) 0.22** \\
8 & Rodvig & 3.54 \(\pm\) 0.19 & 5.20 \(\pm\) 0.21 & **4.84 \(\pm\) 0.24** \\
9 & Simrishamn & 2.75 \(\pm\) 0.17 & 5.47 \(\pm\) 0.20 & **5.39 \(\pm\) 0.25** \\
10 & Kungsholmsfort & 3.91 \(\pm\) 0.15 & **6.76 \(\pm\) 0.18** & 10.83 \(\pm\) 0.36 \\
11 & Marviken & 4.17 \(\pm\) 0.15 & **5.39 \(\pm\) 0.27** & 5.51 \(\pm\) 0.28 \\
12 & Hanko & 4.55 \(\pm\) 0.17 & **3.89 \(\pm\) 0.23** & 3.61 \(\pm\) 0.29 \\
13 & Pori & 4.46 \(\pm\) 0.18 & 2.84 \(\pm\) 0.25 & **4.70 \(\pm\) 0.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sea-level trend [mm/yr] from TG observation and different orbit inclination categories of SA missions (high- and low-inclination orbit) during 2003–2022 at different stations. Better agreements with the ground truth are denoted in bold font. Figure 11: Estimated sea-level trend at the TG locations for two SA orbit inclination (high—red; low—yellow) categories of SA data compared to the TG-derived trend (blue) for the period of 2003–2022. The whiskers represent uncertainties using 95% CI. tends varied from 2.1 to 5.4 mm/yr and the along-track SA was 2.5 to 5.3 mm/yr (see Section 5.1 and Table 6). These results are more or less comparable with other studies that have also shown long-term trend estimates of 3.4 mm/yr ([PERSON] et al., 2019) and 3 mm/yr ([PERSON] et al., 2019). In practice, gridded SA data employ averaging methods and various techniques in processing, which could potentially result in the misinterpretation of sea-level trends. This effect might be more pronounced in coastal areas, impacting their approach and analysis. Moreover, it is also shown in Section 5.4.2 that different SA missions may give different results, whereas this can vary spatially. Regional sea-level trend estimation using SA is more challenging compared to the utilization of SA products for global-scale studies. It is expected that the Baltic Sea area, which has numerous archipelagos, small islands, and rocks within \(\sim\)10 km of the mainland, may contaminate the radar signal, making less opportunities for SA beams to interact with the open water and leading to errors ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). The question now remains as to whether utilizing the SA along-track instantaneous data for sea-level trend research is advantageous over other gridded products. Consequently, this study performs a comparison of sea-level trends from instantaneous along-track SA data with that of three available gridded datasets within this region.