It has been observed that when using sea levels derived from GPS (Global Positioning System) signal-to-noise ratio (SNR) data to perform tidal analysis, the luni-solar semidiurnal (K2) and the luni-solar diurnal (K1) constituents are biased due to geometrical errors in the reflection data, which result from their periods coinciding with the GPS orbital period and revisit period. In this work, we use 18 months of GNSS SNR data from multiple frequencies and multiple constellations at three sites to further investigate the biases and how to mitigate them. We first estimate sea levels using SNR data from the GPS, GLONASS, and Galileo signals, both individually and by combination. Secondly, we conduct tidal harmonic analysis using these sea-level estimates. By comparing the eight major tidal constituents estimated from SNR data with those estimated from the co-located tide-gauge records, we find that the biases in the K1 and K2 amplitudes from GPS S1C, S2X and S5X SNR data can reach 5 cm, and they can be mitigated by supplementing GLONASS- and Galileo-based sea-level estimates. With a proper combination of sea-level estimates from GPS, GLONASS, and Galileo, SNR-based tidal constituents can reach agreement at the millimeter level with those from tide gauges.

据观察，当使用从 GPS（全球定位系统）信噪比（SNR）数据得出的海平面进行潮汐分析时，月日半日（K2）和日月日日（K1）成分由于反射数据中的几何误差而产生偏差，这是由于它们的周期与 GPS 轨道周期和重访周期一致造成的。在这项工作中，我们使用来自三个站点的多个频率和多个星座的 18 个月的 GNSS SNR 数据来进一步研究偏差以及如何减轻偏差。我们首先使用来自 GPS、GLONASS 和 Galileo 信号的 SNR 数据（单独或组合）来估计海平面。其次，我们利用这些海平面估计值进行潮汐谐波分析。通过将SNR数据估计的八个主要潮汐成分与同地验潮仪记录估计的潮汐成分进行比较，我们发现GPS S1C、S2X和S5X SNR数据的K1和K2幅度的偏差可达5厘米，并且可以通过补充基于 GLONASS 和 Galileo 的海平面估计来缓解这些影响。通过适当结合 GPS、GLONASS 和 Galileo 的海平面估算，基于 SNR 的潮汐成分可以与潮汐计的潮汐成分在毫米级上达到一致。