# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 7 Conclusions  
This contribution presented a methodology for deriving accurate SSH from initial untreated post-processed GNSS data. The methodology involves a joint validation of the results using a geoid model and realistic DT to reduce SSH to residual values. Since GNSS measurements should be low-pass filtered to eliminate the effect of sea state conditions, the approach can also enhance SSH data accuracy, by avoiding error propagation due to DT and geoid gradients, which in this study could have caused larger than 5 cm errors. Furthermore, a rigid system of four GNSS antennas was used to eliminate the vessel's high-frequency attitude changes from the measurement data. Such a system is also beneficial, as it can be validated internally. Accordingly, the results indicated a 4.1 cm height determination accuracy for the whole SSH dataset. Other data validation (e.g., examination of residual values and intersection analyses) results ranged from 1.8 cm to 5.5 cm, in terms of standard deviation; suggesting that SSH can be determined with an accuracy of around 5 cm using the shipborne GNSS method. As mentioned, the validation requires DT information. A new method was developed that combines the properties of TG data and an HDM. Tide gauge data allow estimating the spatiotemporal bias of an HDM; thus, linking it to used vertical datum (e.g., a novel geoid-based Baltic Sea Chart Datum 2000), whereby the qualities of spatiotemporal bias are used to constrain TG data errors. As a result, realistic DT relative to an equipotential surface can be obtained, which could be beneficial in oceanographic research for studying marine processes accurately (e.g., determination of mean DT relative to an equipotential surface for studying currents). According to the SSH residual value validations, the developed approach was demonstrated to surpass the one used by [15; 30], with an improvement in standard deviation estimate from 6.3 cm to 5.0 cm, and mean residual from \(-\)8.0 cm to \(-\)5.2 cm. Conceptualization, S.V., A.L. and A.E. ; methodology, S.V. ; software, S.V. ; validation, S.V. ; formal analysis, S.V. ; investigation, S.V. ; resources, S.V., A.L. and A.E. ; data curation, S.V. and A.L. ; writing--original draft preparation, S.V. ; writing--review and editing, S.V., A.L. and A.E. ; visualization, S.V. ; supervision, A.E. ; project administration, A.L. and A.E. ; funding acquisition, A.L. and A.E. All authors have read and agreed to the published version of the manuscript. The research was supported by the Estonian Research Council grant \"Development of an iterative approach for near-coast marine geoid modelling by using re-tracked satellite altimetry, in-situ and modelled data\", grant number PRG330, and by the Estonian University of Life Sciences baseline funding grant \"A 3D model of intraplate deformations combining remote sensing and in-situ measurements with an application to implement a semi-dynamic reference frame in Estonia\", grant number P200188 MIGX. Figure 21: Histograms of residuals (all campaigns combined): the impact of squat estimation function. Data Availability Statement: The used hourly TG data can be obtained from the webpages of the Latvian [46; 50], Swedish [47], Finnish [48; 51], and Russian [49] TG managing authorities. The employed NKG2016 LU VLM model is provided by the Lantmateriet (on behalf of the Nordic Geodetic Commission--NKG): [[https://www.lantmateriet.se/en/maps-and-geographic-information/gps-geodesi-och-swepos/reference-systems/posTGlacial-land-uplift/](https://www.lantmateriet.se/en/maps-and-geographic-information/gps-geodesi-och-swepos/reference-systems/posTGlacial-land-uplift/)]([https://www.lantmateriet.se/en/maps-and-geographic-information/gps-geodesi-och-swepos/reference-systems/posTGlacial-land-uplift/](https://www.lantmateriet.se/en/maps-and-geographic-information/gps-geodesi-och-swepos/reference-systems/posTGlacial-land-uplift/)) (accessed on 28 February 2021).