ALBERT

Description: Inconsistency in vertical reference surfaces between various sea-level data sources delivers a significant barrier to synergizing different sea-level approaches, which allows the determination of a realistic state of dynamic topography. As a result, transferring datasets with different approaches to a common and stable reference surface with a fixed reference epoch is feasible. Hence, a high-resolution geoid plays the role of a key component to link different sources. This study develops a method to reanalyze hydrodynamic model-based (HDM) sea levels via geoid-referenced tide gauges (TG). Thus, the steps of transferring datasets to a common vertical reference (including the same coordinate system, permanent tide system, and reference epoch) are demonstrated. Therefore, a comparison study allows us to highlight discrepancies between data sources and approach a realistic dynamic topography. The method is tested on the Baltic Sea using a dense network of TG stations, a high-resolution HDM, and Sentinel-3A satellite altimetry (SA) data for the period of 2017‒2021.Results show that the mean dynamic topography of corrected HDM is better by a factor of about 1.5. Validation with the Sentinel-3A satellite along-track data confirms the corrected HDM data to be more accurate than the original HDM. The synergy of different sources also identified problematic locations of HDM, SA, and geoid data, along with unreliable TG. For instance, the method identified a possible geoid problem in the eastern Baltic, which may be due to a lack of gravity data. The results are promising and the method can be applied to other sea areas.

Description: Accurate determination of absolute dynamic topography (ADT) has been mostly under-utilized because of limited access to high resolution geoid models. However, quantification of ADT is essential for advancing in realistically understanding ocean dynamic (e.g., ocean current patterns, sea level trends etc.). Most importantly it allows conformity of a stable vertical reference datum to be used amongst nations. As a result, this study develops and explores a method that derives ADT by employing both a geodetic and oceanographic approach. The geodesy approach utilizes multi-satellite along track satellite altimetry (SA) data in conjunction with the NKG2015 geoid model (developed by Baltic Sea countries). For the oceanographic approach the vertical datum of HDMs are often undisclosed, thus a method is applied using geoid referred tide gauges (TG), that corrects the HDM. The study site is that of the entire Baltic Sea for the period 2017‒2019.The statistical results showed average discrepancies between SA and HDM within range of ±20 cm; RMSE between 6‒ 9 cm and a standard deviation between 2‒16 cm. The method employed different sources; thus it identified problems with SA, HDM, TG, and geoid. For instance, possible geoid problems were discovered in the eastern Baltic Sea whereas for SA the northern Baltic sea-ice may be problematic. The comparison also revealed that SA along-track data has potential to show more realistic variation of ADT compared to that of HDM (which tended to generate a smooth surface). Also, in most cases, the HDM tended to underestimate the ADT.

Description: Increasing demand for coastal and offshore marine applications calls for accurate forecast of instantaneous sea level, storm surges and better understanding of marine processes. Previously one of the major limitations in accomplishing this has been and incompatible vertical reference datum and access to a method that intelligently integrates and analyzes all the different sources. With respect to linking the different data sources the use a high-resolution geoid now allows the determination of dynamic topography which represents a more accurate term for sea level. Thus this study presents a proposed methodology, that uses machine learning strategies to forecast and better understand the marine dynamics.The methodology proposed shall utilize mathematical, statistical and machine learning strategies (e.g. neural networks and inter-technique solutions) along with various relevant data sources (e.g. marine geoid, tide gauges, hydrodynamic models, satellite altimetry etc.) to forecasts sea level in the absolute sense along with their uncertainties. The results is expected to: (i) forecast dynamic topography, storm surges up to 7 day ahead, (ii) identify and predict oceanographic patterns and processes (currents, eddies, etc.), and (ii) determine realistic under keel clearance. The developed method shall be performed in the study area of the Baltic Sea that has an intense activity of shipping and maritime activities.

Description: The modelling errors of marine geoid models may reach up to a few decimetres in the shorter wavelength spectrum due to gravity data void areas and/or inaccurate data. Various data acquisition methods (e.g., marine GNSS measurements or satellite altimetry) have the potential to provide sea surface heights more accurately. Similarly, hydrodynamic model data in conjunction with tide gauge readings allow the derivation of reliable dynamic topography. Geometrical marine geoid heights, independent of the usual gravity-based marine geoid models, can be obtained by removing the estimated dynamic topography from sea surface height measurements. This study exploits such geometry information to refine marine geoid models. A data assimilation approach was developed that iteratively combines sea surface height and dynamic topography datasets with an initial gravimetric geoid model. A case study is presented using profile-wise sea surface heights from shipborne GNSS campaigns and an airborne laser scanning survey for refining the EIGEN-6C4 global geopotential model in the Gulf of Finland, the Baltic Sea. Comparisons with a high-resolution regional marine geoid model, the development of which employed recently measured marine gravity data in the study area, reveal that the initial discrepancies of up to around two decimetres reduce to sub-decimetre. It is concluded that the developed iterative data assimilation approach can significantly improve the accuracy of marine geoid models, especially in regions where gravity data are of poor quality or unavailable.

Description: Understanding and predicting future sea level change is a necessity for coastal planning, engineering, and climate studies. One of the main challenges has been that most Absolute Sea level (ASL) sea level trends are based at the location of tide gauges (TG), which tends to be land bounded and the TG data in some location covers limited temporal time span. Satellite Altimetry (SA) however, provide data both in shoreline and offshore although with some limitations. The main challenge of SA data is to provide reliable sources which has higher consistency with TG data specially to the vicinity of coastline. As a result, this study examines the sea level trend derived by multi-mission SA data in conjunction with the TG observation that has at some locations more than 20 years over ten TG station along the Baltic Sea coast. To perform this sea level trends using all available along track SA (e.g. Envisat, SARAL, Cryosat-2 and Jason missions and Sentinels missions) data by Baltic+SEAL project are computed using various methods. The ASL trend computed with using SA is then compared with the TG stations observation.

Description: ETRS89 has become the common standard for harmonized horizontal coordinates in the European countries. In contrast, the national height networks are still determined by leveling with respect to different tide gauges and standards (height types, tide systems). The differences in the definitions cause height discrepancies up to a few decimeters along the borders. National geoid models are defined accordingly. Nowadays, GNSS-based height determination is widely used, which also increases the demand for geoid models as a height reference across borders. For large parts of the continent, the United European Leveling Network provides harmonized heights based on the standards of the EVRS. However, EUREF has yet to adopt a corresponding height reference surface officially. Thus, the EUREF Working Group “Unified European Height Reference” was formed by resolution in 2021 with the objective to enhance the usability of European heights for practical applications such as civil engineering, digital elevation models, etc. The main goal is to establish a European Height Reference Surface (EHRS) which is tailored to a consistent dataset of GNSS-leveling control points (EHRS_CP) referring to the latest ETRS89 and EVRS realizations. Furthermore, comprehensive information about the national integrated spatial reference systems, including heights and geoid models, shall be made available through the Information and Service System for European Coordinate Reference Systems (CRS-EU). The paper gives an overview of the rationale and current activities of the working group. Preliminary comparisons with selected gravimetric quasigeoid models provide an outlook on the increased accuracy of the new GNSS-leveling dataset.

Description: The accurate determination of dynamic topography (DT) is crucial for various applications, including oceanography (ocean circulation and sea-level rise studies), coastal management, engineering, and navigation. Whilst satellite altimetry (SA) is a vital source for determining both coastal and offshore sea level data, the SA data however may be limited due to its spatial and temporal resolution. This limitation suggests an opportunity to use machine learning approaches to fill these tempo-spatial gaps that may exist. Thus, a deep learning method is tested on the Sentinel 3 data for the entire Baltic Sea for the period 2017‒2021. To fill these gaps in the SA-based dynamic topography, a novel approach is employed through a multivariate deep neural network-based algorithm. Input parameters (such as winds, sea level pressure, significant wave height, etc.) were used and a conditional generative adversarial network (CGAN) was employed by stacking convolutional layers. The concept of the CGAN model comprises two parts: the generator model and the discriminator. The generator is trained to reconstruct a map of DT and mislead the discriminator, while the discriminator is trained simultaneously to differentiate whether the generated map is real or fake. As a result, generated DT is used to complete a grid surface of the SA data. The proposed method is evaluated using tide gauge records and a hydrodynamic model for the Baltic Sea. The method is shown to outperform traditional techniques such as optimum interpolation (IO) in terms of accuracy and computational efficiency.

Description: The Lagrangian transport of substances is an essential component in understanding the distribution of heat, salt, nutrients etc. in our marine areas As a result this study takes a deeper examination into the contributors of Lagrangian transport in the Gulf of Finland, Baltic Sea by using a synergy of satellite derived sea surface temperature (SST) obtained from EOS, Moderate Resolution Imaging Spectrometer, high resolution bathymetry data and in-situ (surface drifters, wave gauges, meteorological and hydrological measurements) data sources. The experiments of 38 in-situ surface drifters were deployed at different seasons for the period of 2011–2018, and were designed to capture surface drift in uppermost 2 m layer of the sea. The method examined the relationship of wind and waves on surface drift. Results revealed: (i) Ekman-type drift of surface layer was present in 68% of the occurrences and surface current speed can be expressed as1.5% of the wind speed; (ii) for wave height 〉0.5 m accounting for higher velocities and (iii) for about 7–14% of the occurrences the surface drift is governed by other processes (e.g. eddies, fronts, upwellings) than direct wind and wave impact. For instance, depending on the stratification and season and bathymetry, the presence of coastal upwellings were identified from satellite based SST, frequently occur during the summer months on time scale of 6‒12 days. As a result surface transport are influenced by a combinations of wind- and wave- during moderate and strong winds but on other occasions also underlying synoptic- and basin-scale circulation patterns.

Description: Sea level forecasting that refers to a realistic and accurate vertical datum has became an important component for a variety of disciplines, particularly in coastal and marine disaster management, engineering and navigation. This is encouraged by recent advances in Artificial Intelligence (AI), especially that of encoder-decoder algorithms e.g. Convolutional Neural Network/ Long short-term memory (CNN/LSTM) that considers the nonlinearity have been used successfully to spatio-temporal forecast marine parameters. On the other hand, geoid-referred sea level or dynamic topography (DT), has the potential to be a beneficial alternative term with increased interpretability when compared to sea level anomaly fields, resulting in better ocean circulation and ocean state estimates. Despite its importance, it has received little attention in the literature thus far. As a result, this study develops a method that forecasts dynamic topography using a CNN/LSTM model in the Baltic Sea for the period 2019‒2021, which is also validated using along track satellite altimetry sea level. The methodology employed utilized a multi-source data-fusion strategy with a multivariate-multistep-ahead (1day, 3d, 5d, 7d ahead) framework to generate a new sea level product. Meteorological parameters such as wind speed, sea level pressure, and sea surface temperature, sea surface salinity, and sea surface height also need to be incorporated. The 1day forecast results show a spatial RMSE accuracy within ±4 cm for most of the Baltic Sea. External validation with along-track satellite altimetry data demonstrate differences between forecasted and SA being within 5 cm. The methodology can be applied to other sea areas.

Description: What do the transport sector, planning for the offshore industry, sea level rise monitoring, and environmental and coastal protection have in common? For all these fields, sustainability is nowadays a key aspect, and they all rely on accurate positioning and navigation across borders and between land and sea. Today, this is primarily done employing satellite techniques, but users are largely unaware that this requires a harmonized geodetic infrastructure, i.e., data and services, to determine accurate coordinates and heights relative to a common reference system. Until now, heights on land and charted depths in the Baltic Sea region were heterogeneous and not directly comparable. For example, paper charts of different harbors had different zero levels between countries or even within the same country. Working with this kind of information required expert knowledge. The Baltic Sea Hydrographic Commission has therefore laid the foundation for a unified chart datum in the Baltic Sea, the BSCD2000. A substantial part of the work could be realized with co-funding from the EU in the project “Finalizing Surveys for the Baltic Motorways of the Sea (FAMOS)”. In this paper, the concept of BSCD2000 and its implications for the users are briefly outlined, and applications given the UN sustainability goals are demonstrated. Based on BSCD2000, seamless and user-friendly navigation using satellites will become available for the first time in the Baltic Sea. BCSD2000 hence provides the foundation for novel applications like adaptive ship routing, which will promote the safety and efficiency of ship transport.