1. Introduction

1.1. Background

— When an Adequate Sampling of the Oceans Would Benefit Marine Industries, Coastal Communities, and Science.

Many technologies have been developed to estimate sea states, traditionally applied on an individual basis; see Fig. 1.1. Schematically, the four main sources of wave data could be categorised as:

1. In-situ measurements from fixed or floating systems, which comprise moored and drifting wave-buoys, as well as capacitance wave probes and pressure gauges;

2. Remote sensing data from satellites and aircraft, using altimeters or synthetic aperture radars (SAR);

3. Spatiotemporal wave inference from X-band (or microwave) marine radars [1], which equip more and more ships nowadays, along with the new LIDAR technology [2] [3];

4. Data generated by third-generation ocean wind-wave spectral models.

Fig. 1.1 Overview of the most widely used technologies for sea state estimation. Adapted from Mounet [5].

In spite of the many available means for sea state estimation (SSE), the scarcity of in-situ data from vast areas of the world’s oceans is an ongoing problem [10]. It appears sensible to view the many different SSE methods as complementary and combinable, rather than competing with each other. Overall, there is a growing interest from the ocean engineering and science communities in leveraging the complementary abilities of individual platforms in multidisciplinary research analysing multiple types of wave observations in an integrated manner [11].

Ship-as-a-wave-buoy (SAWB), which could be considered as being part of the first category in the above list of SSE means, is a concept and class of SSE methods which have received a lot of interest in maritime research. The principle is rather simple at first sight: a vessel is considered as a wave-riding buoy, experiencing waves in the seaway, thus inducing motions and other responses of the platform. The so-called wave buoy analogy (WBA) suggests the idea that the vessel responses can reveal some valuable information about the waves that have induced those responses, just like the motions of a wave-riding buoy that can be processed to eventually provide a complete description of the sea state. Most vessels usually have various sensors installed on board to measure their responses. Those sensors help monitor the vessel’’s performance, stability, and structural strength. Many studies have demonstrated that such measurements can also be used to derive a cost-efficient and accurate estimate of the encountered wave conditions. Nielsen [6] made a comprehensive review of available WBA methods.

1.2. Vision and aims

— When Quantity Is a Quality of Its Own.

The idea of a network of wave sensors integrating in-situ observations from multiple vessels has emerged in the scientific literature, introduced, for instance, by Nielsen [7] [8] and Nielsen et al. [9], as a concept in which individual means for SSE are considered as complementary and used collaboratively. Fusing wave data from multiple, heterogeneous observation platforms has the potential to increase the accuracy of sea state estimates, by mitigating the uncertainties inherent to measurements from the individual platforms.

Considering the sheer number of ships in transit on the open sea, there is a true interest and prospect of using wave information derived from station-kept – as well as advancing – vessels in a network-based approach. The maritime industry evolves nowadays in an era of digitalisation, aimed at increasing safety and energy efficiency. A large portion of the world fleet of merchant ships has installed sensors to permanently monitor the vessel responses. A tremendous amount of data is being collected in ship operations and remains to be exploited for many different purposes. In this scenario, the WBA method shows high relevance. Combined with data from other observation platforms, accurate and cost-efficient ship-based observations are a vital input to a networked procedure to continuously map the wave systems around the globe with increased confidence [4].

A network-based approach facilitates collaboration between adjacent vessels, to bring more timely information to decision support systems and (possibly remote) operators. The technology for communication of data at sea is improving, enabling the sharing of information about the sea state within fleets of ships. Beyond conventional ships, this is also relevant for the operation and control of small and autonomous surface vehicles (ASVs), for which situational awareness with regard to the wave environment is crucial [4].

NetSSE was created by researchers to test and distribute engineering tools towards the development of network-based solutions for better-quality sea state estimates on both a local on-site position and at a regional scale. Here, “better-quality” refers to estimates with overall decreased uncertainty with respect to state-of-the-art methods, for instance, higher accuracy, precision, robustness, reliability, and availability. It was decided to have the implementations based on the Python language, which is an open-source, data science programming language widely used in the world nowadays.

References

[1]

Borge, J. C. N., Reichert, K., and Dittmer, J. Use of nautical radar as a wave monitoring instrument. Coastal Engineering 37, 3-4 (1999), 331–342.

[2]

Fu, T. C., Fullerton, A. M., Hackett, E. E., and Merrill, C. Shipboard measurement of ocean waves. In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering (2011), vol. 6, American Society of Mechanical Engineers, pp. 699–706.

[3]

Kabel, T., Georgakis, C. T., and Zeeberg, A. R. Mapping ocean waves using new LIDAR equipment. In Proceedings of the International Offshore and Polar Engineering Conference (2019), vol. 3, International Society of Offshore and Polar Engineers, pp. 2558–2564.

[4] (1,2)

Mounet, R. E. G. Sea state estimation based on measurements from multiple observation platforms. Ph.D. dissertation, Technical University of Denmark, 2023.

[5]

Mounet, R. E. G. PhD Defense - Sea state estimation based on measurements from multiple observation platforms. Presentation at the Technical University of Denmark, January 2024.

[6]

Nielsen, U. D. A concise account of techniques available for shipboard sea state estimation. Ocean Engineering 129 (2017), 352–362.

[7]

Nielsen, U. D. Sea state estimation based on measurements of wave-induced ship responses. Technical University of Denmark, Department of Mechanical Engineering, Kongens Lyngby, Denmark, December 2018. Doctoral dissertation for the degree of Doctor Technices.

[8]

Nielsen, U. D. Sea state estimation using simultaneous data from multiple observation platforms. In Proceedings of JASNAOE’s Autumn Conference 2018 (2018), The Japan Society of Naval Architects and Ocean Engineers, pp. 37–40.

[9]

Nielsen, U. D., Brodtkorb, A. H., and Sørensen, A. J. Sea state estimation using multiple ships simultaneously as sailing wave buoys. Applied Ocean Research 83 (2019), 65–76.

[10]

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Villas Bôas, A. B., Ardhuin, F., Ayet, A., Bourassa, M. A., Brandt, P., Chapron, B., Cornuelle, B. D., Farrar, J. T., Fewings, M. R., Fox-Kemper, B., Gille, S. T., Gommenginger, C., Heimbach, P., Hell, M. C., Li, Q., Mazloff, M. R., Merrifield, S. T., Mouche, A., Rio, M. H., Rodriguez, E., Shutler, J. D., Subramanian, A. C., Terrill, E. J., Tsamados, M., Ubelmann, C., and van Sebille, E. Integrated observations of global surface winds, currents, and waves: Requirements and challenges for the next decade. Frontiers in Marine Science 6 (2019), 425.