RPS MetOcean Pty Ltd is a leading consultancy providing oceanographic and meteorological services in support of coastal and ocean engineering and environmental protection. The website contains listings and mappings of all publicly available metadata of studies conducted by RPS MetOcean.

Through established links with affiliated companies, RPS MetOcean has access to a powerful source of global information, and is world-renowned for its depth of experience and breadth of capability.

Our major focus is on physical oceanography, supplemented by strong resources in marine and local, land-based meteorology. We have more than 25 years experience in the collection, analysis, interpretation and application of metocean data.

Our core business activities are:

* Oceanographic Measurements;
* Metocean Monitoring Systems;
* Coastal and Ocean Engineering;
* Environmental Consultancy; and
* Data Management

all of which are described in the following pages. These activities, and the functions within them, are available individually, or as part of a package tailored to the client's needs.

The global metocean buoy market, valued at $957 million in 2025, is projected to experience steady growth, driven by increasing demand for accurate oceanographic and meteorological data across various sectors. This growth is fueled by several key factors. Firstly, the expanding offshore renewable energy industry, particularly wind and wave power, necessitates comprehensive metocean data for site assessment, installation, and operational monitoring. Secondly, the maritime transportation sector relies on reliable weather forecasts and sea state information for safe and efficient navigation, contributing significantly to market demand. Furthermore, advancements in buoy technology, including improved sensor capabilities, data transmission methods, and autonomous operation, enhance data quality and reduce operational costs, stimulating market expansion. Government initiatives promoting coastal zone management and marine environmental monitoring also play a crucial role in driving market growth. The market is segmented by application (military and civil use) and type (10-meter and 12-meter discus buoys), with the civil use segment expected to dominate due to the aforementioned factors. The competitive landscape includes established players like Fugro Oceanor and RPS Group, as well as specialized technology providers like NexSens Technology and AXYS Technologies. Geographic expansion, particularly in developing economies with burgeoning offshore infrastructure projects, presents significant growth opportunities. The market's projected Compound Annual Growth Rate (CAGR) of 4.2% from 2025 to 2033 indicates a consistent, albeit moderate, expansion. This relatively modest growth rate might be influenced by factors such as the high initial investment costs associated with buoy deployment and maintenance, as well as the potential impact of technological disruptions. However, ongoing research and development in areas such as satellite-based ocean monitoring and improved data analytics may counteract these restraints and accelerate future market growth. The key to success in this market lies in developing robust, reliable, and cost-effective buoy systems that meet the evolving needs of various industries and government agencies. Geographic regions like North America and Europe are currently major contributors to the market, while the Asia-Pacific region is expected to witness significant growth potential due to increasing investment in offshore energy and infrastructure projects.

The metocean buoy market, valued at $957 million in 2025, is projected to experience steady growth, driven by increasing demand for accurate oceanographic and meteorological data across various sectors. The 4.2% CAGR indicates a consistent expansion throughout the forecast period (2025-2033). Key drivers include the rising need for precise data in offshore energy development (wind, oil & gas), marine infrastructure construction and maintenance, and advancements in marine research and climate monitoring. Furthermore, the growing adoption of sophisticated sensor technologies and improved data analytics capabilities are contributing to market expansion. The market segmentation reveals a significant demand for both military and civil applications, with the 10-meter and 12-meter discus buoys representing prominent types, likely due to their varying capabilities and deployment scenarios. Competition is fierce, with numerous established players like Fugro Oceanor, RPS Group, and AXYS Technologies, alongside emerging companies, vying for market share. Regional analysis suggests robust growth across North America and Europe, driven by significant investments in marine infrastructure and research initiatives in these regions. While precise regional breakdowns are not available, a reasonable assumption based on global distribution of maritime activities suggests a higher market share for North America and Europe, followed by Asia-Pacific, with the remaining regions contributing relatively less. However, developing economies are likely to see increased adoption in the coming years. Potential restraints could include the high initial investment costs associated with deploying and maintaining metocean buoys, along with challenges related to data transmission and accessibility in remote locations. Continued growth in the metocean buoy market is expected to be fueled by several factors. The increasing focus on climate change research and understanding ocean dynamics will necessitate more sophisticated and widespread buoy deployment. Moreover, the expanding offshore renewable energy sector, particularly offshore wind farms, heavily relies on precise metocean data for site assessment, construction, and operational safety. Regulatory mandates and stringent safety requirements across various maritime sectors further solidify the demand for accurate and reliable metocean data, creating a sustained demand for buoys equipped with advanced sensors and communication capabilities. Technological advancements, such as the integration of IoT and AI capabilities into buoys, will further enhance data collection and processing, creating more efficient and valuable data streams for clients. The market's growth, however, will also depend on factors like effective cost management and efficient data accessibility solutions to overcome logistical and operational challenges.

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, Michael, Matthew Hall, Ericka Lozon, and Stein Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-89897. https://www.nrel.gov/docs/fy24osti/89897.pdf The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.

IEA Task 49: Integrated Design of Floating Wind Arrays

This dataset contains atmospheric and oceanographic data of 11 locations around the globe of future floating offshore wind farms.Each dataset was used to perform a metocean analysis for preliminary design, published in the Work Package 1 Report of IEA Task 49.

4COffshore ID Name Latitude [deg] Longitude [deg] Water Depth m Distance from shore [m] Country Dataset curated by

IT95
Hannibal

37.842

12.0722

-353

35

Italy
RSE

US0W
Humboldt

40.928

-124.708

-707

43.8

USA NREL

KR0R
Ulsan

35.449

129.949

-188

32

South Korea UOU

IE34
Moneypoint One

52.519

-10.276

-102

23.4

Ireland
GDG

UK6L
Havbredey

58.862

-5.54

-91

41.6

Scotland
DHI

JP06
Fukushima

37.311

141.251

-90

19.4

Japan
AIT

NO44
Utsira nord*

59.276

4.541

-273

42.4

Norway
4subsea / UiS,UiB*

USZ3
Gulf of Maine

43.25

-69.5

-148

138

USA
NREL

KR88
Geomundo**

34.039

126.901

-70

47

South Korea IAE**

FR87
Sud de la Bretagne II

47.325

-3.659

-94

30.7

France
UiB

NO66 Sørlige Nordsjø II***

56.78

4.92

-60

180

Norway 4subsea / UiS,UiB***

• Suplementary dataset published at: https://doi.org/10.5281/zenodo.10048048

** Dataset is confidential, for details of usage reach out to the contact person.

*** Suplementary dataset published at: https://doi.org/10.5281/zenodo.7057407

North Sea Wave Database (NSWD) The dataset contains each year of spectral metocean condition for Significant Wave Height (HSIGN) and wave energy period (TMM10), in meters and seconds. Each variable has a year timestap which the data corresponds too i.e 1980. The latitudes and longitudes of the dataset have resolution of 0.025 degrees at each direction. Latitude starting coordinate is 50 degrees and Longitude 0.

For more information on the process that developed the dataset, the methodogies followed, calibration, valdiation and sensitivity analysis, see:

Lavidas, G., & Polinder, H. (2019). North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments. Atmosphere, 10(9), https://doi.org/10.3390/atmos10090551

Lavidas, G., & Polinder, H. (2019). Wind effects in the parametrisation of physical characteristics for a nearshore wave model. Proceedings of the 13th European Wave and Tidal Energy Conference 1-6 September 2019, Naples, Italy.

The dataset was produced by Dr George Lavidas during the WAVe Resource for Electrical Production (WAVREP, which received funding from the European Union's Horizon 2020 research & innovation programme under the Marie Sklodowska-Curie grant agreement No 787344.

The dataset is accompanied by two publication that (i) present the calibration-validation and production (ii) analysis of the dataset.

The official CORDIS website is https://cordis.europa.eu/project/id/787344 A list of outcomes for the NSWD and the WAVREP project is found at the researcher's page: https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP

It can also be found at the official CORDIS website https://cordis.europa.eu/project/id/787344

Sharing and Access information Creative Commons Attribution (CC BY-SA). The Creative Commons Attribution license allows others remix, tweak, and build upon your work, as long as they credit you and license their new creations under the identical terms.

The metocean buoy market, valued at $957 million in 2025, is projected to experience steady growth, driven by increasing demand for accurate oceanographic data across diverse sectors. This growth is fueled by several key factors. Firstly, the expanding maritime industries, including shipping, offshore energy, and aquaculture, necessitate reliable metocean data for safe and efficient operations. Secondly, advancements in buoy technology, such as the incorporation of improved sensors and data transmission capabilities, are enhancing data quality and accessibility. Finally, governments and research institutions are increasingly investing in oceanographic monitoring infrastructure, further stimulating market expansion. The 4.2% CAGR suggests a consistent, albeit moderate, growth trajectory. Segmentation reveals a strong reliance on both military and civil applications, with 10-meter and 12-meter discus buoys being prominent types. Competition is substantial, with numerous players vying for market share; however, the market exhibits characteristics of moderate concentration with a blend of established players and emerging technology providers. Geographic distribution is likely diverse, with North America and Europe holding significant market share due to developed infrastructure and substantial maritime activity. However, the Asia-Pacific region's burgeoning maritime industries present substantial growth potential in the coming years. The market's growth trajectory is influenced by several restraining factors. High initial investment costs associated with buoy deployment and maintenance can be a barrier for some potential users, particularly smaller enterprises. Furthermore, the dependence on reliable communication networks for data transmission can be a constraint in remote areas with limited infrastructure. Technological advancements are addressing these limitations, but they also contribute to competitive pressure. The continuous innovation of sensor technology, buoy design, and data analytics tools necessitates ongoing investment for companies to remain competitive. Regulation and standardization of data formats and quality also play a significant role in shaping market dynamics. The forecast period (2025-2033) presents opportunities for companies to capitalize on the growing demand for metocean data, but navigating the challenges of high upfront investment and maintaining technological edge will remain crucial to success.

This item has been replaced by one which can be found at [https://doi.org/10.7488/ds/3448] ## The objective of the University of Edinburgh’s work package within the ReDAPT Tidal project was to design and conduct a data acquisition campaign to increase understanding of the flow conditions in the nearfield of a tidal energy converter (TEC) and to increase confidence in flow measurement and analysis methods. Three primary activities were originally identified with a fourth added during project review: Site mean-flow and turbulence characterisation; Acquisition, processing and dissemination of data for the validation of numerical models; Recommendations on metocean instrumentation and monitoring parameters; Provision of data in a format suitable for archival and access by the offshore renewables Industry. The data comprises primarily velocity measurements acquired between June 2011 and October 2014 at the European Marine Energy Centre (EMEC) tidal test site, Orkney, UK. Measurements were obtained by multiple variants of sensor operating on the principle of acoustic Doppler velocimetry including geometrically diverging and single-beam acoustic Doppler Profilers (ADP) or Acoustic Doppler Current Profilers (ADCP) as they are commonly refered to. Measurements were acquired from the seabed around the tidal turbine and from instrumentation platforms designed and operated by the University of Edinburgh which were installed on the tidal turbine in collaboration with the turbine developer. ReDAPT was led by Alstom and included the University of Edinburgh (UoE), DNV-GL Renewable Advisory, EDF Energy, E.ON, Tidal Generation Ltd., Plymouth Marine Laboratory and the European Marine Energy Centre (EMEC). The project centred around a commercial scale (1MW) tidal turbine developed by Alstom deployed at EMECs Tidal Test Site with the aim of producing a comprehensive suite of data on turbine operation, the flow field and the interaction between the two.

The global metocean buoy market, valued at $957 million in 2025, is projected to experience steady growth, driven by increasing demand for accurate and real-time oceanographic data across various sectors. The Compound Annual Growth Rate (CAGR) of 4.2% from 2025 to 2033 indicates a robust expansion, primarily fueled by the burgeoning offshore energy industry (wind, oil & gas), maritime safety and security initiatives, and growing scientific research activities focused on climate change and oceanographic understanding. The military segment continues to be a significant contributor, leveraging metocean buoys for surveillance, naval operations, and coastal defense. Technological advancements, such as the integration of advanced sensors and improved data transmission capabilities (e.g., satellite communication), are enhancing the functionality and reliability of these systems, further propelling market growth. However, high initial investment costs and the need for specialized maintenance can act as restraints, particularly for smaller operators. The market is segmented by application (military and civil) and type (10-meter and 12-meter discus buoys), with the 12-meter buoy segment potentially commanding a larger share due to its capacity for accommodating more sophisticated sensors and withstanding harsher weather conditions. Geographically, North America and Europe are expected to dominate the market, owing to significant investments in offshore infrastructure and a strong presence of key players, but the Asia-Pacific region is anticipated to exhibit considerable growth potential driven by substantial investments in offshore renewable energy projects and expanding coastal infrastructure development. The competitive landscape is characterized by a mix of established players and specialized technology providers. Companies like Fugro Oceanor, RPS Group, and MetOcean Telematics are leading the market with their comprehensive solutions and global reach. However, the entry of innovative companies with niche technologies is likely to increase competition. Future growth will depend on continuous technological improvements, the development of cost-effective solutions, and the increasing integration of metocean data into broader maritime and environmental monitoring systems. The market will likely witness a consolidation trend as larger players acquire smaller, specialized companies to expand their product portfolios and market reach. The ongoing development of autonomous and remotely operated buoys will further contribute to enhancing operational efficiency and reducing costs in the long term, making this technology accessible to a wider range of users.

The Gulf of Mexico Risk Analysis Database is comprehensive Esri geodatabase of vector layers, raster layers, and tables curated for risk analysis within the offshore Gulf of Mexico. Datasets include bathymetry, seafloor characteristics (channels, anomalies, faults, etc.), MetOcean data (wind speed, wave height, etc.), ocean current data, sediment data, and machine learning training regions used in NETL's Ocean & Geohazard Analysis (OGA) tool. This database serves as a compliment to the OGA tool by providing many of the datasets used in the design of the OGA tool, including regions used for machine learning. This database also serves as a valuable resource for risk analysis studies within the offshore Gulf of Mexico. This work was completed under the Advanced Offshore Research Portfolio, FWP Number: 1022476.

The global environmental data loggers market is experiencing steady growth, projected to reach a value of $1228.5 million in 2025, with a Compound Annual Growth Rate (CAGR) of 2.2% from 2025 to 2033. This growth is driven by increasing environmental monitoring needs across various sectors. The rising awareness of climate change and the urgent need for accurate environmental data to inform effective mitigation and adaptation strategies are key factors fueling market expansion. Furthermore, advancements in sensor technology, leading to smaller, more energy-efficient, and more accurate data loggers, are contributing significantly. The oil and gas industry relies heavily on these devices for pipeline monitoring and environmental impact assessments, while the defense sector utilizes them for surveillance and reconnaissance. Research institutions extensively employ these loggers for ecological studies and climate research. The market is segmented by application (Oil & Gas, Defense, Research, Others) and type (Solar Powered, Battery Powered), offering diverse solutions tailored to specific requirements. The increasing demand for real-time environmental monitoring in diverse applications and the deployment of sophisticated data analytics tools for better interpretation of collected data further bolster market expansion. The market's regional distribution demonstrates a strong presence in North America and Europe, driven by established environmental regulations and robust research infrastructure. However, developing economies in Asia-Pacific are witnessing significant growth potential due to increasing industrialization and rising environmental concerns. The adoption of sustainable energy sources, such as solar power, in data loggers is gaining traction, reducing operational costs and environmental impact. While the market faces constraints such as high initial investment costs and technological complexities associated with data analysis, the overall growth trajectory remains positive, spurred by the urgent need for comprehensive environmental monitoring and increasingly sophisticated technology solutions. The continued expansion of renewable energy, stricter environmental regulations globally, and technological advancements in sensor technology and data analytics are poised to drive further market growth over the forecast period. This comprehensive report offers an in-depth analysis of the global environmental data loggers market, projecting a market value exceeding $2 billion by 2028. It delves into key market segments, regional trends, technological advancements, and the competitive landscape, providing valuable insights for stakeholders across the industry. This report utilizes extensive market research and data analysis, including projected consumption values in the millions, to offer a detailed and actionable overview. High-search-volume keywords such as "environmental monitoring," "data acquisition," "IoT sensors," "wireless data loggers," and "oceanographic instrumentation" are strategically integrated throughout the report for enhanced search engine visibility.

Time series of metocean variables derived form WAMOS (marine radar) data collected during the Antarctic Circumnavigation Expedition (ACE, https://spi-ace-expedition.ch/), from December 2016 to March 2017.

Waves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.

By installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Data were collected during the Antarctic Circumnavigation Expedition, which took place from Dec. 2016 to Mar. 2017.

The dataset contains timeseries of relevant metocean variables divided in - Sea state and current parameters (PARA, MPAR) - Sea state and current parameters (PEAK, MPEK) - Ship course, position and speed (COURSE) - Wind speed and direction file (WIND)

Sea state and current parameters files (PARA, MPAR)

File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: 1) ‘PARA’ : spatial mean of the parameters (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. 2) ‘MPAR’ : temporal average parameters calculated using all data collected during the past dt=20 minutes of the time specified in the file. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters)

Time reference: CPU clock.

Values of missing parameters are set to -9, -9.0.

List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - Hs : Significant wave height (m). - Tp : Peak wave period (s). - Tm2 : Mean wave period (s). - Lp : Peak wave length (m). - MDir : Mean wave direction (deg). - PDir : Peak wave direction (deg). - TpS : First swell system - wave period (s). - PDS : First swell system - peak wave direction (deg). - lpS : First swell system - peak wave length (m). - TpW : Wind sea peak wave period (s). - PDW : Wind sea wave direction (deg). - lpW : Wind sea wave length (m). - Usp : Surface current speed (m/s). - Udir : Surface current direction (deg). - IQ : Quality index, ranging from 0 ('no problems detected') to 999 ('images cannot be analysed'). - NSPEC : Number of averaged spectra. - INDEX : Quality index threshold (OK: IQ<Index). - Hmax : Maximum wave height (m). - Tlim : Limit period to separate Swell/Wind Sea (s). - ELEVL : Error number. - CFG-Date : Date/time of last wamos.cfg change (DD-MM-YYYY HH.MI.SS).

Sea state and current parameters files (PEAK, MPEK):

File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: 1) ‘PEAK’ : spatial mean of the parameters (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. 2) ‘MPEK’ : temporal average parameters calculated using all data collected during the past dt=20 minutes of the time specified in the file. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters)

Time reference: CPU clock.

Values of missing parameters are set to -9, -9.0.

List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - Hs : Significant wave height (m). - Tp : Peak wave period (s). - PDir : Peak wave direction (deg). - Lp : Peak wave length (m). - HsW : Wind sea significant wave height (m). - TpW : Wind sea wave period (s). - PDW : Wind sea wave direction deg). - lpW : Wind sea wave length (m). - HSS1 : First swell system significant wave height (m). - Tps1 : First swell system: wave period (s). - PDs1 : First swell peak wave direction (deg). - lps1 : First swell peak wave length (m). - HSS2 : Second swell system significant wave height (m). - Tps2 : Second swell system: wave period (s). - PDs2 : Second swell peak wave direction (deg). - lps2 : Second swell peak wave length (m). - HSS3 : Third swell system significant wave height (m). - Tps3 : Third swell system: wave period (s). - PDs3 : Third swell peak wave direction (deg). - lps3 : Third swell peak wave length (m). - Us : Surface current speed (m/s). - Ud : Surface current direction (deg). - IQ : Quality index. - Tlim : Limit period to separate Swell/Wind Sea (s). - SPR : Mean wave spreading. - CSI : Cross sea index. - GAM : Enhancement factor of the jonswap spectrum. - NORI : from compass or GPS (0 = enable 1 = disable). - ELEVL : Error number - CFG-Date : Date/time of last wamos.cfg change (DD-MM-YYYYHH.MI.SS).

Ship course, position and speed file (COURSE):

File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: ‘COURSE’ : Input from NMEA systems. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters)

Time reference: CPU clock.

Values of missing parameters are set to -9, -9.0.

List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - LAT : Latitude (deg). - LONG : Longitude (deg). - GYROC : Ship gyro compass (deg). - GPS : GPS course (deg). - Shipsp : Ship speed (kn). - Depth : Water depth (m). - GPS-Speed : GPS-Speed (kn). - ASDPW : Internal parameter.

Wind speed and direction file (WIND):

File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: ‘WIND’ : Input from NMEA systems. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters)

Time reference: CPU clock.

Values of missing parameters are set to -9, -9.0.

List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - LAT : Latitude (deg). - LONG : Longitude (deg). - WIND SPEED : Wind speed (m/s). - DIR : Wind direction (coming from) (deg). - WIND SPEED10 : Wind speed at 10 meters height (m/s). - TRUE DIR : Wind direction relative to north (deg).

The global mooring data buoy market is experiencing robust growth, driven by increasing demand for accurate and real-time oceanographic data across diverse sectors. The market's expansion is fueled by the rising adoption of advanced technologies like solar-powered and battery-powered buoys, offering enhanced operational efficiency and longevity compared to traditional systems. Significant applications exist in both military and civilian contexts; military applications leverage the data for navigational safety, surveillance, and defense operations, while civilian applications encompass diverse uses such as environmental monitoring, offshore energy exploration, marine research, and aquaculture. This growing demand has led to a surge in technological advancements, including improvements in sensor capabilities, data transmission technologies, and buoy design, further propelling market expansion. The market is segmented by application (military and civil) and buoy type (solar-powered and battery-powered), offering tailored solutions for various user needs. Geographic expansion is also a key driver, with significant growth projected in regions such as Asia-Pacific and North America, driven by rising investments in infrastructure and ongoing research initiatives. The market is competitive, with numerous established and emerging players vying for market share through product innovation and strategic partnerships. Despite these positive trends, the market faces challenges. High initial investment costs associated with buoy deployment and maintenance can limit adoption, particularly among smaller companies or organizations with limited budgets. Additionally, technological complexities and the need for specialized expertise in data interpretation and analysis can pose barriers to entry. However, ongoing innovations in cost-effective designs and user-friendly data analysis tools are expected to mitigate these challenges in the coming years. The market’s overall trajectory shows significant growth potential, driven by continuous technological advancements and the increasing importance of real-time ocean data across multiple industries. The forecast period of 2025-2033 suggests a steady and sustained market expansion, further solidifying the mooring data buoy market as a vital component of modern oceanographic and maritime operations.

WAMOS (marine radar) data collected during the Antarctic Circumnavigation Expedition (ACE, https://spi-ace-expedition.ch/), from December 2016 to March 2017.

Waves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft.

There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.

By installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Dara were collected during the Antarctic Circmumnavigaion Expedition, which took place from Dec. 2016 to Mar. 2017. The instrumentation operated in any weather and visibility conditions, and at night, monitoring the ocean continuously over the entire Circumnavigation.

Records can support

1. the assessment of metocean conditions in the Southern Oceans; and

2. calibration and validation of wave and global circulation models.

Data - AAS_4434_ACE_WAMOS contains sea state conditions monitored continuously with a Wave and Surface Current Monitoring System (WaMoS II), a wave devise based on the marine X-Band radar (see Hessner, K. G., Nieto-Borge, J. C., and Bell, P. S., 2007, Nautical Radar Measurements in Europe: Applications of WaMoS II as a Sensor for Sea State, Current and Bathymetry. In V. Barale, and M. Gade, Sensing of the European Seas, pp. 435-446, Springer). Sea state consists of the directional wave energy spectrum, angular frequency and direction of propagation. Basic parameters such as the significant wave height (a representative measure of the average wave height), the dominant period, wavelength, mean wave direction, etc… were inferred from the wave spectrum. Surface current speed and the concurrent direction were also detected. Post processed data are available anytime the X-Band radar was operated in a range of 1.5NM; a full spectrum was generally obtained evert 20 minutes.

Data are subdivided in: - WaMoS II frequency spectrum (1-D spectra) - WaMoS II wave number spectrum (2-D spectra) - WaMoS II frequency direction spectrum (2-D spectra)

Data are quality controlled.

File informations

Path to the spectra: \RESULTS\YYYY\MM\DD\HH\ : Year, month, day, hour. space\ : spatial mean results. single\ : raw spectra. mean\ : time averaged files.

Header of the spectra: Additional information that might be needed for data analysis is stored in the headers. The output results generated using different WaMoS II software modules are separated by comment lines starting with ‘CC’. All headers are subdivided into: 1) Polar Header: including data acquisition parameters. 2) Car Header: including Cartesian transformation parameters. 3) Wave-Current Analysis Header: including wave and current analysis related parameters. There is a keyword of maximum 5 characters in each line of the header followed by some values and a comment, after the comment marker ‘CC’, describing the keyword. Values of missing parameters are set to -9, -9.0, -99.0, etc. depending on the data type. The 'end of header' keyword 'EOH', indicated the last line of the header section.

WaMoS II frequency spectrum (1-D spectra):

File Name: YYYY : Year. MM : Month. DD : Day. HH : Hour. MM : Minute. SS : Second. rigID : WaMoS II platform’s ID code (3 letters)

Suffix: ’*.D1S’ : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. ‘*.D1M’ : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.

Time reference: CPU clock.

Data Content: Frequency (f - Hz). Spectral energy (S(f) - m*m/Hz). Mean Wave Direction (MDIR(f) - deg), ���coming from’. Directional Spreading (SPR(f) - deg/Hz).

WaMoS II wave number spectrum (2-D spectra):

File Name: YYYY : Year. MM : Month. DD : Day. HH : Hour. MM : Minute. SS : Second. rigID : WaMoS II platform’s ID code (3 letters)

Suffix: ’*.D2S’ : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. ‘*.D2M’ : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.

Time reference: CPU clock.

Data Content: Spectral energy (S(kx,ky) - m*m/(Hz*rad)) as a function of wave number (kx and ky - rad/m).

Data related header information MATRIX: Size of Matrix. DKX: Spectral resolution in Kx direction (2*Pi/m). DKY: Spectral resolution in Ky direction (2*Pi/m).

WaMoS II frequency direction spectrum (2-D spectra):

File Name: YYYY : Year. MM : Month. DD : Day. HH : Hour. MM : Minute. SS : Second. rigID : WaMoS II platform’s ID code (3 letters)

Suffix: ‘*.FTH’ : spatial mean of the spectra (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. ’*.FTM’ : temporal average spectra calculated using all spectra collected during the past dt=30 minutes of the time specified in the file.

Time reference: CPU clock.

Data Content: Spectral energy (S(f,θ) - m*m/(Hz*rad)) as a function of frequency (f – Hz) and direction (θ - deg).

Data information Mf : number of frequency sampling points. Mth : number of direction sampling points. Data Matrix: Row 1 frequency sampling points, Column 1 direction sampling points.

The dataset download also includes a file, "Available_Measurements", which is a general calendar that provides the list (day and time) of available measurements.

'''DEFINITION'''

The CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019).

'''CONTEXT'''

The sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions. The Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 – 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011).

'''CMEMS KEY FINDINGS'''

In 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm “Gloria” (19/1 – 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 – 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 – 18/11) which also affected the Balearic islands and the Algerian coast and; storm Hervé (2/2 – 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 – 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018).

Note: The key findings will be updated annually in November, in line with OMI evolutions.

'''DOI (product):''' https://doi.org/10.48670/moi-00262

The global marine observation buoy market is experiencing robust growth, driven by increasing demand for real-time oceanographic data across various applications. The market, valued at approximately $800 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated $1.4 billion by 2033. This expansion is fueled by several key factors: the rising need for improved maritime safety and security, growing investments in offshore renewable energy projects (requiring extensive environmental monitoring), and the increasing focus on climate change research and oceanographic studies. The demand for accurate and reliable data from diverse marine environments is pushing the adoption of advanced sensor technologies and sophisticated data processing capabilities within marine observation buoys. Furthermore, technological advancements in areas like solar and battery power systems, data transmission, and buoy design are contributing to the market's growth. The civil use segment, driven by coastal management, environmental monitoring, and aquaculture, is anticipated to experience significant growth, while military applications will maintain a substantial market share due to the ongoing need for surveillance and defense operations. Segment-wise, solar-powered buoys are gaining traction due to their environmental friendliness and reduced operational costs compared to battery-powered systems. However, battery-powered buoys continue to hold a significant market share, particularly in remote locations with limited solar energy availability. Geographically, North America and Europe are currently the leading markets, owing to advanced technological infrastructure and considerable investments in marine research and development. However, the Asia-Pacific region is expected to demonstrate significant growth in the coming years due to rising coastal development activities and increasing government initiatives to support maritime infrastructure development. While challenges such as high initial investment costs and maintenance requirements may pose some constraints, the overall market outlook for marine observation buoys remains positive, driven by the continuous need for comprehensive marine data collection and analysis.

CE0819 Science at Sea Survey. Published by Marine Institute. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).IMEP Training Cruise in Galway Bay. Standard oceanographic research data collection to ascertain the condition of the ocean. MSFD characteristics. MSP scientific research. ICES CTD reporting. Galway Bay ecosystem metocean analysis....

In late summer 2013, the research expedition Oden Arctic Technology Research Cruise (OATRC 2013) took place in the waters northeast of Greenland onboard the Swedish icebreaker Oden.

The expedition was carried out by the Norwegian University of Science and Technology (NTNU) in collaboration with the Swedish Polar Research Secretariat. OATRC 2013 was a project associated with Sustainable Arctic Marine and Coastal Technology (SAMCoT), which is a centre for research-based innovation, initiated by the Research Council of Norway and hosted by NTNU. OATRC 2013 was a follow-up to last year’s expedition to this region.

Purpose:

The main scientific scope of the research expedition was: Collection of data necessary to build, calibrate and validate models for: floaters in ice, ice management operations, iceberg/sea ice drift, ice/met/ocean statistics, evaluation of new technologies and environmental research.

This data set contains meteorological, oceanographic and ship data collected during the expedition Oden Arctic Technology Research Cruise 2013 (OATRC 2013), which was an international research cruise using the icebreaker Oden in the Arctic Ocean. The expedition embarked from Svalbard, Norway on 19 Augusti and proceeded along the coast of Greenland from where the expedition returned to Svalbard on September 23, 2013.

Data include:

Meteorological variables: Air temperature, Humidity, Wind direction/speed, Atmospheric pressure, Photosynthetic Active Radiation (PAR).

Oceanographic variables: Sea water temperature, Conductivity, Salinity and Sound velocity.

Ship data: Position, Speed, Course, Water depth.

Quality Information:

Obviously erroneous data (e.g. negative air pressure) have been omitted. No other processing or quality check of the data has been undertaken. Users should be aware of this in further data handling and analysis.

MOANAPROJECT - Mangopare sensor deployments abstract=Moana Project-Mangopare sensor deployments acknowledgement=Data quality-control and processing provided by MetOcean Solutions, a Division of the Meteorological Service of New Zealand. Mangōpare sensor and deck unit provided by Zebra-Tech, Ltd, Nelson, New Zealand as part of the Moana Project. This work is a contribution to the Moana Project (www.moanaproject.org) funded by the New Zealand Ministry of Business Innovation and Employment, contract number METO1801. author=Dr. Mireya Montaño author_email=mireya.montano@metocean.co.nz cdm_data_type=Point Conventions=CF-1.10, FVON-0.1, COARDS, ACDD-1.3 coordinates=TIME LONGITUDE LATITUDE DEPTH data_centre=MetOcean Solutions, a Division of the Meteorological Service of New Zealand data_centre_email=info@metocean.co.nz disclaimer=By using the data that Meteorological Service of New Zealand Limited (MetService) makes available on this platform, you agree to this disclaimer. The data is supplied by third parties to MetService and is aggregated and anonymised by MetService. MetService does not independently check the data to ensure it is correct, accurate, complete, current, or suitable for use. The data is made available by MetService on as is and as available basis. You agree that any reliance you place on the data, including any analysis of, or decision you make based on, the data (or that analysis) is at your own risk. You agree that MetService has no responsibility or liability for the data (or for any error or omission in the data) or for how you use that data. To the maximum extent permitted by law, MetService disclaims all warranties, conditions, guarantees, and/or representations relating to the data and how you use that data. Easternmost_Easting=186.186102 featureType=Point file_version=Level 1 - Quality Controlled Data geospatial_lat_max=-32.983321 geospatial_lat_min=-73.09050500000001 geospatial_lat_units=degrees_north geospatial_lon_max=186.186102 geospatial_lon_min=162.962633 geospatial_lon_units=degrees_east geospatial_vertical_max=6398.305908562049 geospatial_vertical_min=-5.159617662256664 geospatial_vertical_positive=down geospatial_vertical_units=m history=Transformation of files ready to be published on THREDDS server. infoUrl=https://www.moanaproject.org/temperature-sensors institution=MOANA PROJECT instrument=Moana TD1000 instrument_battery=3.59 (V) instrument_calibration_date=05/11/2024 instrument_firmware=MOANA-3.02 keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=-32.983321 principal_investigator=Dr. Julie Jakoboski principal_investigator_email=julie.jakoboski@metocean.co.nz programme=Moana Project publication_date=25/03/2025 15:22 quality_control_date=2025-03-25T15:14:12 +0000 quality_control_log=qc_tests_applied: impossible_date, impossible_location, impossible_speed, timing_gap, global_range, remove_ref_location, spike, temp_drift, stationary_position_check, reset_code_check, check_timestamp_overflow, start_end_dist_check; qc_tests_failed: [] quality_control_source=https://github.com/metocean/moana-qc references=https://www.moanaproject.org/temperature-sensors sourceUrl=(local files) Southernmost_Northing=-73.09050500000001 standard_name_vocabulary=CF Standard Name Table v70 start_end_dist_m=70.89 time_coverage_end=2025-03-25T08:06:09Z time_coverage_start=2023-10-11T07:50:53Z toolbox_version=0.6.0 Westernmost_Easting=162.962633

The suitability of areas for offshore wind development in the waters off England, Wales and Northern Ireland is the subject of ongoing assessment by The Crown Estate (TCE). In light of: i. the pace of technological change in the offshore wind sector; ii. the potential for future leasing as a response to UK Net Zero; and, iii. TCE’s responsibility to sustainably maximise the value of the seabed it manages, Everoze has been engaged by TCE to develop an updated methodology for characterising Key Resource Areas reflecting the latest and anticipated future technological developments in the sector out to a deployment date of 2040. A Key Resource Area (KRA) defines areas of seabed suitable for offshore wind development based on technology availability over a given timeframe. It is not intended to capture other factors vital to identifying suitability for development (e.g. other seabed uses, environmental constraints, etc.). These additional sensitivities will be considered in successive stages through further analysis by TCE in due course that build upon the KRA identification. In the context of a maturing sector, the methodology for KRA identification has moved away from classifying areas of seabed as Favourable, Limited and Marginal – as has been the case in previous KRA reviews - and towards identifying Technology Groups which characterise technical solutions for a given set of physical site drivers. This data presents the spatial analysis outputs of the criteria defined in the report by Everoze (Characterisation of Key Resource Areas for Offshore Wind – A Report for The Crown Estate, October 2020) for floating offshore wind and should be used in conjunction with the associated report which provides the context and justification to these spatial outputs. The study was run to the waters off England, Wales and Northern Ireland.Below are the criteria used for each floating wind Technology Group defined:TG1 – Conventional Anchoring-Moderate Sea State – Depth 50-250m, Quaternary Thickness >20m, Hs50 < 14mTG2 – Conventional Anchoring-Onerous Sea State – D 50-250m, QT >20m, Hs50 >14mTG3 – Complex Anchoring-Moderate Sea State – D 50-250m, QT 5-20m – Hs50 <14mTG4 – Complex Anchoring-Onerous Sea State – D 50-250m, QT 5-20m, Hs50 >14mTG5 – Pile/Socket Anchoring-Moderate Sea State – D 50-250m, QT <5m, Hs50 <15mTG6 – Pile/Socket Anchoring-Onerous Sea State – D 50-250m, QT <5m, Hs50 <15mThere is a global 9m/s minimum windspeed adopted. Datasets used in the analysis:Quaternary Thickness – BGSWind Speed – Met Office 2015 UK Offshore Wind ResourceBathymetry – Oceanwise Marine DEM 1 arc sec, and GEBCO World Bathymetry (in areas not covered otherwise)Metocean – ABPmer Hs50This data has been prepared by The Crown Estate using the criteria provided by Everoze Partners Limited in the report ‘Characterisation of Key Resource Areas for Offshore Wind’, October 2020. The data is provided for information purposes only and no party may rely on the accuracy, completeness or fitness of its content for any particular purpose. The Crown Estate makes no representation, assurance, undertaking or warranty in respect of the analysis in the report and thus the associated spatial data outputs