Senegal-Mauritania wave and hydrodynamic hindcast models now available

MetOcean Solutions recently completed the development of high-resolution wave and hydrodynamic hindcast models offshore Senegal and Mauritania, West Africa.

“Senegal and Mauritania coastal areas are influenced by oceanographic processes, including tides, coastal upwelling/downwelling, eddies, internal waves, and highly-energetic wave conditions,” says Senior Oceanographer Dr Séverin Thiébaut. “Our challenge was to ensure the models would adequately replicate those multi-scale processes and both ambient and extreme metocean conditions.”

The Simulating WAves Nearshore (SWAN) model was used to resolve the wave climate and the Regional Ocean Modeling System (ROMS) was applied to simulate the hydrodynamic circulation. The technique implemented is known as ‘dynamical downscaling’, using information from large scale global models to drive regional/nearshore models at much higher resolution. All models were carefully calibrated with measured data from several current meters and wave buoys that were made available.

The SWAN model simulates the growth, refraction and decay of each frequency-direction component of the complete sea state, providing a realistic description of the wave field as it changes in time and space.

“In order to reliably replicate the regional and nearshore wave climate, the SWAN nests were defined with increasing resolutions of 5 km, 1 km and 100 m,” explains Séverin. “This approach allows the model to resolve fine-scale features near the coast while still accounting for remote influences to the area from far-field generated swell.”

Full spectral boundaries were prescribe from the MetOcean Solutions’ global wave model to the 5-km SWAN domain. The latter was used to force the boundaries of the 1-km SWAN domain, which in turn was applied to the boundaries of the high-resolution 100-m SWAN domain (Figure 1). All model nests were simulated in series over 39 years (1979 to 2017).

Figure 1: Snapshot of modelled significant wave height from the 5-km resolution SWAN parent nest off the Senegal/Mauritania coasts, delimited by the outer rectangle on (a). Extents of the 1-km resolution child nest are represented by the outer and inner rectangles on (a) and (b), respectively. Extents of the 100-m resolution child nest are represented by the inner rectangle on (b).

Figure 1: Snapshot of modelled significant wave height from the 5-km resolution SWAN parent nest off the Senegal/Mauritania coasts, delimited by the outer rectangle on (a). Extents of the 1-km resolution child nest are represented by the outer and inner rectangles on (a) and (b), respectively. Extents of the 100-m resolution child nest are represented by the inner rectangle on (b).

This ROMS model is an open source state of the art ocean model which has been used widely in the scientific community and industry for a range of ocean basin, regional and coastal scales. ROMS has a curvilinear horizontal coordinate system and solves the hydrostatic, primitive equations subject to a free-surface condition. Its terrain-following vertical coordinate system results in accurate modelling of areas of variable bathymetry, allowing the vertical resolution to be inversely proportional to the local depth. Two ROMS nests were defined with horizontal resolutions of approximately 6 km and 2 km for the regional and local model grid domains, respectively, as shown in Figure 2.

Figure 2: ROMS (a) regional (6 km) and (b) local (2km) computational model grids. The red lines illustrate the transect corresponding to the vertical sigma grid structures provided in the following figure. Note the bathymetry is represented with distinct colorbars in (a) and (b).

Figure 2: ROMS (a) regional (6 km) and (b) local (2km) computational model grids. The red lines illustrate the transect corresponding to the vertical sigma grid structures provided in the following figure. Note the bathymetry is represented with distinct colorbars in (a) and (b).

The terrain-following grid configuration consisted of 30 and 23 vertical levels with increased resolution at surface and near-bottom to better represent the boundary layers (Figure 3). The model was produced over 25 years (1993 to 2017), delivering 3-dimensional current, water temperature and salinity and sea surface elevation data.

Figure 3: Representation of the 30 vertical sigma levels of the regional grid domain over a cross-shelf transect along the latitude of 16.064०N.

Figure 3: Representation of the 30 vertical sigma levels of the regional grid domain over a cross-shelf transect along the latitude of 16.064०N.

Hindcast datasets offer key baseline information for project scoping, offshore and coastal design, project planning and environmental impact assessments.

For further information about MetOcean Solutions hindcast datasets, please contact hindcast@metocean.co.nz.

Nearshore renewable wave energy assessment in Mexico

In July 2016, the international wave power company Eco Wave Power commissioned MetOcean Solutions to provide wave statistics for a nearshore site in Cuyutlan, Manzanillo, on the west coast of Mexico.  

Figure 1: Model depths (top) and snapshot of modelled significant wave height (bottom) for a 0.05 degree SWAN domain for 01 January 2006. Extensions of child nests are shown by the black rectangles. Mean wave direction is shown by the arrows..

Figure 1: Model depths (top) and snapshot of modelled significant wave height (bottom) for a 0.05 degree SWAN domain for 01 January 2006. Extensions of child nests are shown by the black rectangles. Mean wave direction is shown by the arrows..

“Our company required wave statistics to assess whether it was feasible to install a wave power plant at the location”, says Eco Wave Power project manager Guillermo Sherwell. “An overview of the wave conditions is essential for the planning of offshore installations - it allows us to assess operability and identify potential hazards. The data also helps document important environmental conditions that may require further attention.”  

Dr Séverin Thiébaut from MetOcean Solutions was in charge of the project, while Dr Rafael Guedes led the wave model implementations. 

“We set up a multi-nest wave hindcast model to replicate the wave climate at the nearshore site,” explains Séverin. “We modelled the 2005-2014 period so that we could reproduce the ambient wave climate and reliably estimate the most extreme wave conditions that can occur in a 30-year period."

In consultation with the client, the team picked three nearshore sites representative of the proposed location of the wave power plant in water depths of 4, 6 and 8 m. Annual, seasonal and monthly wave analyses were carried out for these sites to extract ambient and extreme wave statistics to assess the design, workability and efficiency of the proposed wave power plant. . 

The Simulating WAves Nearshore (SWAN) model was used for the work. A 4-level nesting approach was applied to downscale wave spectra from a global model to the shallow nearshore locations of interest. Wind fields for the model were derived from the Climate Forecast System Reanalysis (CFSR), and tidal constituents from the Oregon State University Tidal Inverse Solution (OTIS). The site is prone to tropical cyclones, and a cyclone mask was used to remove tropical cyclone signatures from the hindcast metocean data.

The model outputs were used to calculate wave power, the rate at which energy is being transmitted. Fatigue analysis was assessed by estimating the total number of individual waves of varying height and period.

Figure 2: Density plot of the total significant wave height (Hs) vs the peak wave period (Tp) at one of the sites. The plot provides a visual representation of the total number of 3-hourly hindcast data (10 years) per Hs-Tp bin, normalised by the bin sizes to obtain a unit of m/s.

Figure 2: Density plot of the total significant wave height (Hs) vs the peak wave period (Tp) at one of the sites. The plot provides a visual representation of the total number of 3-hourly hindcast data (10 years) per Hs-Tp bin, normalised by the bin sizes to obtain a unit of m/s.

“We produced regional summary maps of the conditions,” explains Séverin. “These showed the spatial distribution of variables such as mean significant wave height for the total, swell and sea components, mean peak wave period, mean wave direction and wavelength.” (Illustrated in Figure 1). Joint probability occurrences of variables such as significant wave height and peak period were also included (as illustrated in Figure 2).

Figure 3: Annual wind rose plot for one of the sites. Sectors indicate the direction from which the wind is coming.

Figure 3: Annual wind rose plot for one of the sites. Sectors indicate the direction from which the wind is coming.

Wind statistics were also generated, including monthly and annual wind speed exceedance probabilities, joint probabilities of wind speed and direction and corresponding wind roses (illustrated in Figure 3). 

“Values such as the 99th percentile non-exceedance significant wave height (Hs) level is often used to assess the wave climate and structure design for energetic events. This denotes the significant wave height which is not exceeded for 99% of the time.” states Séverin. “Similarly useful are extreme metocean statistics like the return period values for wind and wave, i.e. the likely maximum that can occur within a specific extended duration.”

“We are very happy with the quality of the work,” states Guillermo Sherwell from Eco Wave Power. 
 

MetOceanView - making the offshore oil & gas industry safer and more efficient

Worldwide, a variety of offshore oil and gas companies use historical data from MetOceanView to understand the environment they work in. Packaged up in the site’s ‘hindcast app’, historical wind, wave and current data is summarised in an easy-to-access format that users can download and integrate into their operational planning.

“The offshore oil and gas industry needs access to reliable site data,” explains Senior Oceanographer Dr Rafael Guedes. “Nowadays, many data sources are available, and the industry needs a robust web platform where they can easily access, browse and download time-series from some of the best hindcast data sources available around the globe. To meet their needs, we set up a hindcast downloading app within our MetOceanView web service.”

Reliable data

The hindcast app provides access to an extensive compilation of data, going as far back as 1958 for some of the datasets.

“We host some of the most reliable global ocean and atmospheric reanalysis data sources in the world,” adds Dr Guedes. “These include NOAA’s CFSR V1 and V2, Hycom from the National Ocean Partnership Program (NOPP) and JRA-55 from the Japanese Meteorological Agency (JMA). In addition, we also provide MetOcean Solutions’ in-house WAVEWATCH III wave hindcast datasets.”

Gaining efficiency

Dealing with huge datasets is not easy, and to set up a useable service, practical obstacles had to be overcome.

“One common problem when accessing historical data is that the volume of data in gridded datasets can be very large,” says Dr Guedes. “For example, a subset of the HYCOM dataset we host comprises about 2.6 trillion individual data points for each variable. Reading long-term time-series from such enormous datasets can be very slow. To make a user-friendly service, we came up with an efficient process to speed up the reading, so that datasets that used to take more than two hours to download could be read in just five minutes.”

One-stop-shop for ocean data

The service works by hosting a range of reference datasets in our servers, giving the client access to unlimited data from all available global datasets as well as MetOcean Solutions’ wave and current model outputs.

“We provide a user-friendly map-based web interface where the user can inspect the different data sources and request time-series for any available grid location,” adds Dr Guedes. “The results are generated without human intervention and delivered within minutes in standard file formats, a link to which is sent directly to the user’s email.”

Data for safer operations

The hindcast app gives access to historical ocean temperature, current, elevation and wave data, as well as atmospheric parameters such as wind, temperature and precipitation.

“Accurate wave, wind and current information is essential for anyone operating offshore,” explains Dr Guedes. “Oil and gas companies use the hindcast data for a range of purposes, including determining the potential operational conditions for offshore locations and ensuring safety at sea.”

The hindcast data subscription service allows customers to browse map views of global datasets with descriptions of the variables that can be downloaded. Grid points show the exact location of each data point. Time-series are provided in either CF-compliant netCDF files or a format specially requested by the client. Wave hindcast data can be downloaded as time-series of spectral parameters or as two-dimensional, frequency-direction wave spectra. This allows a comprehensive description of the modelled wave field over the entire globe. For example, the spectra shed light on the multiple wave systems influencing wave conditions at a certain location and given time.

Making our clients’ life easier

This is the second year of delivering online historical metocean data services to Shell.  “We’ve found the global hindcast portal very valuable,” states Octavio Sequeiros, Shell Metocean Engineer. “Previously, to obtain data for a given location we had to create a specific purchase order for each request. Now, unlimited high quality historical data for any location are just a mouse-click away.

“The fast data download has made our planning easier, more efficient and cost-effective” continues Mr. Sequeiros. “The app is easy to use, and because it provides map layers along with the data grid locations, we can inspect areas and do a few queries before we decide to download time-series. Many summary statistics are shown in tile format, which means we can get a sense of the data before committing to downloads. We mostly use the time-series of wave parameters and wave spectra. On these, we run our own statistics in-house to obtain operational and design criteria for preliminary studies of new potential sites for oil and gas exploration. Our goal is to get workability predictions using accurate modelling.”

High level statistics from each of the datasets hosted by MetOcean Solutions is available via our MetOceanView interface.

Sea surface temperature snapshot from CFS dataset.

Sea surface temperature snapshot from CFS dataset.

Sea ice area snapshot from CFS dataset.

Sea ice area snapshot from CFS dataset.

Eastward wind snapshot from CFS dataset.

Eastward wind snapshot from CFS dataset.

Sea surface wave significant height snapshot from MetOcean Solutions’ dataset.

Sea surface wave significant height snapshot from MetOcean Solutions’ dataset.

Kaikoura wave model data now available

Hindcast wave model data are now available for the Kaikoura region. The data, which includes two high-resolution nested domains around the Kaikoura Peninsula and Clarence (regions where active coastal construction is taking place) were generated by dynamically downscaling waves from MetOcean Solutions’ global wave model using a series of Simulating WAves Nearshore (SWAN) model nests. 

Snapshot of significant wave height (Hs) and peak wave directions for the SWAN domains defined for Kaikoura. Inserts show the Clarence (right) and Kaikoura Peninsula (bottom) nests.   

Snapshot of significant wave height (Hs) and peak wave directions for the SWAN domains defined for Kaikoura. Inserts show the Clarence (right) and Kaikoura Peninsula (bottom) nests. 
 

“We prioritised the model runs to ensure that suitable time-series and boundary data are available for the Kaikoura rebuild effort,” explains Project Manager Dr Brett Beamsley. “The model domains are representative of the pre-earthquake bathymetry, but we don’t expect wave characteristics at depths exceeding ~30m to be significantly different between before the earthquake and now, because depth changes in deeper water are not expected to significantly influence wave propagation.” 

“Much of the rail and SH1 roading network north of of Kaikoura historically went very close to the sea and as a result were often closed due to waves washing over the narrow foreshore. Design tolerances for reconstruction of these networks will require an understanding of the likely impacts of large waves, including storm return periods and maximum expected wave heights. In the absence of measured data, this understanding can only be achieved through long period hindcasts.

“Additionally, these hindcast datasets can be used for boundary conditions for specific high-resolution wave models employed to understand implications of the new harbour (which is expected to be completed by mid year) or breakwaters, including wave energy penetration, overtopping and infragravity waves.”

For further information on the data available, please contact b.beamsley@metocean.co.nz

An operational oceanographic forecast / hindcast model for Shanghai, China

MetOcean Solutions recently completed the development of operational high-resolution wave and hydrodynamic models for the Yangtze River mouth and coastal areas off Shanghai. 

The work combined cutting-edge science within our agile operating system to set up wave and current models for Hangzhou Bay, a region within the East China Sea which is partially enclosed by the Ryukyu chain of islands. 

“It is a very tricky area to model,” notes Senior Oceanographer Dr Rafael Guedes. “The region is characterised by a wide, shallow and highly irregular shelf with many small islands and underwater reefs. Accurate bathymetry for the area is limited. The site is strongly influenced by the phenomenal seasonal discharge from the Yangtze River, which is one of the largest rivers in the world, and is also subject to strong tidal currents.” 

 Bathymetry of the East China Sea. Red dots show the locations of measured data used to validate the models.

 Bathymetry of the East China Sea. Red dots show the locations of measured data used to validate the models.

 
Progressive downscaling of outputs from MetOcean Solutions’  global wave model WAVEWATCH III  using two SWAN nests. 

Progressive downscaling of outputs from MetOcean Solutions’ global wave model WAVEWATCH III using two SWAN nests. 

Snapshot of surface salinity from the ROMS model. Blue denotes low salinities; red high.

Snapshot of surface salinity from the ROMS model. Blue denotes low salinities; red high.

“In order to model the location well, we had to capture the meteorological events occurring within the East China Sea as well as the swell generated beyond the Ryukyu Islands which propagates into the bay. Frequent typhoons ravage the area, and these are always hard to resolve well. All in all, the area displays a challenging combination of highly variable bathymetry, strong temperature and salinity differences and complex mixing processes.”

The SWAN (Simulating WAves Nearshore) model was used to resolve the wave climate and the Regional Ocean Modeling System (ROMS) was applied to simulate the circulation. 

“To model the area we used a technique known as ‘dynamical downscaling’,” explains Rafael. “This process uses information from large scale global models to drive regional models at much higher resolution. The technique allows us to resolve fine-scale features near the coast while still accounting for remote influences to the area from long-generated swell or meso-scale currents.”

Quantile-quantile plot comparing measured and modelled significant wave height (Hs) for wave hindcast using (black) existing  CFSR  wind fields and (red) adjusted wind fields to correct for observed wind bias.

Quantile-quantile plot comparing measured and modelled significant wave height (Hs) for wave hindcast using (black) existing CFSR wind fields and (red) adjusted wind fields to correct for observed wind bias.

“High-quality input data sources are critical to running wave and hydrodynamic models in such complex settings,” continues Rafael. “We found persistent wind speed bias near the bay in the global reanalysis data source that we used to calibrate the high resolution models. Correcting this bias before running the wave model significantly improved model results just offshore of the bay as shown in the comparison of measured and modelled significant wave height.

The area has heavy shipping traffic, and the operational system outputs, including 7-day forecasts of site-specific waves, winds and currents, are now available to marine users. Please contact us and we will connect you with our partner agency in China.

Historical ocean weather data statistics freely available

Historical data such as 100 year return period value (RPV) wave heights is important baseline information for anyone operating in the ocean.

Historical data such as 100 year return period value (RPV) wave heights is important baseline information for anyone operating in the ocean.

MetOcean Solutions recently provided free access to all our hindcast data statistics from around the world. 

"Anyone with internet access can now view and download historical weather statistics from a range of global locations," states Dr Rafael Guedes, Manager of Hindcasts. "In total we have statistics available from more than 380,000 datapoints around the world, a number which is constantly increasing as we run our models for new locations."

MetOcean Solutions’ hindcast data provide high quality marine weather information generated in-house by a team of expert scientists using state-of-the-art atmospheric and oceanographic models. The hindcast statistics can be accessed through the MetOceanView platform with no login required. 

"Our historical data archives reach back to 1979," adds Rafael. "The information available includes wind and wave distribution statistics, roses, joint probability tables and extreme value analysis data."

Suitable for environmental investigations and climate analysis, the datasets offer key baseline information for project scoping, offshore and coastal design, project planning and environmental impact assessment.

"We display tiles with gridded statistics from selected global datasets, including gridded mean wave height, period and direction for total swell and wind-sea wave components, percentiles and extreme value analysis of significant wave height and wind speed, vector-average currents, as well as mean air and sea-surface temperature."

Click here for free access to the more than 380,000 hindcast data locations worldwide. 

For a list of full historical datasets available, click here.

Fast delivery of complex historical data set

Snapshot of modeled depth-averaged current flow in and around the Irish Sea. The red dot indicates the location where model currents were validated. 

Snapshot of modeled depth-averaged current flow in and around the Irish Sea. The red dot indicates the location where model currents were validated. 

When a client urgently needs a complex historical data set for a tricky location, MetOcean Solutions rises to the challenge. 

Earlier this year, a client was planning to install a submarine cable in the Irish Sea. Ravaged by frequent storms and energetic tidal currents, the Irish Sea is a challenging location for offshore work. In order to allow the specification of robust design criteria, the client required coupled wave and current long term hindcast data. 

Due to the project urgency the client required both modelling outputs and extreme event analysis delivered within a five week timeframe. Given the importance of the work, full validation metrics on the data were required as well.  

Putting our can-do attitude to the test, the Hindcast Team managed to find, obtain and quality check in situ measurements of wave heights, currents and water elevations, allowing the successful calibration of wave and current models. The group ran a 20 year current hindcast, after which a 20 year wave hindcast was specified using the currents as one of the forcing fields. After a concerted effort the design report was finished within the five week timeframe specified by the client. 

The fast delivery of comprehensive historical data sets for complex locations is par for the course for the active Hindcast Team. The expert scientists in the group have comprehensive experience with the caveats of modelling and the creation and validation of historical data sets, which allows the anticipation and smoothing out of any problems that arise. 

"We have spent a lot of time on architecting and developing scripts, systems and software that allow us to focus on the science and leaving the heavy lifting automated," states Dr Rafael Soutelino. "We essentially submit batches of calibration experiments all at once, which means we get results back fast, freeing us up to make scientific decisions and submit more experiments as necessary to refine the process. Because most of the repetitive work is abstracted, we tend to keep focussed on the high-level ideas that are relevant to the outcome."

Left: QUANTILE-QUANTILE PLOT OF CURRENT SPEED AT 23.7 M BELOW SEA LEVEL AT THE VALIDATION LOCATION. RIGHT: TAYLOR DIAGRAM OF CURRENT SPEED AT 23.7 M  OF DEPTH. THE COMPARISON IS FOR THE PERIOD 23/06/2006 - 24/07/2006.

Left: QUANTILE-QUANTILE PLOT OF CURRENT SPEED AT 23.7 M BELOW SEA LEVEL AT THE VALIDATION LOCATION. RIGHT: TAYLOR DIAGRAM OF CURRENT SPEED AT 23.7 M  OF DEPTH. THE COMPARISON IS FOR THE PERIOD 23/06/2006 - 24/07/2006.

The team also benefits from the smart use of compute hardware enabled by MetOcean Solutions' IT infrastructure and code, which sends alerts to the team members in the event of system failure, and allows the re-establishment of a long-term hindcast from a smartphone or from anywhere with internet connection.  

"Our workplace policy of flexible hours also helps on these short turnaround projects," adds Rafael. "We tend to press on hard with a project like this until we see that we're winning. Once things are running to schedule, we then take time off to breathe." 

The data now joins MetOcean solutions library of hindcast datasets. For information on how to access these data, please contact us at enquiries@metocean.co.nz