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1 Waveclimate.com Information Sheet Error! Reference source not found. Page 1 of 1

2 A. About BMT BMT ARGOSS The BMT Group is an international design, engineering risk management consultancy, working principally in the energy environment, transport defence sectors. With locations in all of the markets we serve, ours is an active network that sees us sharing skills knowledge, combining disciplines building international teams to create integrated answers to the questions of our national international customers. Operating as an Employee Benefit Trust, BMT invests in its people the development of the company to future service customer requirements in a technology driven environment. For more information: BMT ARGOSS is a BMT Group Ltd operating company providing metocean nautical consultancy, systems services. Trough combining our expertise in the field of numerical (metocean) modelling, meteorology & weather forecasting, remote sensing data, vessel response behaviour of operating moored ships seabed dynamics BMT ARGOSS is able to provide a wide range of services aimed at reducing the risks involved with design operations. Operations support services: BMT ARGOSS maintains a 24/7/365 weather operations support centre from where meteorological oceanographic support is provided to support on-going operations taking place offshore near shore. Forecasts are produced by experienced marine meteorologist that use dedicated metocean models that are operated in-house. Our primary markets encompass offshore energy, marine transport ports terminals. In addition BMT ARGOSS also caters on shore offshore emergency response organisations with valuable tools that make insightful the effect of the environment on object or within an area during an emergency response situation. App. Fig. A Mission statement Our clients are served by a team of specialists that include: oceanographers, meteorologists, naval architects, mathematicians. BMT ARGOSS maintains offices in: Australia (Perth) Indonesia (Jakarta) Kazakhstan (Atyrau) Malaysia (Kuala Lumpur) The Netherls (Amersfoort Marknesse) United Kingdom (Aberdeen Fareham) The company was formed in 2008 through the merger of ARGOSS B.V. with several BMT companies providing maritime marine environmental expertise. The below services, consultancies (software) systems are a selection the solutions that BMT ARGOSS can provide to help authorities, operators contractors to optimise their operational efficiency /or design. Consultancy projects routinely carried out by our staff include: Metocean information to support operations planning Metocean information to support (engineering) design Port, mooring (inl) waterway layout design (feasibility) studies Seabed dynamics studies for subsea infrastructure Casualty / delay investigations disputes Software solutions that are provided by BMT ARGOSS either on a licence or services basis include: BMT SARIS, a BMT software solution that supports marine Search And Rescue operators by providing advice about the search area the strategy along which the area can best be investigated. BMT Rembrt, a portable real fast time vessel simulator with 6 degrees of freedom that can be used for vessel familiarisation, design testing operational simulation/training. When needed we develop (client exclusive) innovative solutions that build on multiple disciplines of expertise. For more information:

3 B. About Waveclimate.com Waveclimate.com Offshore Waveclimate.com is a web based portal developed by BMT ARGOSS to provide offshore operators with quick easy access to wind wave statistics data. The portal provides instant access to wind wave statistics is designed to support exploration studies to assess operability for marine sites globally. The information on waveclimate.com is based on underlying databases comprising of validated ( where needed calibrated) long-term global regional model hindcasts quality controlled satellite observations. A detailed overview of the wind wave modelling capability present within BMT ARGOSS is provided in Appendix Overview of Wave Modelling at BMT ARGOSS. Overview of model grids available on waveclimate.com Model Grid Grid Resolution Wind forcing Period covered Global 01 00' x 01 15' NCEP Mediterranean 00 15' x 00 15' ECMWF The data on used on waveclimate.com is based on spectral wave data, this allows for the partitioning of the total sea state into wind sea swell components. Waveclimate.com Nearshore In order to also provide information for coastal locations a nearshore wave transformation algorithm is implemented in waveclimate.com. The preferred means of wave transformation in waveclimate.com is based on the principles of wave ray tracing. This method is also widely used in consultancy applications for regions where coastal bathymetric features are less complex. Further information on this model is provided in or can be provided in the Appendix Wave Propagation, Wave Ray Tracing (SWRT). Offshore wave conditions are translated to the nearshore site of interest, nearshore wind data are copied from the nearest offshore model point.. Output Through waveclimate.com a wide variety of offshore wind wave nearshore wave statistics can be downloaded. On many occasions statistics can be copied to other applications for further processing. Some examples of output are provided below. Black Sea 00 15' x 00 15' ECMWF Caspian Sea 00 15' x 00 15' ECMWF Red Sea 00 15' x 00 15' ECMWF Persian Gulf 00 15' x 00 15' ECMWF Offshore location selection screen waveclimate.com Via data selection, analysis display options the user can retrieve a wide variety of ambient climate operability statistics wind wave data for particular months of interest. Typically data on waveclimate.com is updated periodically to include new years of data. Parameters for which information is available are: Wind speed & direction Significant wave height Principle wave direction Mean, zero-crossing peak wave period Histogram probability of exceedance tables - 2 -

4 2D scatter distribution table for Hs vs. principle wave direction In addition to the examples provided it is also possible to generate 3D tables showing for instance wave height vs. period vs. wave direction. Tables can be generated for all year conditions but also for specific months or combined months of interest (seasons). Wave rose Monthly distribution table of wave height Waveclimate.com Persistence Persistence Output A weather window is a continuous time interval of suitable weather wave conditions. On waveclimate.com one can, from our historical databases determine the persistence of weather windows of given durations defined by set operational limits. A key benefit of the persistence functionality offered is the visibility of inter annual variability i.e. the difference between good bad years, invisible in ambient climate statistics over multiple years. For each month, the persistence analysis results in either the: Fraction of time covered by suitable weather windows Number of weather windows of adequate length Waiting time until the next suitable weather window The below figures show the results for the following criteria for the study location: The window endures for at least 12 hours Within each window the following 3 conditions are met wave height is below 1.00m peak wave period is below 6.00s wind speed is below 10.00m/s Persistence table showing per year per month the fraction of time covered by a weather window. Red cells indicate the highest fractions light green the lowest. Seasonality plot showing mean Hs values, 90% exceedance curves maximum minimum monthly mean values from within the database

5 Plot of mean inter-annual variation per month of the fraction of time covered by suitable weather windows Service Availability Pricing The waveclimate.com service is available either through an annual subscription or as a pre-paid (pay per use) system that is based on vouchers (credits). Annual Subscriptions are available for Waveclimate.com Offshore alone or in combination with Waveclimate.com Nearshore / or Waveclimate.com Persistence. An annual subscription is more cost effective when frequent use is anticipated. An account for waveclimate.com based on vouchers is more suitable for clients who require metocean information less frequent. Annual flat fee subscriptions start from EURO 8250,- per year for offshore wind wave statistics. The additional price for the near shore wave propagation functionality is EURO 4250,- per year. In the voucher system a client buys a set of vouchers on-line. These vouchers are sold in quantities of 10 are by default priced at EURO 40,- per voucher. As information is retrieved from the portal the adequate amount of vouchers are subtracted from the account. Waveclimate.com Data Downloads To allow the user to carry out further analyses in house the service also allows for the retrieval of the time series data that underlying the model based statistics. Parameters provided for wind the following parameters for the total sea state its wind sea swell components include: Wind speed & direction Significant wave height Zero-crossing wave period Peak wave period Principle wave direction We note that time series downloads are by default not included in annual subscriptions. Final Notes The models data on waveclimate.com have all been extensively validated where needed calibrated for ambient climate conditions using satellite observation ( where available) in-situ observations. A detailed report indicating the quality of the underlying data used on waveclimate.com can be found at: /docs/service_overview validation.pdf When needed our experts can apply additional (more localized) data validation, calibration analyses procedures on a consultancy basis. As waveclimate.com s automated calibration focuses on the removal of the systematic error for the ambient climate, its data should not be used for design applications.should information be required for design applications where more energetic conditions are more relevant, then we recommend an approach where one of our consultants conducts a more extensive quality control, particularly for the more energetic conditions using our offline data archives. Due to its nature as an automated service we cannot guarantee that information from the portal is accurate or fit for the purpose for which it is used (i.e. extremes, highresolution, nearshore with complex bathymetries). It is the responsibility of the user to decide if the data meets his or her requirements or not. When in doubt: BMT ARGOSS would be pleased to offer advice on the applicability of waveclimate.com results the anticipated data quality for a given location or model point. Waveclimate.com is widely used by a variety of clients that use the service to make an quick assessment of wind wave conditions for sites of interest or to tender for operations that may be affected by metocean conditions. Loyal waveclimate.com users serviced include: Oil & Gas companies Renewable energy operators developers Offshore (heavy lift) contractors Cable laying companies Dredgers Survey companies Overview of number of vouchers needed per download To test the functionality of the waveclimate.com service one can go to: log in using username demo by leaving the password blank

6 C. Overview of Wave Modelling at BMT ARGOSS -1- Global regional scales Ocean waves are a key component in the description of the offshore environment. BMTA uses a range of high quality wave models, measured wave data, certified analysis techniques to accurately describe characteristics of this environment for engineering operability applications. BMT ARGOSS runs 3rd generation wave prediction models based on WaveWatch III (WW3) code (Link) on a global grid several regional grids for hindcast forecast purposes. The global model is forced by the wind fields from the National Centre of Environmental Prediction (NCEP) CFSR Numerical Weather Prediction Model. Regional model implementations are driven by wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) NOAA s Climate Forecast System Reanalysis (CFSR). The figure below provides an overview of the various model implementations. Data are available from 1979 onwards. A table with the details of our wave hindcasts (spectral parameterized) is provided at the end of this appendix -2- Nearshore wave modelling For many applications the WW3 wave data provide sufficient information to characterize the offshore environment. However, in some areas model refinement (i.e. in coastal areas) additional modelling may be required. Depending on the local geometry type of application we can apply the following types of refinements to transform offshore wave conditions to nearshore conditions, optionally including wave growth by wind: Nesting of a high resolution WW3 grid in the global or regional WW3 model grid; Nesting of a high resolution SWAN model grid in the global or regional WW3 model grid; Application of a ray-tracing model to transform offshore wave spectra to locations of interest. Application of nested fine resolution WW3 grids is recommended for relatively open coastal areas where an increase in spatial resolution better reflects variations in bathymetry where a grid size of, say, 5 km is sufficient. As WW3 runs on regular rectangular spherical grids, no local refinements of grid sizes are possible. Further, as WW3 solves its underlying equations with explicit time stepping, too small grid sizes may lead to high processing times. In areas where local refinements of grid size are required BMTA uses the SWAN model. Like WW3, the SWAN model is a 3rd generation wave prediction model which is developed by Delft University of Technology (Link). In contrast to WW3 the SWAN model can hle very small spatial resolutions due to its solution method. Another feature of SWAN is its ability to use regular rectangular, curvi-linear or unstructured grids. Especially the latter option is useful to create a gradual change in spatial resolution from deep to shallow water to enable efficient computational grids. An example of such a grid is shown in the figure below. App. Fig. B Example of unstructured SWAN grid with spatial variation of bathymetry BMTA has developed tools to efficiently nest the SWAN model into our global or regional WW3 model grids. Required depth information is automatically included based on inhouse databases with digital bathymetric information. In relatively simple geometries BMTA applies a ray-tracing technique (SWRT) to transform offshore spectra to nearshore locations. This technique can be applied in relatively open coastal areas over simple highly complicated bathymetries. Wave growth by wind depth induced dissipation effects are included in this transformation. The figure below shows an example of the ray pattern used by our SWRT method. App. Fig. C Example of wave rays used in the transformation of an offshore spectrum to near shore For ship response studies information on infra-gravity waves near mooring facilities or ports is important. Such long waves with periods in the order of minutes are usually generated by short wind wave modulations in the shallow coastal zone. These long waves can be estimated using the nonhydrostatic SWASH model, developed by Delft University of Technology (Link). Boundary conditions for this model can be given in terms of a wave spectrum, e.g. obtained from the SWAN model

7 -3- Hindcast forecast products Our global regional WW3 data bases cover a time period starting in January 1979 up to present. The database contains 3-hourly fields of integral wave parameters, quasi-2d wave spectra (E(f), mean wave direction (f) directional spreading (f)) for selected locations in coastal areas also the full 2D wave spectra E(f, ). These hindcast data are continuously supplemented with the most recent hindcast results. Where needed dedicated databases with hindcast results can be generated using local high-resolution WW3, SWAN or SWRT grids, thereby providing a valuable data source for climate design studies. Also hourly data is available for selected parameters. BMTA can also provide forecast services using the same suite of models as used for our hindcasts. For this particular application our wave models are driven by NOAA forecast winds. These data are recalibrated validated against satellite wind measurements from wind scatterometer (ERS, QuikSCAT) altimeter (all missions flown) by BMT ARGOSS. The satellite wind wave data used for this purpose are themselves thoroughly checked for each mission/sensor/year are calibrated validated on buoy measurements made available through NOAA NDBC. Avoiding bias in calibrations has been a major concern in developing these procedures. The product consists of a graphical tabular presentation of wind wave parameters for a forecast period of 10 days. Such weather reports can either be automatically generated or are manually produced by our team of 24/7 marine meteorologists. They are sent to Clients by / or other electronic means -4- Spectral wave portioning Wind sea swell components can be derived at the output site by spectral splitting of the total sea model spectra. Typically BMTA will select the optimal approach from a choice of three, developed or adapted in-house: o o It is a wind sea or a swell peak according to a certain minimal wave steepness (multiple wind sea peaks are merged into one wind sea peak). The number of swell peaks should be reduced by merging (step by step) swell peaks that are closest to one another in peak direction. -5- Extreme conditions Our global regional databases are based on wave models driven by winds from atmospheric models (NOAA, ECMWF or CFSR). A limitation of these wind sources is that they are too coarse in spatial temporal resolution to resolve highly energetic events like tropical storms (hurricanes, cyclones or typhoons). Inclusion of these effects is relevant for the derivation of design conditions in many areas in the world. BMTA has developed tools to derive extreme statistical characteristics of wind wave conditions originating from tropical cyclones. Historical tropical storm tracks data are stored in our in-house tropical storm database based on data downloaded from UNISYS (Link) IBTRACS (Link). The database contains storm tracks which include time, centre location, forward transition speed direction, central pressure, maximum wind speed (1-minute wind speed at 10m). Since the reliability of historical tracks reduces further back in time, we utilise the data as of 1972 for the southern hemisphere as of 1946 for the northern hemisphere; these are considered to be of high reliability. An example of tropical storm tracks for a location in the South China Sea is shown in the following figure. The 'Holthuissen' method estimates which wind waves could have potentially grown by looking at the fetch along the wind direction. The Holthuijssen splitter returns only two systems (sea+swell). The "Valk" method attempts to find peaks in the spectrum. The number of peaks attempted to find is configurable so usable for a multi-peaked spectrum containing more than 2 swell components. No wind information is used. The "van Vledder" method, in which regions are found of connected frequency-direction spectral bin that belong to a spectral peak, according to the Hasselmann mountaineer or inverse watershed scheme. Each peak reflects a wave system, either a wind sea system or one or more swell systems. After a 2D-spectrum has been partitioned, their spectral parameters are computed to determine whether: o o A peak is too small (according to a certain minimum significant wave height), in which case peaks are combined with the nearest peak A peak is too close to another peak in both frequency direction, in which case they are merged. App. Fig. D Tropical cyclone tracks for a location in the South China Sea To derive statistical properties due to the occurrence of tropical storm the offshore area is divided in blocks of approximately 30 km by 30 km. Next, the wind speed wind direction of each passing tropical cyclone is counted in each block using the Cooper model for specifying the 2Dwind field of each tropical storm. Based on this information a statistical analysis is applied to derive return periods of wind speeds for a selected set of directional sectors (e.g. with 45 width). Associated significant wave heights peak wave periods are derived using the Cooper model. The result of this analysis is an estimate of extreme wind wave conditions for an offshore area that can be used as boundary conditions for a wave transformation to coastal areas, e.g. with the SWAN model

8 Fraction of time exceeded -6- Metocean conditions The above described wind wave databases, either from our global regional WW3 models, or from dedicated nested models runs using WW3, SWAN or SWRT, serve as input data for the derivation of statistical properties of the climate. BMT ARGOSS typically distinguishes 3 different levels of offshore climate severity: 1. Ambient Climate: Normal conditions, comprising of conditions that prevail for the majority of the time. Products of this climate class comprise of (joint) tables of occurrence of wind wave parameters, duration statistics, workability conditions, wind-wave misalignment statistics. Data formats can be tables in excel format or graphical representations in term of roses. 2. Normal Climate Extremes: More energetic conditions, comprising of relatively severe conditions that only rarely occur in a particular area but are not classified as tropical storms. Products of this climate class comprise tables of extreme conditions for certain wind wave parameters per return periods, optionally given per direction sector or season. 3. Tropical Storm Extremes: More energetic conditions, comprising of the severe conditions that can occur as a result of a tropical storm. Products of this climate class consist of tables of wind speed wave parameters (significant wave height peak period) per direction sector return period. The first is typically of primary interest in operability studies all three are considered relevant in design risk related studies. Depending on the purpose of requirement each of these climate levels may require a different approach which may involve the use of additional datasets, different modelling techniques, additional means of data validation / or different analyses techniques. -7- Data validation Where possible BMTA tries to improve data quality by calibration wind wave data against measured information. The prime sources of measured data are satellite data. BMTA routinely collects altimeter scatterometer data to calibrate wind wave data. Wave model data are calibrated with satellite data to remove any systematic error the satellite data are calibrated with buoy data to improve the quality of the wave model. Following calibration the final bias in wave height relative to buoys is predominantly in the order of 5cm while wind speed is often off by less than 20cm/s. Satellite based calibration is usually applied on time series data extracted from the wave model databases for particular locations. Calibration can also be applied to improve the boundary conditions for nested models, although for shallow areas local conditions (wind water levels) may become dominant in achieving certain accuracy. The following figure shows an example of the calibration of wave model significant wave heights against satellite data. Calibration can be effectuated by applying a constant factor offset, but refinements with more weights to the tail of the distribution are also possible. Where possible we try to use local wind wave data to enhance data quality. Wave buoys usually provide the best local wave data to calibrate validate our local wave models PoE of wave height at 19 00'N, 'E for hindcast altimeter wave height [m] App. Fig. E Plot showing wave model performance for the parameter Hs in relation to satellite altimeter -8- Developments BMTA is continuously improving its tools to deliver the highest possible quality of services results thereof will gradually be included into our products. Related to the metocean conditions we are gradually replacing our driving wind fields by CFSR wind fields as they cover a longer time period (30 years instead of 21-years), because they have a higher spatial temporal resolution. Further, BMTA is in the process of improving the quality of these wind fields by calibration against satellite data. BMTA is also improving the quality applicability of their wave models. Firstly by increasing the spatial resolution of our global wave model to 0.5 x 0.5 reducing the output time step to 1 hour. Secondly, by implementing recent advances in wave modelling technology specifically related parameterisations of physical processes of whitecapping, wave-wave interactions depth-limited wave breaking. It has already been shown that these advances lead to better swell forecasts better estimates of wave conditions in shallow areas

9 App. Table A Overview of operational wave model grid hindcasts Model Grid Grid Resolution Area covered Top left Global 0 30' x 00 30' 01 00' x 01 15' Area covered Bottom right Spectral data time step 78 00'N x W 78 00'S x 'E 3 hourly EU Shelf 00 10' x 00 10' 66 00'N x 15 00'W 40 00'N x 31 00'E 3 hourly Mediterranean 00 15' x 00 15' 45 30'N x 5 15'E 30 30'N x 35 45'E 3 hourly Black Sea 00 15' x 00 15' 47 00'N x 27 45'E 41 00'N x 41 45'E 3 hourly Caspian Sea 00 15' x 00 15' 45 00'N x 43 15'N 36 45'N x 53 45'E 3 hourly Red Sea 00 15' x 00 15' 28 00'N, 33 45'E 13 00'N, 43 15'E 3 hourly Persian Gulf 00 15' x 00 15' 30 00'N x 48 15'E 24 15'N x 56 45'E 3 hourly Wind forcing CFSR NCEP CFSR ECMWF CFSR ECMWF CFSR ECMWF CFSR ECMWF CFSR ECMWF CFSR ECMWF Period covered (NPD* = near present day) 1979 NPD* NPD* NPD* NPD* NPD* NPD* NPD* NW Australia 00 10' x 00 10' 8 00'S x E 24 S x E 3 hourly CFSR 1979 NPD* South China Sea / Thail 00 05' x 00 05' 2 00'N x 99 00'E 14 00'N x 'E 3 hourly CFSR 1979 NPD* Indonesia/Java 00 05' x 00 05' 3 00 N x 79 30'E 9 00 S x E 3 hourly CFSR 1979 NPD* App. Fig. F Overview of regional BMT s operational numerical wave model implementations - 7 -

10 D. Calibration of hindcast with satellites -1- Overview Where possible, BMTA improves hindcasted wind wave data by calibration with in-situ observations, in particular by means of our validated in-house satellite observation dataset. Proof is given that, with reference to buoys, calibration with these best satellite observations can help improve the quality of model hindcast datasets: the satellites pull the model towards the buoys. We even state that: Model calibration with satellites is indispensable as numerical models perform poorly in fetch-limited, sheltered areas (NE coasts N-America, Asia, North Sea) models tend to underestimate the high energetic waves (North Sea, Black Sea, Falkls) BMTA routinely collects buoy data quality-flagged satellite data. Altimeters measure wind speed significant wave height scatterometers supply wind speed. First, altimeter missions are mutually calibrated in order to create a consistent set of altimeter data over time. Next, altimeter missions are merged calibrated with buoys. Scatterometer missions do not overlap in time. Therefore, they are calibrated with buoy data on a per-mission basis. Finally, hindcast data are calibrated with this improved satellite data. The in-house set of satellite data is updated calibrated with buoys once a year. Model corrections are updated if necessary. -2- Collocation of satellite samples Satellite observations are normally collected within a radius of 50km around the model points. The satellite data collected in such a small region in a single pass of the satellite are highly correlated. To account for this, we only use the nearest satellite sample from each pass. Model data are then linearly interpolated in time to form matched pairs of satellite measurement/modelled data that can be compared. Where possible, we merge altimeter scatterometer measurements to calibrate model wind speed. On open sea scatterometers supply the best wind measurements but altimeter measurements are also available closer to the coast altimeters are able to capture small scale squall-like phenomena. On open sea scatterometers prevail anyhow due to the large number of observations in comparison to the altimeter. -3- Calibration of ambient climate (automated) The ambient climate is calibrated globally in an automated way by application of location dependent correction coefficients, i.e. scale intercept. This calibration with satellites reduces the systematic model error equally in all of the world s oceans seas, including the semi-closed basins. The figures below show the bias of significant wave height of our (present) global hindcast (1 x 1¼ ) with reference to altimeters, before after calibration: App. Fig. A Bias model-altimeter before after automated calibration with altimeter The next table shows the impact of automated, satellitebased model calibration with reference to over 50 buoys in areas with a more or less consistent wave climate around Northern America (regions are explained later in this appendix). The systematic error in model wave height is reduced by 50% or more. The final bias in model wave height relative to buoys is at most 5cm whilst wind speed is off by less than 20cm/s. App. Table. A Bias model-buoys before after automated calibration with altimeters per region The next two figures demonstrate the positive effect of satellite-based hindcast calibration with reference to buoys in the Atlantic region, now with focus on the higher values of significant wave height. It becomes apparent that (automated) satellite calibration reduces the error in hindcast wave height, both for normal for more extreme wave conditions

11 App. Fig. C Typical correction of high energetic hindcast waves by means of altimeter in the central North Sea -5- Satellite data used for model calibration BMTA routinely collects satellite data accompanying quality flags indicating for example l, ice, rain, spatial coherence or suspect samples. Observations of significant wave height (Hs) sustained wind speed at 10m above the sea surface (u10) from practically all satellite missions to date, are available from in-house archives. Altimeters measure wind speed significant wave height scatterometers supply wind speed. Although this set of raw satellite data is quality-checked, it is still un-calibrated. Prior to use for (automated) calibration of model data, the following steps are taken to further improve the quality of the satellite data: App. Fig. B PoE of significant wave height of hindcast (red) buoys (blue) before (upper) after (lower) automated calibration with altimeters in the Atlantic region (ATL) -4- Local calibration (done by consultants) In consultancy projects, we use the above automated calibration, meant to correct the ambient climate, as point of departure. Our consultants use their expertise our data validation software tools to apply additional calibration tailored to the location of interest purpose of requirement. Typically, extreme wind wave conditions require additional attention. Un-calibrated model hindcasts often have shortcomings with respect to high energetic waves in a number of areas. The impact of satellite calibration is illustrated for the North Sea in the next figure. The probability of exceedance (PoE) distributions in the next figure show a typical altimeter-based correction applied to the higher energetic hindcast waves for a location in the central North Sea, which is part of our high resolution (10 x10 ) EUshelf model. The altimeter-based corrections result in a (more) realistic 100-year return value for hindcast significant wave height in the central North Sea. 4. Improve the quality of altimeter measurements a. Remove spikes trough median-filtering b. Apply mission-dependent a-priori corrections, e.g. removal of extreme winds waves from Jason-2 reported during the first month of the mission (July 2008) c. Calibrate each satellite with a master based on overlapping years; samples, no more than 1 hour apart, are co-located at crossovers d. Merge the set of mutually calibrated altimeter data to get a consistent data set over the years e. Calibrate the merged altimeter data with buoy observations; samples are co-located within 50km 30 minutes. 5. Calibrate scatterometer wind speed per mission with buoys measurements, co-located within 50 km 30 minutes. 6. Merge calibrated altimeter calibrated scatterometer wind speed observations. Based on the number of measurements available, scatterometer data will be dominant in the calibration on open sea whereas altimeter will gradually take over towards the coast

12 Bias STD of error in wave height (m) The above steps reduce the relative root mean square error (RRMSE 1 ) of altimeter wave height from about 15% to just over 10%. The next table summarizes the error statistics of best satellite data relative to buoys: App. Table. B Error statistics of best satellite data (S) with reference to 50 buoys (B) based on the years Altimeter Scatterometer Hs U10 U10 Avg B 2.06 m 6.96 m/s 6.91 m/s Bias S-B 0.00 m 0.05 m/s 0.06 m/s STD 0.25 m 1.34 m/s 1.07 m/s RRMSE 10.3 % 17.5 % 14.1 % Corr The next plot demonstrates that our best in-house satellite data, used for hindcast calibration, has consistent high quality over the years, both in terms of bias variability Bias STDE Error in wave height of alt minus buoy per class Classes App. Fig. E Error statistics of best satellite data with reference to 50 buoys over the years Not only the mean values but also the higher extreme values of significant wave height as observed by buoys by best altimeter match very well as can be seen from the probability of exceedance (PoE) plot below. App. Fig. F PoE of significant wave height of best altimeter 50 buoys based on the years Buoys used for satellite calibration BMTA maintains a set of over 50 buoys around Northern America for satellite calibration verification of the global model. The Gulf of Mexico (GOM) Northern Atlantic east of Northern America (ATL) Offshore Newfoundl (NFL) Northern Pacific (PAC) The region around Hawaii (HAW) The buoys are extensively checked for bad or duplicate data records. Satellite data are co-located for each buoy (within 50 km 30 minutes) the consistency of satellite-buoy error statistics is checked over the years. Sudden changes, traced back via the regional statistics to individual buoys, are mostly caused by a malfunction of the buoy. The buoys are depicted in the next figure. App. Fig. G Location of buoys used to calibrate satellite data as reference for hindcast data. For statistics, five regions are distinguished with a more or less uniform wave climate: 1 The relative error RRMSE is the root-mean-square error normalised by the root-mean-square value of the buoy wave height. Similarly, we maintain use a set of buoys in the North Sea in the Northern Atlantic for verification of our local high resolution EU-shelf model

13 E. Wave Propagation, Wave Ray Tracing (SWRT) Within BMT ARGOSS the Wave Ray Tracing Model is the simple method to propagate offshore wave conditions from a wave hindcast to the shallow-water environment or to obtain an offshore wave climate that consists of a weighted average from surrounding model grid points. The Wave Ray Tracing Model is used when the nearest hindcast grid points with representative data are situated far from the location of interest or if there are bathymetry gradients, coastlines or isls between these grid points the nearshore location. In other words, it is used when the hindcast model resolution is insufficient for the (shallow-water) area surrounding the location of interest. It is generally not used to assess effects of objects like dams, breakwaters, reefs dredged channels with spatial scales of a few representative wavelengths. The nearshore transformation takes account of the following using the offshore hindcast at one or several grid points as boundary conditions: Sheltering Refraction of waves by varying bathymetry Wave shoaling due to changing water depth Wave breaking due to the limited water depth or steepness of waves in deep water Local fetch-limited growth. The first step in the wave transformation is to calculate the wave trajectories from the offshore boundary to the near shore location of interest. A wave ray is the trajectory in space followed by a wave packet with a particular frequency initial propagation direction. It is determined by the spatial variation of depth current. The present model only accounts for the effect of depth variation; the effect of current variation is ignored. A wave ray bends ( wave refraction ) where there is a gradient in depth perpendicular to the wave direction, which causes a gradient in propagation speed along a wave crest. Starting from the near shore location, ray back-tracing is used to compute the wave rays for a discrete set of frequencies near shore propagation directions. The frequencies are the same as those of the wave hindcast model providing the offshore wave conditions, the wave directions are on a regular grid of 5 degree spacing or finer. An Ordinary Differential Equation (ODE) solver is used to solve the coupled equations for position propagation direction backwards, while deriving the wave number magnitude from the local depth. The depth is determined from a realistic digital bathymetry of the region of interest of sufficient resolution, based on nautical charts. The ray curvature is limited by an uncertainty principle to prevent unrealistic directional variation (oscillations). The solver adapts its step length to guarantee sufficient accuracy. An example illustrating wave rays is provided in the left image below When the wave rays are computed, the near shore spectra are calculated from the offshore spectra multiplied by weights determined from the Action Balance over the ray, reduced to satisfy the breaking criteria when necessary. For paths traced back to the shore, weights are set to zero fetch-limited growth is estimated from the local wind to account for waves from these directions. Fetch-limited growth is computed using a growth curve a JONSWAP spectral shape. Finally the propagated fetch-limited spectra are combined. By default the model bathymetry will be extracted from Admiralty Charts but when available other public domain sources or survey data that may be available can also be used. An example illustrating the local bathymetry is provided in the right image below. Furthermore, it is optimal to tune the model against measurements in the model domain. If these are not available the model output will be used as is, subject to a pragmatic examination by an experienced oceanographer. App. Fig. G Example of Wave Ray Tracing plots indicating wave rays (left) local bathymetry (right)

14 F. Wave Propagation, SWAN -1- Introduction Within BMT ARGOSS the SWAN model is typically used in situations where either the bathymetry includes complicated features, or non-linear wave interactions take place, or wave wind growth is important. SWAN can also include dams, submerged breakwaters reefs. Wave breaking bottom dissipation are also properly accounted for. SWAN will stop behaving properly for very steep slopes, will not model the behaviour of very long waves (T>30S) properly in the coastal area or in closed basins. SWAN is a two-dimensional spectral wave model of the third generation. This model is being developed by the SWAN team, which is mainly based in TU-Delft (Netherls) but gets contributors from many institutions has users all over the world. BMT ARGOSS is one of the contributors to the SWAN model. The link to the official web site can be found here: The SWAN solves the energy balance equation (reference) in the whole computational domain. The wave energy is discretized in a frequency directional domain at each node of the spatial computational grid, allowed to propagate in space evolve in time. The following wave processes are represented in the model Wave propagation in time space, shoaling, refraction due to current depth, frequency shifting due to currents non-stationary depth. Wave generation by wind. Three- four-wave interactions. Whitecapping, bottom friction depth-induced breaking. Dissipation due to vegetation. Wave-induced set-up. Transmission through reflection (specular diffuse) against obstacles. Diffraction (approximation) Wave current interaction (optional) SWAN computational grids cover 5 dimensions: spatial, frequencies, directional the time dimension. We pay strong attention to the design of our computational grids, we always focus on the following points: The frequency grids should cover the relevant range of periods representative of the local climate with enough resolution to capture the spectra properly; The directional grids might need a high resolution for cases with narrow bed spectra; The spatial grids have to capture with enough accuracy first, the bathymetry (to avoid interpolation errors) all the areas with significant gradients (due to refraction effects induced by slopes or currents); The time stepping or convergence behaviour need to be fine enough to capture the time variation of the forcing response of the domain. A time step too coarse might lead to non-converged results; Our experts always carry out sensitivity analyses to make sure that the settings applied for our projects are optimal. The spatial resolution the extent of the grids is one of the main items of interest in grid design. Our experts make use of two types of grids: regular grids or unstructured meshes. The first (regular grids) are based on rectangular elements do not require much effort in design, but give limitation in resolution for large areas. To overcome this limitation, regular grids of different resolutions are nested into each other. The resolution increases as the distance to the location of interest decreases. The second method to build spatial grids is based on triangular elements, requires dedicated tools in order to build such a grid. However, this method gives more flexibility allows us to refine our grids around features that might have a significant impact on the wave climate. Such grids allow us to build much larger domains without losing accuracy where it matters (areas with large gradients). SWAN can be configured in a number of different ways comprising of stationary non-stationary modes structured versus un-structured grids. The best suitable configuration is determined in discussion with the client, the purpose of requirement, the local geometry budgetary time constraints. -2-1D Approach For locations with almost parallel depth contours, SWAN can be used in a one-dimensional mode (the computational grid is defined along a representative profile perpendicular to the coast). SWAN will include the wind growth, the wave refraction effects, the non-linear wave interactions the bottom dissipation. It also includes wave breaking wave-set up. For ambient climate conditions the SWAN 1D model will be nested into the nearest BMT ARGOSS global hindcast model output point used to propagate all sea-states (spectra every 3 hours) towards the depth of interest. The full spectral information will then available at all required water depths, the requested normal statistics will be derived during the analysis. For the derivation of (preliminary) design conditions, the SWAN 1D model is used in stationary mode over reasonable horizontal distances (hypothesis of stationarity: propagation time of the waves over the computational domain shorter than time variation of the boundary forcing). The model is used to propagate specific extremes derived in deeper water for all relevant directional sectors, including the proper forcing: extreme wind speed, water level, wave height spectral shape associated with return period. The full spectral information is then available at all required water depths for all return periods offshore directional sectors. -3-2D Approach Given the more complex coastal zone, e.g. isls, shoals (reefs, s bars, underwater structures), underwater canyons, significant l masses complex coastlines, we will apply a two-dimension configuration of SWAN. Taking advantage of the ability of SWAN to include obstacles to accurately model wave reflection transmission around these structures, 2D wave fields will be investigated around the location of interest at other sites that are or may be of particular interest or concern. For ambient climate conditions the 2D SWAN models will be nested into our global wave watch III model, providing full 2D spectral boundary conditions over a period of more than 20 years. These boundary conditions will then propagated (with wind forcing) towards the nearshore location of interest

15 -4- Transformation Matrix Approach Given the purpose of requirement the local geography bathymetry we will apply the transfer function approach. We will set up, a predefined matrix of offshore wave conditions (integrated parameters Hm0, Tp, Dir) propagate them towards the nearshore locations of interest in stationary mode, giving for each offshore condition its nearshore corresponding condition. All these results will then be condensed into a transfer function that is in turn used to transform the full offshore climate or forecasted offshore conditions. This approach provides the full climate statistics forecast conditions in terms of integrated wave parameters but not the most accurate spectral shapes associated. -5- Full Hindcast Propagation Given the apparent requirement for very accurate results anticipated benefits of having full 2D spectral data available, our 20 years (plus) of offshore wave data will be propagated towards the coast with 2D SWAN model configuration in non-stationary mode. These model grids will be directly nested into the WaveWatch III model get spatially variable boundary conditions, which will help to further increase the accuracy of results. The wind forcing will also be defined in two dimensions time varying, based on global atmospheric model results. -6- Wave Current Interaction In areas where water level variations (tidal) currents on the local wave climate are anticipated we include the water level variations wave (tidal) current interactions in the computations. This will be done in the same manner as the wind wave boundary conditions. -7- Extremes - For the derivation of nearshore design conditions (ex: for breakwater design, coastal defences, pipeline ling,etc ), the SWAN 2D model is used in stationary mode over reasonable horizontal distances (hypothesis of stationarity: propagation time of the waves over the computational domain shorter than time variation of the boundary forcing). The model is used to propagate specific extremes derived in deeper water for all relevant offshore directional sectors, including the proper forcing: extreme wind speed, water level (including tidal surge levels), wave height spectral shape associated with return period. This approach is preferred to carrying out the extreme analysis on a nearshore ambient climate because the nearshore physical effects related to very extreme storms are not included in a 20 year hindcast (impact of the surge, duration of the storm, etc ) thus do not provide a reliable basis for extrapolation. The full spectral information is then available at all required water depths for all return periods offshore directional sectors. The nearshore waves are then presented per offshore wind sector per return period. App. Fig. H Example of unstructured SWAN grid with spatial variation of bathymetry

16 G. Ambient Climate, Workability / Downtime Analysis -1- Introduction For offshore coastal locations, a workability analysis estimates the probability that an operation, when started in an arbitrary month of the year, can be successfully completed within a given duration. The information from this type of study is particularly useful when planning an operation that comprises of multiple phases that each have their own limiting operational criteria. In this type of study an operation is typically divided into several (consecutive) phases. Each phase is defined by the amount of work to be done (hours) by operational limits(wind wave conditions) required to be able to safely complete the work for that particular phase. Constraints are expressed in terms of operability limits for ice cover (%), wind wave parameters switches indicating (ir-) reversibility / allowance for interuption of the work the need to immediately start the work after the completion of a previous phase. A phase during which the work cannot be reversed / interrupted is also referred to as a non-interruptible or critical phase. One particular sequence of phases corresponding constraints is referred to as a case. Multiple cases can be analysed. For each case, the analysis provides monthly distributions averages of the cumulative duration per phase of the time needed to complete the entire operation. Based on this type of study, the most favourable month best suitable approach can be selected for the operation. -2- Method The study is based on (in house) model time series of ice cover (%), wind wave parameters, calibrated with satellite observations ( / or in situ data when available). Within the time coverage of the database a large number of operations are simulated for each case analysed. Each simulated operation starts at an arbitrary date within the month considered. At some starting dates the work can begin immediately, for other dates the operation is delayed because of bad weather. The result is a large set of simulated durations to complete the work, both per phase for the entire operation. The probability that a phase or the entire operation will be completed within a given duration is found after sorting this data. Each phase of the operation is characterised by the type amount of work to be done (duration without constraints); Whether the work is reversible; The set of operability limits to be applied, for example to wave height wind speed; Whether the work must start immediately after completion of the previous phase; only relevant for two consecutive critical phases. App. Fig. I Workability studies are regularly used to support various types of installation operations App. Fig. J Workability studies can be carried out for offshore near shore locations -3- Operational thresholds (limits) Basically, operability limits can be applied to any combination of wind wave parameters. Limits can be applied to the following parameters: Wind speed (1-hour sustained at 10m); Significant wave height (total, wind-sea or swell); Wave period (total, wind-sea or swell); Presence of ice. When carrying out an analysis it is important to consider the effects that certain thresholds may have on the outcome of the study. For instance: applying limits to wave period as such is possible but questionable: long waves will be rejected but when wave height is very low, for example 0.01m, this does not seem to make sense. Operability limits applied to wind speed may vary per direction. Limits for significant wave height may vary with the corresponding mean wave direction/peak wave direction the zero-crossing/mean/peak wave period. In order to carry out the study the client is requested to provide operational limits in a specific format that may be either filled out in its entirety or in part. An example of such a table is provided below: App. Table B Operability limits (fictive) defined for limit set 1 for a single project phase Limit set 1 of 3 Sector center U10 (m/s) Hs (m) Peak wave period T (s) T 4 4<T 6 6<T 10] T>10 Omni ** ** ** ** ** 0º º º º º º º º A blank version of the above table is provided at the end of this section the client is requested to fill out this table according to the specifications of the operation for which the study is required

17 Probability of exceeding a criterion (%) Wave periods are by default used as exact. The figure below illustrates how wave periods are divided depending on the requested limits. -5- Relative severity of constraints In the study we can provide insight in what parameter limit is likely to have the most impact on the project execution. Exceedance of criteria for limit set 1 at 20 00'N 'E Total w ave height Wind speed Ice Wave period Height of sw ell Period of sw ell Height of w ind-sea Period of w ind-sea They can also be interpolated between wave period bins. The effect of this interpolation is illustrated in the figure below. 0 J F M A M J J A S O N D Month of the year App. Fig. M Impact of constraints -6- Deliverables description The deliverables of a workability study comprise of: Monthly probability distributions of the duration of the entire operation its phases, for each of the cases analysed. Summary table plot of expected duration of the entire operation for each case analysed against month of the year. Per month considered a table like the one below is prepared indicating the probability that the operation its phases can be completed within a given number of days(or hours). App. Fig. K Wave height samples limits (2D linear interpolation). Both figures above illustrate how samples are either found to be acceptable or rejected given the thresholds provided. -4- Project Phases As described in section 1 of this appendix we distinguish between project phases (P), that can all have their own limit sets (L) multiple variations can be investigated in different cases with different limit sets. A 3 phased operation carried out for 3 cases could be summarised as below. App. Table C Case description summary of 3 phased project for 3 cases Case Phases (P) limits sets (L) applied 1 P1 (L1) + P2 (L2) + P3 (L3) 2 P1 (L3) + P2 (L2) + P3 (L1) 3 P1 (L2) + P2 (L3) + P3 (L1) The above scenario would be adjusted accordingly based on the requirements specification of the project. App. Table F Probability (%) of the duration (days) of a fictional operation ( its phases) when started in an arbitrary month. Duration (days) Phase 1 Phase 2 Phase Mean Cum Information like in the table above is aggregated to show variability between different cases months visualised in graphs

18 Mean duration (days) App. Table D Mean duration of the entire operation (days) per month for all cases Month Case 1 Case 2 Case3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Tables like above allow for the comparison of the effect of different project approaches (for instance different material with different operational limits) operability variations between different months. To illustrate workability in different months the information in the previous tables is also set out in easy to interpret graphs. 160 Total mean duration per case at 20 00'N 'E Case 1 Case 2 Case J F M A M J J A S O N D Month of the year BMT ARGOSS 2013 App. Fig. L Mean duration of the entire operation per case against the months of the year App. Table E Probability (%) of the duration (days) of a fictional operation when started in an arbitrary month Duration (days) Case 1 Case 2 Case Mean

19 App. Table G Operability limit set to be defined by client. Additional limit sets may be added as required Limit set: Sector centre (Interpolated directional sectors) of Wind U10 (m/s) T Hs (m) Wave period T (s)* range (Peak period by default) with limiting H(s) per period range < T Hs (m) < T Hs (m) T > Hs (m) Omni And / Or 0º 45º 90º 135º 180º 225º 270º 315º *As indicated in section 3 (top left of the second page) wave periods can be defined as exact periods (default) linear interpolated period bins. Please select the required option of interest below (by crossing the required option. Only one period type type of wave period binning can be applied per study. Apply exact limits (default) Linear interpolation in Hs limits as function of wave period ( ) ( ) Define wave period type to be applied Tp Tz Tm ( ) ( ) ( ) App. Table H Characteristics per phase (N) Phase Activity description Work (Hours) How much time is required in hours? Reversible / interruptible May the phase be interrupted or not. If yes this means work can be started from where it was interrupted? Immediate start? Will this phase need to start immediately after the previous phase?* N *Only relevant for 2 consecutive (with immediate start) non-interruptible phases. App. Table I Case descriptions Case Phases (P) limits sets (L) applied for N cases n Client is requested to fill out fields indicated with as required. Where needed additional copies of tables may be provided. Rows may be added where the last row is indicated with N/n

20 H. REMBRANDT DMA App. Fig. N Typical Situation for application of Rembrt DMA to assess operability Introduction To analyse the motion responses of a moored ship to accurately determine the loads on its mooring systems BMT ARGOSS has developed the mathematical model REMBRANDT DMA (Dynamical Mooring Analysis). The model is applicable for vessels operating offshore (FPSO s), for vessels moored in the coastal zone (at LNG or Oil Terminals) in ports (such as bulk carriers, cruise, container ro-ro vessels). In the analysis the ships can be subjected to environmental forcing (i.e. wind, waves current) other external forcing (for example passing ships). This Appendix discusses REMBRANDT DMA how we apply it. Effective in-house systems The following paragraphs explain how we use our longterm historic metocean databases in combination with local models our computing cluster to give better, less conservative answers. The model REMBRANDT DMA is a state-of-the-art time domain simulation package for multi-body motions. REMBRANDT DMA has the following features: It properly models hydrodynamic reaction forces in the time domain: the frequency dependent added mass wave radiation damping are transformed to the time domain using Cummin s Convolution Integral Method. It computes the first second order wave forces using the wave force transfer functions derived by the industry-stard ANSYS AQWA-LINE package, see figure below. The hydrodynamic interaction effects with the seabed or a quay are properly accounted for. App. Fig. O AQWA representation of an LNG carrier Wave conditions can be represented by integrated wave spectra (for example JONSWAP, Pierson- Moskowitz, ICCEE), quasi-1d spectral representation (see Fig. C) a full 2Dimensional wave spectrum from our database or local wave model; It accounts for flow forces on the hull (e.g. following OCIMF 2008) accounting for limited water depths viscous damping terms for roll yaw motions; It uses time varying wind forces, including their directional variability, for example using the API stard wind spectrum which accounts for gustiness of the wind. It can represent Multi-body hydrodynamic interactions wave/flow/wind shielding effects. It incorporates non-linear mooring lines, anchor chains fender forces. Complex environmental conditions Typically operations take place with wind, flow swell waves all coming from different directions which change in time with speeds heights that also vary. If we are lucky, the wind waves are coming from the same direction as the wind. Choosing realistic but severe combinations of these parameters requires expertise not only in statistics but also in the response of the floating object. Choosing combinations of conditions for simulations that later can be used to determine operability also requires a considerable effort from experts that can be better spent. Use of REMBRANDT DMA avoids the need for these choices. The splitting of wave spectra into sea swell components is a pragmatic method to represent the complexity of the wave systems at the site. However, when performed by non-experts swell components may easily be overlooked which can result in overly optimistic operational statistics. Further, making selections of representative conditions from Hs--Tp-Direction climate scatter tables may result in over conservative choices due to the assignment of all the energy to a wave system to which the ship is particularly sensitive

21 An example of a mixed sea system is shown in the Hovmuller diagram below. In the figure, it can be seen that on Day 3 Day 6 the wave system consists of two very pronounced spectral peaks (orange/red colours) at about 12 seconds at about 5 seconds having almost a 90 offset in wave direction indicated by the black arrows. the response that uses realistic combinations of wind waves, swell waves, wind current if relevant. Design It is not practical in the design process to compute analyse multi-year time series of responses to test each new mooring line layout or fender stiffness. Initial designs are therefore developed using design winds, waves currents at high low water levels. A multi-year simulation is then easilly made for the initial layout to identify the typical conditions during survival operational downtime. The design is then completed using these selected conditions, with a final check for the whole data series. App. Fig. P - Hovmuller diagram showing the propagation of wave energy variance through time (days on the vertical axis). The Approach Operability Survival Downtime Our favoured approach to evaluate the moored ship operability or survival downtime in complex environmental conditions is to assess the behaviour of the moored ship using historical time series of environmental data. This involves automatically running REMBRANDT DMA with long time series of environmental conditions, including directional wave spectra. This means that correlations between water level, wind, waves current are automatically included without the need to derive idealised statistical relationships. Capturing the correlated conditions in a limited number of simulations (as used in more traditional approaches) requires considerable thought may still not give good answers. The specialist has to decide the combinations of wind wave height period direction, swell wave height period direction wind speed direction that are representative. This requires time requires that the specialist already has insight into the conditions that the moored ship is sensitive to. The data BMT ARGOSS holds world wide detailed databases for high quality wind, waves, currents water levels for a period over 30 years. This results in more reliable better design estimate as unrealistic extreme events or possible unfavourable combinations of events with a high percentage of occurance are well accounted for. App. Fig. R Wave heights derived from a detailed SWAN hindcast for several terminals in a partly sheltered bay. The advantage of using time-series of metocean data rather than climate tables (or scatter diagrams) is that the correlation between wind, waves / or currents is correctly reproduced. The vessel responds accurately to the site specific metocean conditions enabling the derivation of site-specific statistics for down-time survivability criteria. When using climate tables this is not the case often conservative assumptions need to be made about the correlation. This approach is particularly appropriate in situations where the seastates are complex (e.g. wind waves swells coming from different directions). App. Fig. Q Berge Stahl moored at an exposed berth. The application of quasi 1D or full 2D wave spectra (either from numerical models or measurements) in a mooring analysis avoids the necessity to artificially split the wave systems allows for a more accurate vessel response for such wave systems. Furthermore, applying long term time series allows for a reliable statistical representation of

22 The results A DMA hindcast results in time series of vessel motions mooring forces of up to 30 years. The resulting motions forces are then set against motion criteria (limits determining feasibility of loading unloading) mooring forces criteria (load limits for lines fenders) to determine those instances when the ship cannot operate (operational downtime) or cannot stay at berth (survival downtime). Extrapolation to year long design criteria is usually done on the basis of design conditions. However, it is the ship response that defines the determining loads for the terminal what can be classified as operational contions. The results of the motion response time series form the basis of the terminal optimisation. Example The figure opposite shows a scatter diagram for significant wave height versus peak period for which a DMA hindcast of mooring simulations has been carried out as if the vessel would have been moored at the site under these conditions. Down-time events are denoted by red dots conditions for which the motion response meets the operability requirements are indicated with blue dots. It can be observed that for the high waves higher periods the operations should be stopped if the vessel was moored at the site. Also, some down-time relates to events with rather low waves low wave periods. In these events, down-time is clearly the result of other aspects of metocean conditions which are not explainable solely on the basis of this Hs-Tp scatter diagram. Underlying swell (not necessarily observable in the peak period Tp), the directionality of the waves (e.g beam waves causing roll motions), or wind may all cause downtime. App. Fig. S Scatter distribution of Hs vs. peak wave period where blue indicates operational conditions red indicates downtime