Wave Observations and Forecast Modeling to Support Development of Virginia Offshore Wind Energy (RFP #14DE01, Topic 2)

Transcription

1 Wave Observations and Forecast Modeling to Support Development of Virginia Offshore Wind Energy (RFP #14DE01, Topic 2) Project Modeling Report for Year 2014 Updated October 6, 2014 Submitted to: Commonwealth of Virginia Department of Mines, Minerals and Energy Submitted by: Jeff Hanson, Ph.D.; WaveForce Technologies LLC Brian Blanton, Ph.D.; WaveForce Technologies LLC Kevin Gamiel, Island Edge Consulting LLC Mark Bushnell, CoastalObsTechServices LLC Julie Thomas, CDIP With Partners: Marty Bell and Steve Woll; WeatherFlow Inc Andre van der Westhuysen; NOAA NCEP Michael F. Forte; WaveForce Technologies LLC Kelly Knee; RPS ASA Jason Fleming; Seahorse Coastal Consulting Administrative Point of Contact: Mark Bushnell, CoastalObsTechServices LLC; ; Technical Point of Contact: Jeff Hanson, Ph.D., WaveForce Technologies LLC; ;

2 EXECUTIVE SUMMARY To support Virginia offshore renewable wind energy development, lease-area wave observations are underway and a custom ocean wave forecast modeling system is being developed for the Commonwealth of Virginia Department of Mines, Minerals and Energy (RFP #14DE01). The observations and forecasts will provide reliable wavefield condition information for all offshore activities including vessel and operations safety, asset and construction scheduling, and system operations planning. The measurement and forecast results will be conveniently available online to better inform critical decisions, leading directly to project cost savings for the Commonwealth. The project is committed to developing a robust, reliable, and accurate wave forecast system. The team consists of recognized wave observation and modeling experts from smallbusinesses in the mid-atlantic region with an extensive record of success with conducting wave measurement and modeling projects for both federal (NOAA, FEMA, USACE, BOEM) and local (VA, NC, MD, DE, NJ) government agencies. Furthermore the nation s wave Wave Observations modeling experts at NOAA, the Coastal Data Information Program (CDIP), and industry weather forecast provider WeatherFlow are critical partners on the project. Extensive leveraging of existing and past projects allows us to deliver the very best observations and forecasts available while providing good value to the State. Forecast Domain Bathymetry and Buoy Location We have procured and deployed an industrystandard Datawell DWR-MKIII Directional Waverider buoy within the Commonwealth of Virginia wind energy Lease Block 6111, aliquot P. The observations employ the highest quality equipment and processes, presently used for the Nation's largest directional wave observing network (CDIP). The buoy data are available online through CDIP at and through the National Buoy Data Center (NDBC) at Once the forecast model is operational, the buoy will provide essential ground truth data for quantifying and improving model prediction skill. Datawell Wave Buoy Prior to Deployment 2

3 Modeling System A custom wave modeling system has been developed for the Virginia offshore domain. The core of the modeling system is the proven Simulating WAves Nearshore (SWAN) wave model, dynamically coupled to the industry standard ADvanced CIRCulation Model (ADCIRC). This modeling system provides the Commonwealth flexibility in adding water levels, tides and currents to the forecast. The system will use the latest research to predict wave-breaking activity in the offshore areas of interest, critical information for planning safe operations at sea and predicting expected wave loads on offshore structures. Hindcast validation tests have shown the model to predict wave heights with mean errors less than 1 ft. For the operational system, wave prediction accuracy is expected to further improve due to the following additions: 1. Superior local wind forecasts from industry provider WeatherFlow. 2. Enhanced bathymetry to capture the details of wave interactions with the sea floor. 3. Direct boundary inputs from NOAA operational global WAVEWATCH III forecast system. Better forecasts for at-sea safety Operational Forecast Implementation During FY15 we propose to make the wave forecasting system operational. The system will be deployed using Amazon Web Services (AWS), commonly known as the Amazon cloud. This offers a number of advantages over traditional solutions, including zero hardware costs, utility-based pricing, increased reliability, high-speed networking and dynamic resource allocation. Hence, cloud computing will result in significant cost savings for the Commonwealth. The Virginia forecast System is configured for operation in the Amazon Cloud The forecast system has been developed to be fully expandable. An available option is to add water levels, tides and currents to the operational forecast. This would provide additional safety and construction information related to storm surge and water currents, and would also improve the wave forecast by incorporating water depth changes into the wave model dynamics. Furthermore, the modeling system can be expanded to include additional domains of interest to the wind energy community. If adopted by neighboring states, potential benefits include cost sharing/reduction, common products for consumers, and enhanced system credibility. 3

4 Online User Interface Our FY15 plans include hosting the wave observation and forecast information in a convenient, webbased system for easy access by wind area site planners, developers and operators. Building upon technology developed for the highly successful Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS), we will develop a modern map-based user interface for the measurement and modeling products. Furthermore the system will host real-time wind and wave forecast validations using available observations in the region, including the buoy deployed for this project. As new data are available, associated model performance graphics, error metrics and skill scores will update daily to provide operators with a real-time measure of forecast confidence. A custom user portal will provide access to Virginia wind area measurement and forecast data products Path Forward The DMME wave observation and forecast modeling team is ready to implement a robust operational forecast system, coupled to a modern online user interface, for the Commonwealth of Virginia. The cloud-based approach will provide a cost-effective, scalable, and feature-expandable path forward into the offshore wind energy sector. Project Contacts Mark Bushnell, CoastalObsTechServices LLC; ; Mark@CoastalObsTechServices.com Jeff Hanson, Ph.D., WaveForce Technologies LLC; ; Jeff@WaveForceTechnologies.com 4

5 1. Introduction 1.1 Background The Commonwealth of Virginia Department of Mines, Minerals and Energy (DMME) RFP #14DE01 describes a need to develop a demonstrably accurate wave height and breaking wave forecast model to support Virginia offshore renewable wind energy development in the vicinity of the Virginia wind lease area depicted in Figure 1.1. Operational wave forecasting will support decision making on several levels, from planning daily operations, mitigating the risk of offshore wind project construction delays, and scheduling facility inspection and maintenance activities based on predicted wave loading histories. Furthermore such a capability will help developers evaluate the risk of lost revenues due to moderately high wave action during extreme storm events. Operational wave height forecasts in the wind lease region are currently provided by the National Weather Service (NWS) using the third-generation numerical wave model WAVEWATCH III. The Virginia wind lease domain resides within the 0.25-deg resolution Western North Atlantic WAVEWATCH III modeling grid. In a US Bureau of Safety and Environmental Enforcement (BSEE) investigation of extreme wind and wave climatologies for offshore wind lease areas, Forte and Hanson (2012) evaluated WAVEWATCH III hindcast performance in the mid-atlantic offshore domain. The results of this study suggest that the wind and wave resolution within this modeling system is inadequate to consistently resolve peak wave conditions in extreme events. During a 10-year evaluation period ( ), Forte and Hanson show that WAVEWATCH III peak significant wave heights are under-predicted by 0.3 m-0.5 m. A recent coastal storm surge modeling study of the FEMA Region III mid-atlantic coastal domain resolved this issue of under-predicting wave heights by using (1) forcing winds that adequately resolve the storm structure and capture the peak winds associated with the event, and (2) an adaptable wave modeling system that allows you to add resolution where needed based on shelf bathymetry and expected wind gradients (Blanton et al., 2011; Hanson et al., 2011). Although this investigation was focused on prediction of coastal storm surge during extreme events, an important element was having an accurate estimate of wave forcing along the coastal boundaries. An unstructured, finite element version of the Simulating WAves Nearshore (SWAN) model developed for the FEMA study exhibited a high level of wave prediction skill in the mid-atlantic region. The modeling system was validated on a variety of extreme storm events including Hurricane Isabel (September 2003), Hurricane Ernesto (September 2006), and Extratropical storm Ida (or Nor Ida; November 2009). The resulting system captured the peak significant wave heights from each of these storms with a very high degree of skill with an overall bias of only 0.14 m, which is certainly close to the overall accuracy of the ground truth measurements. Of critical importance in developing an accurate wave forecast is to have high-quality driving wind fields. The NWS National Centers for Environmental Prediction (NCEP) generates a variety of operational wind products for the mid-atlantic coastal region, including the 5-km National Digital Forecast Database (NDFD) and an experimental 2-km Weather Research and Forecasting Model (WRF) simulation. In 5

6 addition, WeatherFlow, Inc. has performed extensive research on understanding of the middle Atlantic coastal zone, resulting in the development of a significantly improved wind and weather forecasting system for the middle Atlantic coastal waters. It is likely that output from this system can be used to support offshore wind energy applications. Figure 1.1. Nautical chart detail of forecast modeling domain of interest, including the commercial wind energy area and associated research leases. Furthermore, offshore wind energy development requires information on breaking wave behavior in the areas of interest. Wave energy is dissipated both by depth-induced breaking and by oceanic whitecapping. Depth-induced breaking results from wave shoaling in shallow water and oceanic whitecapping is largely controlled by energy input by the wind. Both types of wave breaking can influence operational safety as well as structural loading leading to fatigue. For operational safety, information of the fraction of breaking waves may be sufficient. For load calculations, information on the energy dissipation rate is needed. Although SWAN has source terms for computing breaking wave dissipation rates and the fraction of depth breaking coverage (Van der Westhuysen et.al. 2007), oceanic whitecap fraction is not presently an output option. This research area is further addressed below. 6

7 1.2 Approach This project has leveraged the modeling system development work performed for FEMA, as well as a North Carolina Renewable Ocean Energy study (Edge and White, 2011), to develop and test a state-ofthe-art wind and wave modeling system for the Commonwealth of Virginia. Here we describe 1) the development of the wave modeling system for the region, 2) a comprehensive validation to demonstrate model accuracy, and 3) an implementation plan for operational forecasting. We include an improved forecasting approach for breaking waves, which is necessary to fully characterize conditions in the area of interest. To achieve these objectives we have engaged the academic, government and private partners necessary to ensure seamless integration of the latest modeling technologies within an established national program of wave measurements and modeling. The proposed effort leverages a substantial amount of work currently and previously funded by other entities. This leveraging has resulted in a substantial cost savings for Virginia DMME. This wave model development effort builds upon recent mid-atlantic coastal modeling work performed for BSEE, FEMA, and North Carolina. The primary deliverables reported on here are described below. The specific task numbers from our accepted proposal are used for reference. Task 14.4: Wave Observations- Present acquired wave data from the DMME lease area wave buoy and compare with surrounding buoys and operational model output. Task 14.5: Modeling System. Describe required modeling system components. This includes the necessary unstructured bathymetry grid for SWAN. Task 14.6: Cloud Computing - Describe implementation of the wave modeling system using computer resources in the Amazon Elastic Compute Cloud (EC2). Document model efficiency and anticipated operational costs. Task 14.7: Modeling System Validation Evaluate relevant modeling system components including the bathymetric grid, input winds, and model system performance. Task 14.8: Wave Breaking - Develop a breaking wave modeling approach for the Virginia wind lease domain that includes prediction of the ocean surface whitecap fraction. Task 14.9: Forecast Implementation Plan - Develop an implementation plan for the cloud-based operational forecast system. These deliverables are presented in the following sections. A final section describes the recommended modeling system tasks for FY15. 7

8 2. Wave Observations (Task 14.4) 2.1 Buoy description Our project partners from CoastalObsTechServices LLC and the Coastal Data Information Program (CDIP) have procured configured and deployed a state-of-the-art Datawell DWR-MKIII Directional Waverider buoy within in the Commonwealth of Virginia wind energy Lease Block 6111, area P (Figure 1.1). The deployment occurred on June 11, 2014 at N, W in a water depth of approximately 26m. Additional details are available in Appendices I and II. Once the Virginia wave forecast model is operational, this buoy will provide valuable ground truth data for quantifying and improving model prediction skill. The purpose of this section is to present the wave data collected to date from the new Virginia energy buoy. It should be noted that there are other NDBC and CDIP buoys in the area, as depicted by Figure 2.1 and listed in Table 2.1. In particular, the Cape Henry buoy (Station 44099) is closest to the Virginia buoy and is used here as a reference point to ensure that the Virginia buoy is providing reasonable data. Figure 2.1 Wave measurement stations including the new Virginia DMME wind energy buoy. 8

9 Table 2.1. Offshore Virginia Buoy Stations Owner NDBC ID Latitude Longitude Depth (m) Type USACE N W 95 3-meter discus buoy Scripps/CDIP N W 18.3 Datawell DWR-MKIII Scripps/CDIP N W 11.6 Datawell DWR-MKIII CBIBS N W 8.2 Axys Watchkeeper VA DMME N W 27 Datawell DWR-MKIII 2.2 Data Preparation Coastal Data Information Program (CDIP) The buoy data are initially collected and processed by CDIP. The Datawell buoys provide a continuous data collection opportunity, achieved via an Iridium data link every 30 minutes, at a nominal 1 Hz sampling frequency. All quality-controlled CDIP data and products are available from the CDIP Web site in near-real time 1. The time series records are converted to half-hourly directional wave spectra and transmitted to NDBC every 30 minutes. Bulk wave statistics from these measurements are subsequently included on their Advanced Weather Interactive Processing System (AWIPS) for distribution to the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Office and inclusion on the marine weather broadcasts. The spectral data are also distributed to the National Oceanographic Data Center (NODC) on a monthly basis. NODC provides federally recognized, permanent, and accessible archive capability. XWaves Ocean Wavefield Analysis Tools The WaveForce Technologies XWaves ocean wavefield analysis tools, described at are routinely used by metocean engineers to convert measured and modeled spectral wave data into a variety of products to support such activities as offshore site selection, engineering design, fatigue evaluation, operational hazard assessment, and model performance evaluation. Included are industry-proven tools for the separation and tracking of ocean wavefield sea and swell wave systems. The Virginia buoy data prepared by CDIP are processed using XWaves to develop a suite of wavefield analysis displays including data comparisons with the nearby Cape Henry buoy, and a detailed decomposition of the Hurricane Arthur wavefield. 1 &stream=p1&xitem=pm) 9

10 For the results depicted in this section, the following XWaves analyses were performed: 1. A 3-hour weighted smoothing was applied to the directional wave spectra from CDIP. This helps reduce measurement noise in the final results. 2. The CDIP spectra were interpolated to 48 frequency bins of.01hz resolution and 36 angle bins of 10-deg resolution. This further helps to reduce measurement noise and allows for efficient processing of the wave spectra 3. The resulting spectra were partitioned and tracked into evolving sea and swell wave systems The above procedure was applied to both the Virginia DMME buoy observations plus the Cape Henry buoy observations for the measurement time period (11 June 7 July 2014). This allows direct comparison of results from the two buoy stations. 2.3 Observations The observational period starts with the buoy deployment on 11 June 2014 and spans until July 7, The extension past June 30 allows inclusion of waves from Hurricane Arthur which passed to the south of the wind energy lease area. Cape Henry Comparisons To develop confidence that the Virginia energy buoy is reporting reasonable wavefield data, we compare observations from the Virginia buoy to those from the Cape Henry buoy during the observational time period. Comparisons of significant wave heights, peak wave periods, and peak wave directions from these buoys appear in Figure 2.2. Overall the two stations show good agreement, providing us with immediate confidence in the fidelity of the data collected by the Virginia energy buoy. Detailed comparisons of the buoy wave parameters during Hurricane Arthur appear in Figure 2.3. Although the peak wave periods and directions align very well during the storm, it is noted that the peak wave heights in the Virginia energy area are 2-3 ft higher than at the Cape Henry station. This is expected, as the storm waves will dissipate energy as they move into shallower water towards the coast. Furthermore this provides a nice demonstration as to why independent wave measurements are needed for the Virginia energy area, as this area experiences a different wave climate than in the vicinity of the Cape Henry buoy. Wave Systems In addition to the mean wave parameters described above, the buoy spectral data allows us to explore details on the evolving wave systems in the study area. A wave system is defined as a coherent sequence (train) of waves, generated from a specific wind generation location, and propagating past the measurement site. Windseas are wave systems that are locally generated, while swell wave systems propagate into the area from distant wind events. An initial look at the wave systems observed in the Virginia energy area is provided by the wave spectrograph display of Figure 2.4. For reference, the significant wave height record for the time period appears in the upper panel. The lower panel, referred to as a spectrograph, depicts the evolving wave energy as a function of wave frequency. Hotter colors (yellows and reds) depict times and frequencies 10

11 with higher wave energy. A vertical slice from this plot represents the non-directional wave spectrum computed from a given half-hourly wave record. Wave systems are represented by continuous bands of hotter colors in the display. Growing windseas appear as near near-vertical bands of color that rapidly evolve from high to low frequencies, while evolving swells appear as long horizontal bands of low frequency energy. It is interesting to note that at any given time it is common to see one or two evolving swells along with a simultaneously occurring windsea, demonstrating the complexity of the wave field in this offshore environment. To explore these details, the time periods represented by the yellow boxes labeled (a) and (b) on Figure 2.4 are further examined below. Wave system detail during a low wave energy period starting on 20 June 2014, depicted by box (a) on Figure 2.4, is depicted by the wave vector history plot of Figure 2.5. Such displays show wave system evolution in terms of wave component vectors, where each vector represents an identified peak in the directional wave spectra. Evolving wave system height, peak period, and mean direction are represented by vector length, origin on the y-axis, and azimuth, respectively. Furthermore coherent wave systems are identified by color groups, where black vectors represent windseas and colored vectors represent various swell systems. Figure 2.2. Comparison of Virginia Energy and Cape Henry buoy wave observations for the full time period of interest. 11

12 Figure 2.3. Detail of Virginia Energy and Cape Henry buoy wave observations during the passage of Hurricane Arthur on July 4, Figure 2.4. Significant wave height time series (upper) and spectrograph (lower) showing Virginia energy buoy wavefield evolution and areas for enhanced inspection (yellow boxes). 12

13 Inspection of Figure 2.5 immediately shows the complexity of the offshore wavefield, with up to 4 wave systems occurring simultaneously in the wind energy site. It should be noted that these are all very low energy wave systems, as the total significant wave height through this time period is between 0.4 and 1.4 m (lower panel). The gradual increase in wave height through this time period is driven by a growing southeast windsea (black arrows) starting on June 21. Of significant importance to offshore operations is the occurrence of long-period swells with strong orbital currents that can exert substantial forces on offshore structures. Here we see the presence of such swell in two different systems of s periods across the bottom of the wave vector history plot. Such swells can be traced to storms off South Africa during the Southern Hemisphere winter (May-August). It is important to note that the Virginia energy buoy has the ability to resolve such events. Figure 2.5. Wave vector history during a low energy time period starting on June 20, See text for plot description. In contrast to the low energy period depicted by Figure 2.5, the evolution of wave systems leading up to and through Hurricane Arthur are depicted by Figure 2.6. This display shows that that the peak 3.2 m wave heights during Arthur on July 4, 2014 were composed of two orthogonal wave systems; a strong northeast windsea (black arrows) and a southeast swell (red arrows). Such a confused sea is extremely dangerous for vessels and furthermore places strong wave loads on offshore structures from orthogonal directions. All of these examples demonstrate the importance of having quality wavefield measurements from which to compute wave loading histories and validate forecast modeling. 13

14 2.4 Climatology Although there are insufficient data to present a full wavefield climatology for the offshore wind energy site, we have collected data during an important summer period (11 June 7 July 2014) and present those results herein. The distribution of windsea and swell heights, periods, and directions during this period is presented by the climatology displays of Figure 2.7. The wave height histograms (upper left panel) show a mix of windseas and swells, with dominant sea and swell wave heights of approximately 0.5 m. Furthermore the results indicate that the largest waves during this period are most typically from local windseas. Figure 2.6. Wave vector history during Hurricane Arthur. See text for plot description. The distribution of observed wave periods (lower left panel) is strongly bimodal. Windsea and regional swell periods are broadly distributed between 2 and 6 s. The most common swells are approximately 9 s duration, representing a fairly persistent southeast wave train generated by summer trade winds. Examples of these waves can be seen on both of the wave vector plots (Figures 2.5 and 2.6). The swell period histogram also indicates the presence of longer period swells out to about 14 s. As indicated previously these are typically from the Southern Ocean during the Northern Hemisphere summer months. 14

15 The wave direction histogram (upper right panel) shows a bimodal distribution for both windsea and swell, representing both northeast and southerly wind fields. The large peak in swell directions around 100 deg represents swell alignment with prevailing bathymetric contours. The scatter plot (lower right panel) simply shows the correlation between sea and swell wave heights and periods during this period. The large excursions in height in both windsea and swell are a result of the passage of Hurricane Arthur. These results show that the test area wavefield can be broken down into the following four general wave components: 1. Locally generated windsea 2. Regional swell from local windsea 3. Southeast trade swell 4. Southern ocean swell Although no tropical storms were observed during this short period, tropical storms can also result in swell originating from the North Atlantic tropical domain. A longer climatology period will certainly reveal this and other important wave generation areas for the site. An overall wave system summary appears in Table 2.2. This joint occurrence table shows the percentage of time that various wavefield conditions prevailed during the measurement period. Note that windseas were present 90% of the time, a single swell system 58% of the time, and two swell systems 28% of the time. Windseas occur in the presence of swells 76% of the time. Furthermore, there were no calm periods observed during this time. Figure 2.7. Virginia energy area wavefield climatology for the period 11 June 2014 through July 7,

16 Table 2.2. Joint Occurrence Table (%) for Observed Wave Systems Conditions 1 Swell 2 swell No Swell Total Windsea No Windsea Total Modeling System (Task 14.5) The modeling system for this DMME project uses a suite of state-of-the-art numerical models to compute wind and wave field characteristics for the study site. The wave models are the nearshore wave model Simulating Waves Nearshore (SWAN) and the deep-water wave model WAVEWATCH III (Tolman, 2002). Wind fields for validation studies are provided through analyzed Oceanweather, Inc. wind products. Details of these models are described below. For simulation of waves on the continental shelf, we have chosen to use the coastal wave model SWAN, a third-generation, phase-averaged numerical wave model for the simulation of waves in waters of deep, intermediate and finite depth (Booij et. al., 1999, Zijlema and van der Westhuysen, 2005). Specifically, we are using a version of SWAN that has recently been formulated in finite elements (Zijlema, 2010) and dynamically coupled (Dietrich et al, 2011) to the storm surge and tide model ADvanced CIRCulation model for oceanic, coastal and estuarine waters (ADCIRC, Luettich et al, 1992; Westerink et al, 2008). Although this finite element version of SWAN has a different numerical implementation, it solves the same physics as the usual finite difference version of SWAN. Advantages to using this formulation include that 1) it permits a very flexible arrangement of model nodes that can be tailored to resolve critical hydraulic features without unnecessary over-resolution in other areas; 2) its implementation for parallel computing leverages the communication and input/output facilities built in to ADCIRC; and 3) it has been used extensively for many recent storm surge and wave projects that need very spatial high resolution. These projects include recent FEMA coastal flood insurance studies (Region 2, Region 3, and Texas), ongoing flood protection engineering design analysis for the New York/New Jersey region, sea level rise impact studies along the US east coast, and storm surge and wind wave risk studies for nuclear power plants located near the coast. For this project, we have developed a regional SWAN model grid that covers the bulk of the Middle Atlantic Bight continental shelf, from just north of Cape Hatteras to just north of Atlantic City, New Jersey, and includes Chesapeake and Delaware Bays. This domain is shown in Figure 3.1 (left panel). The finite element grid is denoted with the gray triangles and has approximately 10 km resolution in the DMME area. The ocean depths were interpolated from the FEMA Region 3 digital elevation model, 16

17 which has been updated in the DMME area with sounding data from the provided FUGRO dataset. The bathymetry for this grid is also shown in Figure 3.1 (right panel). SWAN is forced along the outer boundaries by the WAVEWATCH III wave spectra and over the entire nearshore domain by wind fields. For the multi-year validation simulations, we used wind fields from the USACE s Wave Information Studies (WIS) wind field reanalysis for the period , which are based on Oceanweather Inc's Interactive Objective Kinematic Analysis system (Cox et al, 1995). For a Hurricane Isabel (2003) simulation, we used wind fields from the FEMA Region 3 study, which were provided by Oceanweather for that project. Examples of these two types of wind fields are shown in Figure 3.2. Figure 3.1. SWAN modeling domain. Left) Unstructured (finite element) grid for project, shown with the gray triangles. There are nodes and triangles. WAVEWATCH III wave spectra are applied as boundary conditions at the green asterisks. The DMME site is marked with the red asterisk. Right) Bathymetry for regional SWAN grid. Depths are in meters, and are clipped at 50 meters to show details on the continental shelf. The grid depths in the deep ocean are consistent with the observed depths. 17

18 Figure 3.2. Examples of wind fields used for validation studies. Left) Hurricane Isabel (2003) wind field (vectors) just prior to landfall along the North Carolina coast and the maximum significant wave height (colors) over the Isabel simulation. Right) WIS wind fields from 2010, at September 3, 12:00 UTC. The large-scale wind fields (red) are used to drive the WAVEWATCH III simulation, and the higher resolution regional winds (blue) are used to drive the SWAN simulation. The VA DMME regional finite element SWAN grid boundary is shown in black. For clarity, every other wind vector is shown for the regional winds. The strong circulation shown near the middle east coast is Hurricane Earl. The weak circulation in the southeast corner of the large-scale winds (at 42 deg W, 15 deg N) is the remnants of the short-lived tropical storm Gaston. In either case, we used these winds to 1) compute the offshore wave fields using a ¼ deg western North Atlantic NOAA WAVEWATCH III configuration used for an ongoing renewable ocean energy project along the North Carolina coast (Edge and White, 2011), and 2) to compute the regional wave response using the SWAN model. It was necessary to compute the deep-water wave energy spectra for the hindcast simulations because NCEP does not archive the energy spectra from the operational model forecast system. The wave spectra computed with WAVEWATCH III in the first step are specified as open boundary conditions for SWAN s grid (shown with the green asterisks in Figure 3.1). The SWAN outer boundary was chosen to exactly align with existing operational NCEP WAVEWATCH III output locations that were implemented for mid-atlantic nearshore wave model applications. This choice will significantly simplify the use of NCEP operational wave model outputs in the regional modeling system for the operational forecasting setup. The simulation output consists of complete (global) time histories of the wave field parameters such as significant wave height, wave period and wave direction, as well as station (single point) output at specific locations in the model domain with the complete set of directional wave energy spectra. The locations for station output are currently set to existing NOAA NDBC observation locations within the 18

19 regional domain, as well as the Virginia DMME locations including the wave buoy deployment location. Additional locations can be easily added during the operational phase to suit specific needs. 4. Modeling System Validation (Task 14.7) 4.1 Objectives The development of any forecast system requires a comprehensive set of validation tests to determine if model accuracy is sufficient to meet forecast requirements. For example, the Virginia Forecast is required to provide area wave information out to several days with a greater short-term accuracy than is provided by the operational NOAA WAVEWATCH III model. We aim to achieve this goal through (1) forcing the wave modeling system with an improved high-resolution 36-h regional wind model, and (2) implementing a higher-resolution bathymetric grid in the modeling system. Although the full impact of the Virginia operational winds cannot be tested until the model becomes operational during 2015, a set of validation tests were performed during the 2014 model development phase to quantify modeling system performance using hindcast wind fields. Specific objectives of these tests were as follows: Quantify the hindcast performance of the VA DMME model during summer months Quantify the hindcast performance of the VA DMME model for storms events The summer month performance is important as the forecast model will initially be used to schedule wind area construction activities including vessel transits to and from the site. The storm event performance is important to help gage when construction and operation activities need to be terminated due to extreme wavefield conditions. For these tests, quality hindcast wind fields obtained from the US Army Corps of Engineers Wave Information Studies program were used. Additional detail on the WIS modeling is available at the WIS website Furthermore, an evaluation of the WeatherFlow operational wind product was made using observed winds from the Chesapeake light tower. This initial wind assessment provides a first glimpse into the accuracy and fidelity of the operational wind fields that we anticipate using to force the operational wave forecast model. 4.2 Approach The Virginia modeling system validation study required a meticulous preparation of the raw data and employed a variety of analysis approaches for evaluation of the various hindcast events. Essentially all of the data preparation and analysis was performed using the XWaves wavefield analysis toolbox developed by WaveForce (WaveForce, 2014). The XWaves Evaluate Module performs robust statistical comparisons between observations and model output (Hanson et al., 2009). The user selects between the full data set (all records), or a peak event analysis. The peak analysis employs a peak-over-threshold (POT) routine to isolate data peaks above user-defined or automated (standard deviation multiplier) thresholds. Temporal correlation (TC) and Quantile-Quantile (QQ) analyses are performed on the selected wind and wave parameters. Resulting error metrics include bias, RMS error and Scatter Index. 19

20 Preparation of the observed and hindcast data for analysis included the following: 1. Adjustment of observed winds to a neutral stability 10-m reference height following Large and Pond (1981). 2. Application of a 3-hour tapered smoothing function to the buoy wave spectra. This eliminates much of the sampling noise not present in the model. 3. Interpolation of the observed and hindcast wave spectra to a common set of frequency and direction bins. This allows for a direct comparison of like quantities. 4. Time-pairing of observed and hindcast wave spectra using a 20-minute temporal offset threshold. 5. Partitioning the observed and hindcast directional wave spectra into sea and swell components (Hanson and Phillips, 2001; Hanson et al., 2008). 6. Computation of significant Wave height (Hs), peak wave period (Tp), mean zero-crossing wave period (Tz), and mean direction from the observed and hindcast spectra and spectral partitions. 7. Computation of the following error metrics for wave height and period: bias, RMS error, Scatter Index (SI), and Regression coefficient (R 2 ). Also computation of angular bias and circular correlation for wave direction. All computations are described by Hanson et al., As indicated above, a variety of hindcast data were required to satisfy our model evaluation objectives. Note that all the hindcast runs were forced with WIS winds and employed the van der Westhuysen et al. (2007) whitecap dissipation source term. Furthermore, all hindcast validations were performed at the Cape Henry buoy station (Figure 2.1). This station was selected due to it having the closest proximity to the Virginia offshore wind energy area as well as being the buoy with the closest water depth to the VA buoy location (Table 2.1). This ensures that we are evaluating model physics behavior at a relatively similar range of depths. The results from each of these are described below. 4.3 SWAN Wave Hindcast Evaluation The full year 2010 SWAN hindcast was evaluated for replicating the wind sea and swell climatology and statistics observed at the Cape Henry buoy station A full-year test was deemed necessary to capture the full range of operating and storm conditions expected within the Virginia offshore wind energy area. The year 2010 was selected as it was the most recent year that the WIS winds were available to this study. In the following sections, the wave partitioning results are examined to determine how well the SWAN application performs for both locally generated wind seas and for swells generated away from the buoy location. The investigation includes a comparison of the observed and hindcast overall wave climatologies, sea and swell error metrics, and event peak characterization. Wavefield Climatology Observed and hindcast wavefield climatology data are evaluated to assess how well the hindcast captures the wave climate of the region of interest. A comparison of windsea and swell height, period and direction histograms appears in Figure 4.1. It is observed that the distributions of all three wave parameters agree well between observations and hindcast. The wave height distributions show that the site frequently exhibits a low-level swell of order m in height, centered at about 10s period, and arriving from approximately 100 deg (from North). Wind seas exhibit a higher range of heights, are 20

21 typically centered at 5s period, and arrive from either northeast or southeast directions. From these comparisons it appears that the hindcast captures the overall wave climatology reasonably well. Bulk Wavefield Properties Integral or bulk wavefield properties are used to provide information on how well the observed and hindcast total wave fields compare. Such properties are computed by integration over the entire spectrum and include the total significant wave height, zero-crossing or mean period, and mean wave direction. Time series comparisons of these properties for the 6-month period June through November 2010 appear in Figure 4.2 a-d. Included in this comparison are low-wave height summer months, important for site construction activities, as well as a number of storm events including Hurricane Earl on 3 September The SWAN wave heights (upper panels) agree reasonably well with observations, with the exception of some under-prediction of wave heights during summer events and over-prediction of wave height during peak fall and winter events. These results are comparable to the existing operational predictions, and will be rectified in the operational model (see 4.5 below). The SWAN mean wave periods in Figure 4.2 a-d (middle panels) are biased low compared to the observations. This is actually expected as our boundary waves are generated by a grid that does not include the extreme North and Eastern boundaries of the North Atlantic Ocean, and furthermore does not include any of the South Atlantic Ocean. Hence, distant low-period swell from any of these regions will not be represented in this evaluation hindcast. Our operational implementation, however, will include wave generation from the entire North and South Atlantic Ocean basins. SWAN wave directions in Figure 4.2 a-d (lower panels) agree favorably with CDIP observations. A slight rotation of hindcast waves, a few degrees clockwise of the observations, is apparent in much of the record. Although these are within the expected measurement error range, this may also be influenced by the bathymetric grid used in these test cases and is more fully discussed in the next section. 21

22 Figure 4.1. Station year 2010 wave system height (upper), period (middle) and direction (lower) histograms for the buoy observations (left) and SWAN hindcast (right). 22

23 Figure 4.2a. Comparison of Station observed (CDIP) and hindcast (SWAN) bulk wavefield properties for June Figure 4.2b. Comparison of Station observed (CDIP) and hindcast (SWAN) bulk wavefield properties for July

24 Figure 4.2c. Comparison of Station observed (CDIP) and hindcast (SWAN) bulk wavefield properties for August 2010 Figure 4.2d. Comparison of Station observed (CDIP) and hindcast (SWAN) bulk wavefield properties for September-November

25 Wave System Evaluation Correlations between the observed and hindcast paired windsea and swell statistics provide a detailed record-by-record evaluation of hindcast performance. Furthermore this approach allows us to quantify hindcast skill with a robust set of error metrics. A windsea and swell wave height scatter plot appears in Figure 4.3. Here we have partitioned the wavefield into three groups: WS Locally generated wind seas Sw1 Regional swells up to 13s wave period Sw2 Distant swells greater than 13s wave period The separation of swell into two wave period groups allows us to evaluate hindcast performance separately for regional and distant swell. The 13s wave period cut-off was selected by inspection of the wave period histograms of Figure 4.1. Although the majority of observed swells fall into the Sw1 category, Sw2 represents the important tail of the wave period distribution. Such low period waves can have significant impacts on offshore operations. Therefore, Figure 4.3 depicts the correlation of observed and hindcast wind sea and swell group significant wave heights. Although the three wave groups are reasonably well represented by the black line of best fit, there is much scatter to the data. The error metrics for these correlations appear in Table 4.1. These metrics suggest that there is not much difference in the way the hindcast represents each of the three wave groups. It should be noted, however, that wave height bias becomes increasingly more negative with increasing wave period. This is likely driven by the under-estimation of swell contributions from outside of our limited North Atlantic modeling domain. Error metrics for hindcast mean wave periods and directions appear in Tables 4.2 and 4.3, respectively. Note that mean wave period bias becomes increasingly more negative with increasing wave period. This is further evidence that our boundary waves are lacking important wave energy from the distant reaches of the North and South Atlantic basins. As suggested by the mean wave direction time series plot of Figure 4.2, the computed angular bias for wind sea and swell mean wave directions (Table 4.3) depicts a few degrees of rotation. As described below, a refined modeling grid will be implemented in the operational release of the model. It is likely that the refined grid will yield small adjustments to mean wave directions as the swells react to the detailed bottom bathymetry in the region. 25

26 Figure 4.3. Correlation of observed (Baseline) and hindcast (Evaluate) windsea and swell significant wave heights for 2010 at station Table 4.1. Station Year 2010 Wave Height Evaluation Metrics Wave System Bias (m) RMS Error(m) R 2 WS - Wind Sea SW1 - Regional Swell SW2 - Distant Swell Table 4.2. Station Year 2010 Mean Wave Period Evaluation Metrics Wave System Bias (s) RMS Error(s) R 2 WS - Wind Sea SW1 - Regional Swell SW2 - Distant Swell

27 Table 4.3. Station Year 2010 Mean Wave Direction Evaluation Metrics Wave System Angular Bias (deg) Cir Cor WS - Wind Sea SW1 - Regional Swell SW2 - Distant Swell WeatherFlow Winds The custom WeatherFlow wind model domain for the Virginia wind energy area was made operational in early June Chesapeake Light tower station output were collected from the model for an approximate 3-week period and compared to the light tower observations made at this location. In preparing the light tower data for analysis, wind speeds were adjusted to a 10-m reference height as described above. Time series comparisons of observed and nowcast wind speed and directions for this time period appear in Figure 4.4. WeatherFlow winds track the observed winds quite well, and capture what appear to be daily fluctuations in wind conditions. A slight overestimation of modeled wind speeds at some of the peak events may allow for a somewhat conservative wave forecast. For a more quantitative assessment of WeatherFlow performance at this station, a scatter plot of observed vs. hindcast wind speeds appears in Figure 4.5. Included in the plot are error metrics for the correlation. The model results are well distributed about the line of best fit and depict a slight 0.4 m/s positive bias, with an RMS error of only 2 m/s. A longer time series, including storm events, will be required for a complete evaluation of the WeatherFlow wind product. Figure 4.4. Time series comparison of observed and modeled wind speed and direction at the Chesapeake Light Tower during June

28 Figure 4.5. Scatter plot of observed (NDBC) vs. modeled (WeatherFlow) wind speeds at the Chesapeake Light Tower during June Model performance data appears in the upper left corner of the plot. 4.5 Recommendation It is important to note that hindcast performance reported here will not be a complete reflection of anticipated forecast performance. It is likely that the model short-term forecasts will be better than the observed hindcast performance for the following reasons: 1. The wave hindcast model domain does not cover the entire North and South Atlantic Ocean basins, as the forecast model domain will. Hence swell from both high-latitude North Atlantic storms and Southern Ocean storms is excluded in the hindcast analysis, but included in the ground-truth observations. 2. The hindcast windfields were generated from a relatively coarse global windfield model. The forecast windfields will be from a high-resolution regional wind model. 3. An improved, high-resolution, bathymetric grid has now been developed for the wind energy area. Hence, these initial validation test results should be considered preliminary measures of model performance. Once the operational model is in place, a more in-depth validation will be performed using the operational winds and ground-truth data directly obtained from the wind area buoy. 28

29 5. Wave Breaking (Task 14.8) 5.1 Dissipation Source Terms The SWAN model includes separate wave dissipation formulations, or source terms for bottom friction, depth or surf breaking, and oceanic whitecapping. To visualize the output of these terms during a major storm event, a SWAN hindcast of Hurricane Isabel (September 2003) was performed. The maximum significant wave heights produced by the Hindcast are depicted in Figure 5.1. It is interesting to note that there is a significant amount of spatial variability in wave heights in the vicinity of the Virginia offshore energy area. Over small spatial scales, this variability is primarily controlled by the various dissipation source terms. Example Dissipation source term output during the time of maximum wave heights appear in Figure 5.2 a-b. For these simulations the default settings on all source terms were applied. The results show that during the extreme wave conditions of the hurricane, surf breaking is the dominant dissipation mechanism within the 26-m depth region of the Virginia energy area. SWAN also has an output option for the fraction of depth breakers (Q B ) after Battjes and Janssen (1978) Q B = f(h rms /H m ), which expresses Q B as a ratio of mean wave height (H rms ) / max possible height (H m ) and allows breaking to increase as H rms approaches H m. Sample Q B output during the Hurricane Isabel extreme wave heights appears in Figure 5.3. This further confirms the extent of depth breaking during extreme storms can extend out to the Virginia wind energy area. Although important for offshore operations, SWAN does not include an option for the fractional coverage of ocean whitecaps. This will be further addressed in Section 5.3 below. Figure 5.1. Hurricane Isabel maximum significant wave heights. Location of the Virginia wind energy area is depicted by the circled red X. 29

30 5.2 Whitecapping Source Term Evaluation At present there are two whitecap dissipation source term options in SWAN, that of Komen et al. (1984) and van der Westhuysen et al. (2007). The default option is that of Komen, formulated to close the energy balance of waves with no explicit link to breaking and dissipation observations. In this case, the dissipation is proportional to the energy density and the overall steepness of the wave field (i.e. wave height to wavelength ratio). A consequence of this approach is that wave breaking does not impact the spectral shape and the average wave period. Using an approach based on observations, the van der Westhuysen method includes a power-law dependence on wave age. This potentially allows a more realistic evolution of the spectral shape during whitecapping. The selection of the appropriate whitecapping source term will help ensure that the whitecap fraction estimates generated by the model can be used with confidence. Separate year 2010 hindcast runs were made using the Komen and Westhuysen whitecap dissipation source terms. Each of these is compared to buoy observations at station Significant wave height comparisons appear in Figure 5.4. Wave height scatter plots (upper panels) compare the buoy observations (baseline) to time-paired hindcast wave heights (Evaluate). Although the scatter plots appear similar, note that a linear regression through each (green line) more closely matches the line of best fit (black line) in the Westhuysen physics case (upper right panel). The significant wave height time series comparison from December 2010 (lower panel) suggests that on average more whitecapping energy is dissipated at these depths by the Westhuysen Whitecap dissipation source term in SWAN. Associated error metrics from the full year of comparisons appear in Table 5.1. These results confirm that the Westhuysen source term yields a lower wave height bias and improved RMS error levels over the Komen source term. 30

31 Figure 5.2a. SWAN dissipation source term output (m 2 /s) during Hurricane Isabel peak wave heights: Bottom friction (DISBOT, upper left), whitecapping (DISWCAP, upper right), surf breaking (DISSURF, lower left), and total (DISSIP, lower right). Uniform scaling is applied to all 4 panels. Location of the Virginia wind energy area is depicted by the circled red X. 31

32 Figure 5.2b. SWAN bottom friction (DISBOT, left), and whitecapping (DISWCAP, right) dissipation source term output (m 2 /s) during Hurricane Isabel peak wave heights with exploded scaling to show enhanced detail. Figure 5.3. SWAN Surf Breaking fraction (Log10) during Hurricane Isabel extreme wave heights. Location of the Virginia wind energy area is depicted by the circled red X. 32

33 Figure 5.4. Whitecap dissipation source term wave height comparisons at station 44099, including year 2010 scatter plots (upper panels) and December 2010 time series detail (lower panel). Baseline: CDIP observations; Evaluate: SWAN Hindcast data. Table 5.1. Error Metrics for Whitecap Dissipation Source Term Evaluation Source Term Bias (m) RMS Error(m) R 2 Komen et al Westhuysen et al Whitecap Fraction Estimation As noted above, there are currently no options in SWAN for exporting ocean surface whitecap fraction estimates. This may be rectified in the near future, and additional whitecap dissipation source terms are currently under development by the National Oceanographic Partnership Program (NOPP) on ocean wave modeling (see Tolman et al., 2013 and related articles in special issue). Until that time, however, 33

34 the method of Hanson and Phillips (1999) can be applied. Using wind, wave and whitecap fraction (W) data collected during a stormy winter period in the North Pacific Ocean, Hanson and Phillips showed that the power-law relationship W = 3.4 x 10-3 e w 1.5, where e w is the total energy dissipation by breaking, provides a greatly improved estimate of whitecap fraction W over more conventional wind-based relationships (Wu, 1992). Such a relationship could be applied here, using the output of the whitecap dissipation source term as the energy dissipation input. 6. Cloud Computing (Task 14.6) 6.1 Introduction An operational wave forecasting system requires significant information technology (IT) resources including high Internet bandwidth, high performance computing and sufficient data storage. Traditionally, IT infrastructure and resources would be designed, purchased and housed in a data center, at significant up-front and continuing expense. We proposed instead to implement the operational wave forecasting system using computer resources through the Amazon Web Services (AWS), commonly known as the Amazon cloud. A cloud-based forecast modeling system offers a number of advantages over traditional solutions. Modern cloud-based systems are highly reliable, enable high-speed networking and are able to grow and shrink on demand. Cloud-based computing generally employs a utility-based approach to resource utilization and pricing, resulting in significant cost savings; there is no computer to purchase and maintain, and charges are commensurate with usage. This section describes a successful cloud-based wave forecast demonstration. The demonstration validated the overall cloudbased concept, generated forecast model results for hurricane Isabel and provided performance metrics sufficient to develop an operational cost estimate. 6.2 Cloud Resource Configuration We acquired several cloud resources during the demonstration period and used them as a software development platform, to demonstrate model operation and to estimate operational costs. In the typical usage scenario, one utilizes AWS by first creating a free Amazon user account at A large suite of different services is made available through a web browserbased dashboard. We used the following services for the forecast demonstration. EC2 (Elastic Compute Cloud) - This service provides a means for provisioning computer instances in the cloud. Through a technical interface, one selects desired machine characteristics, e.g. CPU type and count, RAM size and basic data storage options. The desired operating system is also selected. The OS options include different versions of Windows and 34

35 several Linux distributions including Red Hat, SUSE and Ubuntu. Amazon also provides its own Linux distribution, based on Red Hat Linux, which comes with AWS tools pre-installed. We chose this distribution for all instances. Once instances are configured, they may be controlled either through the dashboard or via command-line tools provided free of charge by Amazon. One may launch an instance and leave it running indefinitely or one may start and stop an instance on-demand. Since the cost model is based on hourly usage, this utility-based pricing model can have significant cost savings to the user, depending on the application. EBS (Elastic Block Store) - The EBS provides persistent, reliable data storage. The data is replicated for reliability and is available to any running cloud instance. Although each instance comes with a default amount of storage, larger volumes are typically acquired using the EBS. Internet - AWS is on the Internet backbone for speed, well-suited for the type of distributed forecast system we propose. AWS is also geographically distributed. For example, one may choose to launch instances on the east coast (Northern Virginia) or the west coast (Oregon), perhaps based on proximity to end-users. All model inputs originate from online data providers. The forecasting system will poll those providers to determine when new data sources are available and to retrieve them. For example, the system will use boundary conditions from NOAA s operational WAVEWATCH III model. Also, wind field data will be polled and retrieved from WeatherFlow as new sets become available. Amazon EC2 has bandwidth more than sufficient for this system. CloudWatch - This service provides detailed insight into near-time usage and billing estimates. A graphing utility provides custom graphs and reports on all AWS service utilization on-demand. We used the above collection of services to perform a forecast system demonstration. The demonstration used two cloud instances and a large EBS volume as depicted by Figure 6.1. Once the Broker instance was launched from the AWS dashboard, all work was subsequently carried out by securely connecting over the Internet using SSH (secure shell). These instances are described in more detail below. Figure 6.1. Demonstration Cloud Deployment 35

36 Broker The Broker is a cloud computer instance required to monitor upstream data sources for new data sets, preprocessing data sets, managing operational forecast runs and serving results via the World Wide Web (WWW). We deployed the standard Amazon Linux instance on a single-core, 64-bit machine as the Broker. The instance ran continuously throughout the contract period and served as the generalpurpose machine. Team members were able to securely connect to the Broker, install software development tools using standard Linux package-management tools, access online data sources and interact with the Cluster. Cluster The Cluster is a high-performance, multi-cpu cloud computer instance used to run the operational forecast model. The model software is a parallel code and benefits from each additional CPU used during processing. We deployed the largest standard compute instance EC2 has to offer, known as the c3.8xlarge. This instance has 32 CPUs, 60 GB of RAM and 640 GB of attached storage. Data storage The forecast model generates a moderate amount of data for each forecast. Not all output data must necessarily be retained, operational requirements will dictate specifics. The demonstration did not require a large data store, but a small Amazon Elastic Block Store (EBS) was acquired to gain experience and prove the concept. 6.3 Forecast Demonstration The forecast demonstration was designed to validate the entire forecasting workflow on cloud resources and to estimate operational costs. The forecasting workflow, depicted in Figure 6.2, consists of the following elements: Poll online data repositories for new wind products and NOAA WAVEWATCH III boundary conditions. We successfully demonstrated polling and downloading data from several NOAA and partner repositories using standard ftp, www and OPeNDAP, a web-based scientific data set delivery protocol. Preprocess data sets. We successfully demonstrated using the Broker instance to do various preprocessing methods on data sets, e.g. conversion from source format to that required by SWAN. Preprocessing scientific data often requires 3rd party software libraries as well as custom-written software. These packages, e.g. python and OPeNDAP, were easily installed on the Broker using standard software package management tools. Launch Cluster instance on demand. We successfully demonstrated launching a Cluster instance on demand from the command-line and through a python library provided by Amazon. The proposed operational system will manage the Cluster instance automatically, likely through python code. 36

37 Copy input data and model configuration to Cluster machine. We successfully demonstrated copying the data to the newly launched Cluster instance using the standard secure copy (SCP) Linux command. Run 32-cpu parallel wave model. The SWAN model requires MPI (message passing interface) and FORTRAN software libraries, along with NetCDF and others. Once again, installing these 3rd party codes was done using the yum Linux software package manager. The model was configured for an 8-day hindcast for Hurricane Isabel. The model was successfully executed using MPI on 32 CPUs. Copy output data back to Broker. We again used scp to move results back to the Broker for post-processing and publication. Note that AWS offers many different options for storing, transferring and publishing data sets. As depicted in Figure 6.1, we also successfully tested dynamically sharing the EBS volume as one method of using the data between Broker and Cluster instances. Publish forecast data set. We successfully demonstrated deploying the www server on the Broker and providing http-based access. Figure 6.2. Demonstration Forecast Workflow 37

38 6.4 Operational Cost Estimate AWS provides a rich set of dashboard applications and other methods to determine resource usage and billing estimates. We calculated cost estimates for the three primary cloud resources; the Cluster computing cost, the Broker instance cost and data download cost. In one instance, we ran the Cluster for about 26h at full utilization, running the forecast model in a loop. This test determined an average forecast run time of 102m or 1.7h. Figure 6.3 is a snapshot from a CloudWatch report that covers that test period. The blue line represents CPU utilization, from 0 to 100% and back to 0% when the test was complete. The yellow line represents the AWS charges for that period. CloudWatch also allows the user to download detailed comma-separated data files. With this data set, we verified advertised costs and generated a spreadsheet cost estimator tool for the Cluster, download and data storage resources (see Table 6.1). The cost of transferring data from the Internet into the EC2 is free. Therefore pulling new wind and boundary data sets into the system has no cost. However, there is a cost for transferring data from EC2 back out to the Internet. The costs are defined in terms of GB transferred/month. The first 1 GB is free. Transferring up to 10TB/month is $0.12/GB; next 40TB is $0.09/GB and so on. We estimate we will fall well within the 10TB/month range. For estimation purposes, we assume 20 forecast downloads/day. Figure 6.3. CloudWatch Usage vs. Cost Plotting Tool 38

39 Table 6.1. Cluster, Download and EBS Storage Cost Estimates Cluster instance cost per hour (USD) 1.68 EBS data storage cost per GB-month 0.10 Download rate/gb, up to 10TB/month (USD) 0.12 Forecast run time (hours) 1.7 Forecast data storage (MB) 50 Forecast downloads per day 20 Forecasts per day All Forecasts run time per year (hours) All forecasts storage per year (MB) Total data downloaded per year (MB) 365, ,000 1,460,000 Costs Avg Cluster yearly cost (USD) 1, , , Avg Cluster monthly cost (USD) Avg storage yearly cost (USD) Avg storage monthly cost (USD) Avg download yearly cost (USD) Avg download monthly cost (USD) Total yearly cost (USD) 1, , , Total monthly cost (USD) Estimating costs for the Broker instance is different from the Cluster because the Broker will be constantly in service. AWS provides additional pricing options for this scenario. The recommended Broker option would be to purchase a reserved instance, the i2.2xlarge instance type, for example. A reserved instance is purchased with a one-time cost followed by a reduced hourly rate. In this case, the i2.2xlarge has a one-time cost of $1,288 and $.575 per hour (see Table 6.2). 39

40 Table 6.2. Broker Cost Estimate One time cost (USD) 1, Hourly cost (USD) Yearly cost (USD) 5, Total monthly cost (USD) Conclusion The cloud computing demonstration verified that the Amazon cloud is an effective platform on which to build a DMME wave forecasting system. The services are cost-effective, easy to use, reliable and secure. The scalable nature of AWS, and EC2 in particular, provides a clear path for expansion, should additional forecasting, higher resolution model grids, or more frequent forecasting be required in the future. 7. Forecast Implementation Plan (Task 14.9) Using the knowledge and techniques developed in prior studies as well as in the Modeling System, Validation, and Cloud Computing Tasks in this project, we have developed a plan for implementing the unstructured SWAN wave model in an operational mode to provide multi-day forecasts of wave conditions at the site. There are several components to a robust operational system, including skillful numerical models for wind and wave forcing and high-resolution wave responses, file and data management techniques, and compute resources that are available on demand and with sufficient speed to ensure near real-time forecasting capability. The above-described validation study demonstrates that the model is configured appropriately, which results in and good wave hindcast predictions. The validation simulations were carried out using Amazon EC2 compute resources, with deployment methods and scripts that will be directly used in the real-time operational implementation. The primary task in implementing the operational system is thus in coordinating the various needed real-time inputs to the SWAN wave model, including the operational NOAA NCEP WAVEWATCH III wave spectra output and the wind fields computed by the WeatherFlow weather forecasting system. The overall workflow plan for the operational system is shown in Figure 7.1, and is based on our experiences detailed above in the forecast demonstration section. Generally, each forecast period (e.g., at 0, 6, 12, and 18Z) will begin by checking for the availability of external data sources (NCEP and WeatherFlow forecast output). When both data sources for the current forecast period are available, the data fields will be processed into the specific formats required for SWAN. A package containing the complete simulation (model executables, configuration files, and pre-processing input files) will then be created, compute resources on Amazon EC2 provisioned, and the package deployed for execution. The system will then wait until the results are available, transfer the results back to the host machine for 40

41 verification and validation, and then send the output files for wave heights, periods, and directions to the project s data server. Details of the processing steps are not shown, but many of the needed steps have already been developed in the course of the validation study (such as converting WAVEWATCH III spectra format to SWAN format and reformatting of wind and pressure fields into SWAN format). Additionally, our team (Blanton and Gamiel, in particular) have extensive experience in developing automated software systems that manage environmental and earth sciences computations. We anticipate that, initially, the time between the start of a forecast period and the availability of the detailed SWAN forecast products will be approximately 9-11 hours. This is largely dependent on the availability of the Weather Flow wind and pressure fields, which are available approximately 8 hours after the forecast period start time, due to the high resolution of the WRAMS model. Each SWAN forecast simulation will take approximately 1.7 hours on 32 EC2 cores, with subsequent post-processing of about 1 hour. This results in the 9-11 hour estimate of the project s wave forecast information. Details of the operational wave and wind data sources are given next. Figure 7.1: Workflow for planned operational forecast system. Arrows with dotted lines indicate connections to external data servers. The lower level steps operate on the project s Amazon host machine, transfer files to and from external servers, and deploy simulations to the provisioned EC2 resource. 7.1 Real-time data sources The SWAN model is configured to apply wave spectra along the domain s open boundary and apply 10- meter, 10-minute wind velocity at the surface. In the validation study, we used high-quality wind fields from Oceanweather, Inc. to drive the offshore (WAVEWATCH III) and nearshore (SWAN) wave models, with WAVEWATCH III simulations providing wave energy along the regional model boundary. In the operational forecasting context, we will replace the winds with operational winds from the WeatherFlow Regional Atmospheric Modeling System, and replace the oceanic wave energy spectra with the operational WAVEWATCH III output from NCEP. 41

42 Offshore wave boundary conditions NCEP operates the WAVEWATCH III wave model on several different spatial domains, including the Northwest Atlantic area, shown in Figure 7.2. This figure also shows the VA DMME regional model domain and the locations of the WAVEWATCH III output points for the Wakefield area. NCEP WAVEWATCH III output is posted to a production file server 2, accessible by FTP. A component of the VA DMME forecasting system will look at the NCEP FTP server for new files, acquire them as they become available, and interpolate the WAVEWATCH III wave energy spectra to the SWAN open boundary locations. Figure 7.2: Left) Snapshot of the operational NCEP Western North Atlantic WAVEWATCH III model domain, at the 111th hour forecast from 30 July This shows the wave height and peak wave direction. The WAVEWATCH III forecast simulations are driven by the NCEP Global Forecast System (GFS) model output. Right) Location of the VA DMME forecast domain and operational NCEP WAVEWATCH III output points (blue dots). Wind forcing for operational wave forecasting There are several sources of operational meteorological model products available, including NCEP s NAM and Global Forecast System (GFS) model output. We will use NCEP meteorological products while we develop the fully operational wave model system, since it is easily available and we have methods that handle the GRIB2 3 format on Lambert Conformal coordinate systems. 2 ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/wave/prod/wave.yyyymmdd/bulls.tccz/, where CC is the cycle (00, 06, 12, 18). The Wakefield output spectra are in files named multi_1.nw-akq51.spec. 3 GRIB2 is the GRidded In Binary format typically used for meteorological model output. Version 2 is the most recent format and metadata standard for GRIB, and is easily converted to other formats such as NetCDF. 42