MARACOOS SEMI-ANNUAL REPORT: 12/1/2012 – 5/31/2013NOAA Award Number NA11NOS0120038 (June 2011 – May 2016)

1) PROJECT SUMMARYThe Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) is implementing a

partnership-based strategy that supports stakeholder needs for sustained regional ocean observation and forecasting in the Mid-Atlantic Bight from Cape Hatteras to Cape Cod. Over the 5-year project duration, MARACOOS is (1) uniting and integrating the organizational activities of MACOORA (established in 2004) and the operational activities of MARCOOS (established in 2007); (2) maintaining and expanding the existing observing, data management and forecasting subsystems focused on the transition from data-generated to model-generated ensemble ocean forecast products that target multiple users; and (3) expanding end-to-end operations across all five regional themes through (a) enhanced education and engagement activities, (b) the leveraging of resources beyond IOOS through expanded Users and Advisory Councils, and (c) the application of IOOS Association (formerly NFRA)-endorsed metrics to measure and demonstrate success.

2) PROGRESS AND ACCOMPLISHMENTSMARACOOS has exploited both successes and lessons learned in moving forward toward its 5-year goals.

Chief among these goals are the aim to expand from observation to forecasts and the aim to expand the suite of valued information products across the 5 MARACOOS themes. In order to achieve these goals and thereby realize the potential envisioned in the 5-year plan, we see increasing user engagement and expanding leveraging of new and existing partnerships as crucial. Simultaneously, traditional performance metrics are being refined to gauge the spatial coverage and temporal reliability of the MARACOOS observations, and new metrics are being developed to assess the accuracy of these observations and our forecast products. Our milestone schedule is found on page 13, Section E of the MARACOOS proposal. Progress towards milestones and metrics for each of the 5 subsystems are outlined first, followed by highlights of significant meetings and events. In sections A-J, the work accomplished during this reporting period to fulfill those milestones is discussed in greater detail.

Milestones and Metrics:Management Subsystem. During this reporting period, bi-weekly board meetings and operations meetings

were held via conference call. The bi-weekly operations meetings included discussions on the Weather Forecast Ensemble Validation, HF-Radar, Gliders, Satellites, DMAC, Ocean Forecasts, Education, and Outreach. This report will complete the bi-annual progress reporting requirement for MARACOOS year 2.

Observing Subsystems: a) Weather Ensemble: The MARACOOS weather ensemble continues to operate regularly and is run twice daily at 00 and 12UTC. WeatherFlow currently utilizes 7 forecast models in the ensemble. b) Satellites: There were 0 days of data acquisition gaps for both the L-band and X-band satellite ground stations. The L-band system at Rutgers was down for 3 due to a hardware and software refurbishment in May, however, the University of Delaware system redundancy allowed for continued acquisition during these days. c) HF-Radar: The MARACOOS metric for long range data coverage and availability has surpassed the 80/80 metric goal (80% coverage 80% of the time) with an 80/90. This is thanks in large part to the efforts of the MARACOOS HF-RADAR technician’s field work after the destruction left behind by Superstorm Sandy. d) Gliders: Globally MARACOOS deployed 9 gliders for a total of 346 days in water days covering 7148km. In the MARACOOS region there were 3 deployments totaling 93 in water days covering 1781 km.

DMAC and Education and Outreach Subsystems: The uptime for observations and model data in the MARACOOS Data Center continues to be >98% and the auto-monitoring system provides feedback on most of the data feeds.

Modeling Subsystem: The 4-cycles-a-day operational NYHOPS forecast system at Stevens Institute of Technology was 100% operational during this 6-month reporting period. The HOPS model continued to make weekly nowcasts/forecasts (Wednesdays) and was available during 92% of the reporting period. Two forecasts were delayed due to weather related computer shut downs and two for personnel issues. The ROMS Espresso model was operational 100% of the time.

Meetings/Engagement Activity Highlights: During the reporting period, MARACOOS was engaged in a variety of meetings, including reaching out to a broad set of agencies, including NOAA’s National Weather

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Service and the National Centers for Environmental Prediction, NOAA’s National Marine Fisheries Service, NOAA’s National Ocean Service, as well as in discussions to build on existing and/or develop new relationships with the U.S. Army Corps of Engineers, EPA, Department of the Interior, MARAD, and FDA.

Additionally, MARACOOS and its partners have engaged with national and international organizations to represent MARACOOS and IOOS capabilities, as well as to proactively search for opportunities to partner and leverage existing funding streams. Examples of these engagements include IOOS-Association-related activities (IOOS Association Board meeting), Bi-weekly MARACOOS Board meeting conference calls, bi-weekly operations calls, and international meetings. Through its Board, its User Council, and its membership, MARACOOS is continuing to broaden its reach in the region. Activities from this reporting period include: NERACOOS Annual Meeting (December 2012), MTS Tech Surge (December 2012), Fisherman’s Energy meeting (December 2012), MARCO Ocean Portal Meeting (December 2012), AMS conference (January 2013), MARCO collaboration meeting (January 2013), Romer Shoal Lighthouse (NY Harbor entrance) Meeting (January 2013), Partnership for the Delaware Estuary Conference (January 2013), TCS collaboration meeting (February 2013), MD Watermen’s Assn Annual Meeting and Trade Show (February 2013), MA Lobstermen’s Assn Annual Meeting and Trade Show (February 2013), Workshop on Data Visualization to Support Ecosystem Based Fisheries Management (Feb 2013) , Information for Legislative Staffers (March 2013), IOOS Association Board Meetings (March 2013), Coastal America Student Summit (March 2013), Mid-Atlantic Regional Planning Meeting/Workshop (April 2013), CBIBS Partnership Meetings (April 2013), NWS collaboration meeting (May 2013), Industry Workshop Discussion (May 2013), World Ocean Council Smart Ocean/Smart Industries panel (May 2013), University of Delaware Environmental Institute Collaboration meeting (May 2013), Oxford Lab Vibrio Model Meeting (May 2013), MyMARACOOS meeting with RI fishing industry thought leaders to refine product (May 2013), Meeting on CERF/TCS Conference Collaboration with IOOS input (May 2013), Coastal Marine Spatial Planning Meetings (May 2013), MAFMC Habitat Committee meeting (Deep Sea Corals) May 2013, NMFS Protected Resources Group meeting (May 2013), OpenOcean Study Group on Butterfish Assessment (May 2013) , and Meetings with Users and Developers on MARACOOS Website (Dec 2012 – May 2013).

A) Atmospheric Data Integration:   WeatherFlow continues to run a multi-model mesoscale ensemble twice per day at 0 and 12UTC. Production reliability is now at 98%. The number of members in the ensemble and run-by-run availability varies according to the contributing party model run reliability. Currently ensemble members include WeatherFlow RAMS (2km), Rutgers WRF (3km - 0UTC only), NCEP WRF East (5km), NWS Wakefield WRF (5km) and NWS Sterling WRF (4km).

At present WeatherFlow has new hardware in place to allow the addition of NOAA's Assimilation and Modeling Branch 3km High-Resolution Rapid Refresh (HRRR) and NCEP's 4km NAM nest.  WeatherFlow is currently acquiring these runs for inclusion. The hardware shift will facilitate the transition of the ensemble resolution from 4km to 2km. The addition of the HRRR and 4km NAM expands the number of models that include the requested radiation parameters, allowing us to create ensemble values for those parameters. Increasing the number of ensemble members also allows us to generate the ensemble uncertainty through calculation of the variance on ensemble members at each ensemble grid point. Lastly, testing has shown that the inclusion of additional members that cover the entire MARACOOS domain reduces the interior edge effects created by members that cover only part of the domain.

An ensemble visualization interface has been built to visualize ensemble performance. This can be found at http://co.weatherflow.com/maracoos (linked to the MARACOOS web site). Plots are of modeled wind barbs and contoured wind speed with WeatherFlow and CMAN wind barb observations overlaid. They are viewable for the ensemble and each contributing member, for all times out to 24 hours. Animated loops are also available.

WeatherFlow is transitioning the data management tasks to Applied Science Associates THREDDS server. Access to the data and usability remain a focus. Radiation parameters are being provided with some contributing members, and will be added to the ensemble output with the hardware transition. WeatherFlow continues to provide daily statistics on performance of each member, as well as the ensemble itself.

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B) HF-Radar Equipment: The Mid Atlantic High Frequency Radar Network is recovering well from the damage during Hurricane Sandy. Seventeen of the forty-one radars were damaged with four of them being total losses. Ten of the sites suffered antenna damage. The antennas at eight of the stations have been repaired and were operating during this progress period. The antennas at Martha’s Vineyard, MA and Misquamicut Beach, RI are the remaining antennas that need repair. These will be completed in the next progress period. Seven of the stations sustained more severe damage. We were able to get four of the stations operating again (SILD, BELM, PORT and GCAP). The sites HOOK, SEAB and SPRK are still down due to lack of infrastructure at the sites and lack of HF radar equipment to place at the station. The goal is to reestablish these sites in the next progress period with funds from the Sandy Supplemental appropriations bill.

The system that was ordered in the last progress period arrived to MARACOOS. It is currently being used as a spare at the MVCO site. The plan was to install this system at Cape Lookout, NC, but the park service at Cape Lookout did not think they would be able to provide enough power for the system. Mike Muglia (UNC) is going to procure a low power SeaSonde in the coming months for installation at this site. Rutgers has sought permission from East Hampton, NY for installation of the radar. This would fill the gap between MRCH and BLCK. We anticipate installing the radar in the next progress period.

The uptimes of the individual sites in the network are provided in Error: Reference source not found. The long-range average is on par with previous periods. The 13 and 25 MHz network averages are below what we typically deliver. The main cause of the low average is the after effects of Sandy. We hope to correct this in the next progress period.

The temporal and spatial coverage of the long-range network for this reporting period is presented in Figure 1.

C) HF-Radar QA/QC: The MARACOOS HF radar operators have held twice monthly conference calls to exchange information and develop new procedures. We are working on a paper that explains and provides guidance on the first order line settings that are necessary when extracting current information from the Doppler spectra of the SeaSonde. The MARACOOS HF Radar Team is also developing a protocol for the reprocessing of radial data. A reprocessing document has been created that documents the antenna bearing and sea echo phases that are valid for a particular time period. This information is necessary when reprocessing radials from spectra.

We presented and authored several papers using the surface current data during this period. They are:

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Site 12/1/12 - 5/31/13 Site 12/1/12 -

5/31/131 NAUS 74 1 MISQ 02 NANT 100 2 BISL 993 MVCO 0 3 MNTK 04 BLCK 66 4 GCAP 605 MRCH 95 5 STLI 636 HEMP 0 6 PORT 27 HOOK 0 7 SILD 208 LOVE 98 8 CMPT 799 BRIG 93 9 HLPN 2610 WILD 100 10 SLTR 4211 ASSA 98 11 VIEW 9912 CEDR 100 12 CBBT 013 LISL 98 13 CPHN 8614 DUCK 99 14 SUNS 7015 HATY 90 46

74

1 SEAB 02 BELM 03 SPRK 04 BRNT 885 BRMR 606 RATH 997 WOOD 99

49

5 MHz (Long Range)

13 MHz (Mid Range)

25 MHz (Standard Range)

Avg:Avg:

Avg:

Figure 1: Temporal and spatial coverage of the long range network from December1, 2012 to June 1, 2013.

Glenn et al. (2013) “Process-Driven Improvements to Hurricane Intensity and Storm Surge Forecasts in the Mid-Atlantic Bight: Lessons Learned from Hurricanes Irene and Sandy” MTS/IEEE OCEANS13, Bergen, Norway, June 10th – 13th, 2013.

Roarty, Kerfoot, Kohut, Glenn, Whelan, Hubbard (2013) “Improving the Measurements of High Frequency Radar: Reduced Averaging Times and Bistatics” MTS/IEEE OCEANS13, Bergen, Norway, June 10th – 13th, 2013.

O’Donncha, Ragnoli, Suits, Updyke, Roarty (2013) “Uncertainty analysis of numerical model simulations and HFR measurements during high energy events” European Geosciences Union General Assembly 2013, Vienna, Austria, April 7th -12th, 2013.

Figure 2a: Drift prediction for the Cape Hatteras after sinking on April 23, 2013 using 5 MHz HF radar network. Figure 1b: Drift prediction for the Cape Hatteras after sinking on April 23, 2013 using 13 MHz HF radar network.

We continue the work on sending the 25 MHz HF radar data into NOAA PORTS. MARACOOS conducted a teleconference on May 16, 2013 on this topic that included. Teresa Updyke (ODU), Todd Fake (UConn), Hugh Roarty (Rutgers), Chris Paternostro (NOAA) and Greg Dusek (UNC) were present on the call. The NOAA team briefed the MARACOOS team on the status of their analysis. Their analysis has shown that only a 10% data availability is needed in order to calculate tidal harmonics as long as the data gaps are evenly spaced. A 30% data availability is needed in order to calculate tidal harmonics if large data gaps are present in the record. The NOAA team shared the preliminary reports with the group.

The R/V Cape Hatteras (SeaTow) was lost at sea on April 23, 2013, with only the captain on board. Rutgers had used this vessel on countless occasions for glider and mooring operations and for CODAR antenna pattern measurements. We used the surface currents to predict the path of the vessel and any debris. The long-range network (Figure 2a) and standard range network (Error: Reference source not foundb) predicted a drift to the south and then to the west. Todd Fake also provided a Short Term Prediction System (STPS) drift prediction 66 hours out from the sinking. The body of the captain was on May 1 in close proximity to the area predicted by the drifters.

D) Underwater Gliders: Over the last six months there were two glider deployments in the Mid-Atlantic. The first effort was the deployment of a glider in the MAB to sample the winter storm Saturn (Figure 3). The glider was deployed prior to the storm and conducted a 20 day mission spanning 457 kilometers. The glider was rapidly advected south during the storm. Associated with the storm was a large resuspension of particles. This storm will provide another data set that will be used by MARACOOS to study the ocean and atmosphere interactions and their influence on storms.

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MARACOOS satellite operations provide real time locations of coastal water masses and provinces defined by a global standard through the MARACOOS THREDDS server. In a privately funded venture, Glider operations at the University of Delaware have been using these maps to guide sampling of gliders to detect the endangered Atlantic sturgeon. In a demonstration mission, we hypothesized that specific, satellite detected coastal water types are significantly associated with Atlantic sturgeon, providing a potential tool for fisheries management. Since April 10, 2013, University of Delaware glider operations have been guiding a biotelemetry integrated glider through detected water masses to test if they can be used fisheries management (Figure 4).

The MARACOOS glider team has begun planning for the next major effort in the mid-Atlantic which will be to focus on the Fall 2013 transition season. The goal is to collaborate with the IOOS regional associations to north and south and conduct a joint glider mission spanning the majority of the eastern seaboard. This coordinated effort will serve many purposes including 1) provide a regional database to enable IOOS modelers to

assess the utility of the gliders to increase model predictive skill, 2) conduct a coordinated effort providing a template for the national glider backbone, 3) conduct a survey of the northeast system relevant to scale of migrating fish between the RAs, and 4) provide a sustained presence along the shelf during the peak of the summer/fall storm and Cold Pool Water (CPW) transition season. Currently the plan calls for 5 simultaneous glider deployments, 3 within the MARACOOS region. A discussion on the CPW importance and previous history is given below.

During summer, a distinctive, bottom-trapped, cold water mass called the Cold Pool Water (CPW) resides as a swath over the mid to outer continental shelf throughout much of the Middle Atlantic Bight (MAB). This evolving CPW is important because it strongly influences the ecosystem, including several important fisheries. Thus there is a priority to better understand the relevant ocean processes and develop CPW forecast capability. Over the past decade, repeated high resolution Slocum

glider measurements of ocean temperature and salinity (T/S) profiles have been obtained along a coastal New Jersey cross-shelf transect. This effort has helped to define the variability of the CPW structure off of New

Jersey. With recent joint ONR and MARACOOS support, ocean gliders have occupied a series of along-shelf zigzag trajectories from Massachusetts to New Jersey and New Jersey to Maryland. A particularly comprehensive set of 2007 glider measurements enabled us to make the most detailed determination of the Cold Pool evolution ever. In particular, these measurements better define the importance of the May-June inflow of the cold water from the Georges Bank/Gulf of Maine region (see Figure 5) on the early annual definition of the Cold Pool. The issue of inter-annual variability of the Cold Pool water properties is highlighted in a manuscript in preparation by

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Figure 3: Glider surface currents plotted along the track for the winter storm Saturn deployment. Temperature, salinity and backscatter profiles are shown on the right.

Figure 4: UDel Glider OTIS glides through different water masses detecting Atlantic sturgeon.

comparing this 2007 Cold Pool definition with a more limited set of 2012 measurements of a dramatically warmer (~2oC) coastal ocean.

Figure 5 (left) The glider-measured transect-minimum temperature Tmin versus time for the Massachusetts cross-shelf legs (+) and New Jersey legs (o) of a series of zig-zag MURI glider runs in the Mid Atlantic Bight in 2007. The manual fits to the parts of the time series after the initial Apr-June cooling phase, indicate an average June-October 2007 T min

warming rate of 1.05oC for the region. An estimate of temperature envelope of Cold Pool waters for both the May-June and September-October 2007 missions are indicated in red. (right) The Tmin versus Salinity @ Tmin relationship indicates a near-isohaline warming of the core of the Cold Pool (i.e., Tmin for both legs) during 2007. The Cold Pool T-S water property definitions for May-June (red) and the September-October (magenta) 2007 are indicated.

E) Satellites:

Public Data Feeds and Infrastructure UpdatesMARACOOS continues to support expanded satellite coverage from the Gulf of Mexico, to Cuba to

Newfoundland. We utilize de-clouded sea surface temperatures (SST’s) from the Rutgers University L-Band dish and post the data to a publically-available THREDDS server in a Climate Forecast (CF) netCDF format. The MARACOOS CF-compliant SST data feed, which began in 2005, is updated in near-real time (http://tds.maracoos.org/thredds/SST.html). This real-time SST data is also visualized via the MARACOOS Asset Map (http://assets.maracoos.org), in Google Earth (http://modata.ceoe.udel.edu/web_kmzs/), and via browser-based map services (http://orb.ceoe.udel.edu/public-access). The University of Delaware X-Band dish has been brought online to create real-time CF-compliant netCDF4 ocean color data feed, which is being updated in near-real time (http://tds.maracoos.org/thredds/MODIS.html). This data is processed with NRL’s Advanced Processing System. Our ocean color THREDDS feed includes 39 ocean color-related products, including estimates of the inherent optical properties, which are critical for understanding the coastal ocean.

MARACOOS has recently completed MODIS-Aqua mission re-processing through APS to correct known sensor drifts on the MODIS-Aqua platform. The re-processed data is available on the MARACOOS THREDDS server. Additionally the Delaware X-Band dish has also been upgraded to receive VIIRS (MODIS replacement) ocean color data. Data streams from VIIRS are currently being evaluated. The data processing stream has been ported to the University of Delaware Mills Super Cluster for satellite data processing. This has significantly increased the speed of data delivery. Data from the University of Delaware Geo-Stationary satellite receiving is

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available on the web (http://udceoevis1.ceoe.udel.edu/modis/images/gvar/).

Product DevelopmentCoastal Salinity Product: Estimating coastal salinity at high spatial resolution (1km) in the coastal ocean

was a challenge that has been addressed by MARACOOS scientists. Seasonal climatologies of coastal salinity in the MARACOOS region have been developed and are available for research (http://modata.ceoe.udel.edu/dev/egeiger/salinity_climatologies/). NOAA has recently tested this product for operational predictions in the Chesapeake Bay region (http://coastwatch.chesapeakebay.noaa.gov/cb_salinity.html), but found that it was not yet developed enough for daily operational predictions. This salinity product will continue to be developed by MARACOOS scientists.

Sea Surface Temperature for High Resolution Weather Forecasts: RU-COOL has developed a state-of-the-science declouded, coldest pixel composite satellite SST product which is currently being ingested into real-time atmospheric simulations with the Rutgers University version of the Weather Research and Forecasting (RU-WRF). By using a coldest pixel composite (something that has never been done before) the very high resolution (1-km grid spacing) SST product resolves coastal upwelling and cold wakes from storms like Hurricanes Irene and Sandy. These oceanic phenomena have a great influence on the near-surface boundary layer of the atmosphere and the resulting offshore wind resource, which is currently being assessed via RU-WRF simulations for a project funded by the New Jersey Board of Public Utilities.Fisheries Product Development: Developing satellite based products for fisheries support is a major focus for our research group to help us understand the distribution and migration of these living resources. In one example, we now use a unique biome analysis of the real-time ocean color data to produce a product that can be used to identify significantly different water masses and biomes that are in the MARACOOS region. These MARACOOS biome and water mass products were used to drive a real-time regional butterfish model, which was tested by commercial fishermen

(http://ecologyofcoastaloceanseascapes.blogspot.com). The results of this experiment are now being evaluated. In addition to our butterfish work, we are also using satellite based water masses to understand the detection of the endangered Atlantic sturgeon in the Mid-Atlantic. On one mission, we found that Atlantic sturgeon detected by the glider were all in the same satellite derived water mass, suggesting that these fish associate with specific hydrographic regimes (Figure 6). This work was recently published in Fisheries Magazine: (http://www.tandfonline.com/doi/abs/10.1080/03632415.2013.782861#.UbDriWR4ZhI)

This work is now operating experimentally to guide a glider with integrated acoustic sensors to discover the migration path of Atlantic Sturgeon and Sand Tiger Sharks in the MAB. Updates to the

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Figure 6: The glider mission overlaid on the two-week composite of satellite sea surface temperature (a), salinity (b) water mass fronts (c) and the unique water mass in which the glider detected the Atlantic sturgeon (Nov. 2-15, 2011). The horizontal banding in salinity is an artifact of the ocean color sensor design. Mean surface currents (grey arrows, b) show the average movement of water during fish detections.

mission are happening in real time via Facebook: (https://www.facebook.com/OrbLab).

F) Dynamic Models: In March the Rutgers and Stevens Modeling groups met at Stevens to formulate steps to integrate their respective modeling systems. Discussion centered on mutual clarification of how the respective models define sea level datum, and the physics embodied its simulation for storm surge prediction in NYHOPS. As a result it was concluded the NYHOPS group will evaluate using the ESPreSSO model output for open boundary conditions to set the dynamic topography that augments the sub-tidal NOAA ETSS sea level that enters the NYHOPS system. A reciprocal step is that the Rutgers group is evaluating the NYHOPS flows as they exit from New York Harbor with the intent to introduce those output as "data" in the ESPreSSO 4DVAR assimilation system, effectively supplanting the explicit specification of the Hudson River discharge in ESPreSSO.

An experimental ESPreSSO version 2.0 (Ev2) went into real-time operation on Memorial Day weekend incorporating numerous configuration changes that are expected to deliver a much improved bottom temperature estimate on the MAB shelf. Pending the acquisition of subsurface temperature data in the MAB for quantitative skill assessment, an announcement of the availability of Ev2, presentation of Ev2 output on the Assets server, and decommissioning of Ev1 is scheduled for end of 2013.

The 4-cycles-a-day operational NYHOPS forecast system at Stevens Institute of Technology was 100% operational during this 6-month reporting period. During this period, the system was made more secure by replacing some unsecure internet services with more robust ones. Alterations and enhancement in the NYHOPS automation code were made as needed. For example, NYHOPS predictions of coastal currents for USCG SAR operations are now available more securely and earlier than before. In addition, with assistance from NOAA OR&R and USCG, a new NYHOPS webpage has been created and is being tested that makes some of the NYHOPS currents available for simulating pollutant movement and dispersion in a realistic, NYHOPS- and NOAA-GNOME based, tidal environment, for educational and outreach purposes.

In continuation of the investigations on Hurricane Sandy highlighted in the previous report, the NYHOPS system was used to run scenarios of assimilating different atmospheric model forecasts. Within the past 6 months, NYHOPS modelers expanded their investigation on the differences between NYHOPS storm tide predictions subject to envelopes of operationally available atmospheric model forecasts before and during Sandy. They included Penn State University’s operational 36hr-lead-time wind and pressure forecasts to the NYHOPS atmospheric forcing functions to drive the NYHOPS total water level predictions for the historic event. It was found that predictions of storm tide during Sandy were extremely sensitive to which available wind forecast was used and from which model, especially in the NY/NJ Harbor region where peak-water-level differences of 3ft or more were calculated depending on the atmospheric model used to force NYHOPS. The Rutgers-WRF atmospheric model initiated 48hrs before Sandy landfall produced, in this case, the best NYHOPS storm surge forecasts. When compared to atmospheric-hindcast-driven surge-predictions, this research implies a) that uncertainty of storm tide modeling forecasts due to uncertainty in the available atmospheric forecasts of wind and pressure can be quite large, and b) that the simplifications in physics and numerics used in the models is comparable in magnitude to the inherent uncertainty in the evolution of weather itself. That uncertainty could and should then be quantifiable using well- constructed and trained coupled weather/ocean ensembles.

The operational implementation of the Harvard Ocean Prediction System (HOPS) at SMAST (SMAST/HOPS) for MARCOOS has been described in two recent publications in the special issue of Continental Shelf Research on Ocean Observations. These are: 1) An operational ocean circulation prediction system for the western North Atlantic: Hindcasting during July–September of 2006 (Schmidt and Gangopadhyay), which presents a validation and skill quantification of the system using satellite Gulf Stream paths and drifter data from the Shallow Water 2006 experiment; and 2) Multi-scale forecasting in the western North Atlantic: Sensitivity of model forecast skill to glider data assimilation (Gangopadhyay et al.) where Glider and SST data-assimilative system runs during November 2009 were reanalyzed to understand the impact of temperature and salinity assimilation in terms of their spatial and temporal footprints.

Two new improvements of the HOPS system are worth noting. First, the LEVITUS climatology has been replaced by Mid-Atlantic Bight climatology (MOCHA) in the shelf from Cape Hatteras to Nova Scotia. This new implementation is being tested in the operational mode (from March 2013) and in hindcast (2010 and 2011) mode where a skill assessment is being performed to evaluate the performance of the model. Second, the SMAST/

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HOPS SST assimilation is using the glider data in the shelf when available to estimate the mixed layer depth (MLD). This replaces the older computation of MLD using the Ekman depth equation. Furthermore, a short discussion of the weekly forecast has now been posted on the website every week.

A hindcast study of the bottom temperature estimation (from 2007-2012, September to November period) using data and model outputs is under investigation. Data from Federal and State surveys, XBTs, Gliders and multiple model outputs (from SMAST/HOPS, NYHOPS, ESPRESSO) will be combined in a multi-scale topography-constrained objective analysis to produce the daily bottom temperature maps.

G) DMAC: Maintaining and hardening the data server infrastructure for MARACOOS dataIn order to meet the demands of the significant growth in data volumes and to develop a plan towards

hardening the DMAC infrastructure, a new rack server has been purchased and will be located in a commercial server farm with redundant power and connectivity. We will maintain the Cloud instance for the short term to parallel the new dedicated server. The server will contain model and observation archival data sets along with a layer of service technologies including TDS, WMS, and SOS.

The DMAC team continues to work on enhancing data access to new model products, including the WeatherFlow ensemble forecast and new satellite-derived products.

The MyMARACOOS web and mobile site continues to evolve based on user feedback. New data visualizations including a satellite derived fronts product and an experimental bottom temperature product have been added. Other features include new conditions reporting tool, chlorophyll layer, and additional bycatch data.

H) Education and Outreach: The MARACOOS undergraduate education team has been leveraging OOI Education and Public Engagement Funding to develop two online Lab Lesson Builders (LLB) on OOI EPE tools that focus on 1) Development and effects of hurricanes and 2) Ocean acidification. In June 2013, EPE team members will attend a workshop for community college and university professors (Cutting Edge Program) where we will test these labs with 25 teaching faculty. The primary feedback received over the last several months on these tools is that they will fill the need for improved access to real-time data streams to help faculty prepare and utilize these resources. We plan to continue to actively explore ways by which we can build awareness and access to IOOS/OOI data streams. We plan to start development on a coastal inundation unit for the LLB.

I) Economic Benefits: FISHERIES: MyMARACOOS is a decision tool for fishermen (recreational and commercial) with detailed real-time oceanographic information that can be utilized by the public to their economic benefit. Specifically, the identification of frontal zones, optimal bottom and sea surface temperatures, sea state and chlorophyll concentrations can all be used to locate fish more efficiently (and, importantly, avoid unwanted species due to regulatory requirements). This increase in efficiency translates to reduced fuel usage and expense, reduced carbon footprint, and reduced wear and tear to fishing gear, all beneficial outcomes economically and environmentally. For charter boat operations, MyMARACOOS can result in happier customers due not only to increasing the chance for a successful trip but also for forecasting real time sea state conditions  so charter captains can identify and avoid locations where wind, waves and currents interact and can produce discomfort (sea sickness) for paying customers.

In addition, MyMARACOOS now incorporates the most up-to-date grids of regulated bycatch species interactions based on both federal fishery observer data and voluntary industry avoidance programs. Fisheries include: Squid-Butterfish; Scallop-Yellowtail; Sea Herring-River Herring. Additional fisheries will be added as the data is made available.

HURRICANE SANDY FORECASTING, HUMAN LIFE AND SHIPPING: Accurate forecasts from the MARACOOS Ocean and Atmospheric Observatory were integral in reducing economic losses and loss of life during and after Hurricane Sandy in October 2012. Based in part on MARACOOS observations and forecast models, 23,000 TEUs were diverted from the Port of NY/NJ to other east coast ports and then safely delivered via truck and rail avoiding a disruption to merchants during the critical Christmas shopping season. The value of 6,700 containers diverted to Virginia alone was over $1 billion.

NAVY: The US Navy’s Atlantic Fleet Headquarters sortied approximately 80 ships out of Hampton Roads ports and shipyards at a cost of $10 million 2-3 days prior to the onset of 50 knot sustained winds associated with

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Hurricane Sandy. All ships safely exited with no damage or loss of life. Advanced warnings from IOOS-MARACOOS observations were deemed crucial to assisting the Navy with a safe evacuation in a timely and orderly fashion. Based on historic damage to the fleet from past hurricanes, a storm of Sandy’s magnitude otherwise could have caused $500 million in damage to ships and pier facilities. Sustained winds of 50 knots or greater causes damage to both the piers and the ships.

INUNDATION FORECASTING: In Hoboken, NJ 1,700 buildings were severely flooded, impacting up to 34,000 people. These people were spared discomfort, injury or worse by advanced warnings provided by NOAA, from information provided by MARACOOS, specifically the (IOOS) high-resolution storm surge forecasts from Stevens. The Mayor ordered an evacuation of “all ground floor apartments” by midnight Sunday night. This decision saved a significant number of lives, because there are thousands of street-level and basement-level apartments in the city. The Mayor advised against leaving cars in low-lying areas of town – advice that saved thousands of cars from damage, as any car left on all but a few streets in the city would have been submerged.

OIL AND GAS: After the passage of Sandy, Buckeye Partner Pipeline in NJ was the first utility company to come back online, within 48 hours. Per Patrick Hodgins, Houston –based Director, the Buckeye Partners Pipeline was the only utility to use an industry service provider, Impact Weather (www.impactweather.com), to lead their prep work, which started 7 days prior. Impact Weather, like the vast majority of private firms, does not collect their own data but relies exclusively on NOAA-IOOS products and services. In NJ, the MARACOOS Observatory supplies integral data.

J) Video Funding: The period of performance for the production of three short video stories was 7/1/12 – 1/15/13. The Rutgers Film Bureau completed the 2nd IOOS ocean story video (Red Tide) and 3rd IOOS ocean story video (Coast Guard Search and Rescue) and submitted both to IOOS in April and May of 2013. Both films have introductions by Fabien Cousteau. The video production took longer than originally anticipated as Fabien's interview had to be rescheduled because it was scheduled at Rutgers for the evening Hurricane Sandy hit NJ.

3) SCOPE OF FUTURE WORKThere are several additions coming next year (beginning June 1, 2013) to the MARACOOS core operations

work that will be described in detail in the MARACOOS year 3 statement of work to be submitted to Grants Online in July. In summary, they include the additions of funds for: 1) Applied Science Associates work to develop the IOOS data portal, 2) Rutgers and ASA work to begin development on a national Glider DMAC, 3) The first Challenger Mission glider deployment by Rutgers, 4) Ray Toll to work on the IOOS QUARTOD, 5) Caribbean Wind LLC to take over operations of the Chesapeake Bay Integrated Buoy System.

4) LEADERSHIP PERSONELL AND ORGANIZATIONAL STRUCTUREDuring this reporting period, Jay Odell of the Nature Conservancy was appointed to the MARACOOS Board.

5) BUDGET ANALYSISThe total budget for years 1 and 2 is

$4,587,423. Subcontract totals for the 12 subs and Rutgers, the prime, are shown in Figure 7. Current balance remaining for the lead and all subcontracts as of May, 2013, is shown in the last column. Note that many of the subcontractors billing lags several months, so these totals are not indicative of the total spent to date.

MARACOOS Semiannual Report: June 2013 10

Figure 7. MARACOOS years 1 and 2 budget distribution with total and remaining budget listed for the prime and all subcontractors.

# Distribution Year 2 Remaining LEAD Rutgers $1,479,212.00 $51,256.22 1 Applied Science Associates Inc $456,290.00 $23,398.56 2 Center for Innovative Technology $94,458.00 $21,923.51 3 Old Dominion University $228,795.00 $103,807.40 4 Stevens Institute of Technology $175,038.00 $1,285.64 5 University of Connecticut $215,254.00 $35,139.44 6 University of Delaware $897,590.00 $86,372.00 7 University of Delaware $145,587.00 $71,162.27 8 University of Maryland $177,830.00 $49,007.38 9 University of Massachusetts Dart. $467,266.00 $85,589.90 10 University of North Carolina C $17,783.00 $16,646.09 11 University of Rhode Island $56,658.00 $42,187.53 12 WeatherFlow Inc $175,662.00 $28,026.68 Total $4,587,423.00 $615,802.62