About Coastal and Ocean Modeling Testbed
Coastal waters and lowlands of the U.S. are threatened by sea-level rise, flooding, oxygen depleted “dead zones,” oil spills, and unforeseen disasters. With funding from the IOOS Program Office, strong and strategic collaborations among experts from academia, federal operational centers and industry are forged to create the U.S. IOOS Coastal and Ocean Modeling Testbed (COMT).
The COMT serves as a conduit between the federal operational and research communities and allows sharing of numerical models, observations and software tools. The COMT supports integration, comparison, scientific analyses and archiving of data and model output needed to elucidate, prioritize, and resolve federal and regional operational coastal ocean issues associated with a range of existing and emerging coastal oceanic, hydrologic, and ecological models. The Testbed has enabled significant community building (within the modeling community as well as enhancing academic and federal operational relations) which has dramatically improved model development.
COMT projects are designed to assess the performance of existing models, create new model code and tools, inform and train users, and build a repository of evaluation data sets to expand and improve the modeling capabilities of operational partners and the broader coastal and ocean modeling community.
A crucial component of COMT is cyberinfrastructure, which includes hosting a data server, tools and toolkits to facilitate access to data, models, model input files, and model results (i.e. the IOOS Model Viewer), coordination with NOS development and skill assessment for models transitioning to operations, the IOOS compliance checker, and access to high performance computing resources such as the NOS Coastal Modeling Cloud Sandbox.
For the most up to date information on current projects, please see comt.ioos.us.
Current Projects
Past Projects
- Coastal Waves, Surge and Inundation in the Gulf of Maine
- Coastal Waves, Surge and Inundation in the Gulf of Mexico
- Coupling the National Water Model to the Coastal Ocean
- Northeast Coastal Ocean Forecast System (NECOFS)
- A Unified Framework for IOOS Model Data Access
- Advancing the West Coast Ocean Forecasting System
- Operational Forecast System for the Salish Sea
- Chesapeake Bay Hypoxia
- Gulf of Mexico Hypoxia
- US West Coast Model Intercomparison Project
- Puerto Rico/ U.S. Virgin Islands Inundation
- Cyberinfrastructure
Success Stories
Discover how Coastal and Ocean Modeling Testbed projects have impacted their communities and made a difference.
Gulf Coast Model Helping Government Agencies Study Mercury Contamination
Coupled Ocean Modeling Platform for Physics and Contaminant Exchange
Recipient: Louisiana State University
The EPA and USACOE is collaborating with the LSU team to study sediment mercury contamination at the Patrick Bayou superfund site. The agencies provided LSU with their data to incorporate into the model runs which handle extreme compound flooding and storm events.
Chesapeake Bay Model Provides Local Residents with Dead Zone Reports
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
The Virginia Institute of Marine Science (VIMS) and Anchor QEA operate a real-time Chesapeake Bay Environmental Forecast System (CBEFS) computer model that forecasts environmental conditions in the Bay every day. In the fall of every year, the modeled dissolved oxygen concentrations are used to evaluate the severity of hypoxia over the entire summer relative to the recent past and the long-term record, which currently spans 38 years. These “Dead Zone Reports” have been picked up by local and national media outlets and provide an avenue for both informing the public through sharing the science and wide dissemination of the capabilities of the forecast system.
Chesapeake Bay Model Informs Angler Decision Making
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
The Chesapeake Bay Environmental Forecast System (CBEFS) has a positive impact on saltwater anglers by helping them make better-informed decisions about their fishing activities. Our recreational angler advisory group taught us that for-hire captains use the CBEFS portal 1-2 times daily to plan and direct their fishing effort, and 100% of focus group members said that they use the CBEFS portal to plan their fishing. CBEFS helps anglers become more efficient in their fishing and lessens the likelihood of wasting resources (time, fuel, etc.) to find productive fishing grounds.
Chesapeake Bay Model Tailors Forecasts to Meet Local Stakeholder Needs
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
The Chesapeake Bay Environmental Forecast System (CBEFS) has tailored their alert outputs according to stakeholder needs, including additions of sea nettle time series around nuclear power plants, average wave conditions map for seagrass habitat work, 2020-2023 salinity, temperature, and dissolved oxygen time series for aquaculture evaluations, first order Back River flows for a Johns Hopkins class project, water surface elevation and near-surface currents time series for Dr. Joseph Zhang (VIMS) and NOAA related to the Key Bridge, and 2D high-frequency near-surface currents maps for evaluating use for fate/transport related to the Key Bridge.
Chesapeake Bay Model Reduces Barriers to Data Access
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
This project has helped to substantially reduce barriers to data access by providing the Chesapeake Bay community with enhanced data products. The MARACOOS Chesapeake Bay Portal provides a simplified view of OceansMap, focused on data services most relevant to the Chesapeake Bay. This simplified portal ensures that boaters, fishers, recreators, and other users can analyze the data they need most without sifting through information from other geographic regions. Additionally, the Portal ensures that users can visualize, compare, and analyze the forecast datasets quickly and provides them with tools (e.g., permalinks, animation tools) to easily collaborate or share information with others. The CBEFS forecasts are also available for users on the IOOS Model Viewer.
Chesapeake Bay Model Helps Local Shellfish Gardeners and Commercial Farmers
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
In 2024, Karen Hudson, extension specialist, received an inquiry about salinity levels and the threat to oysters from a waterfront land owner with a shellfish garden. She responded with biological information along with links to the CBEFS tool and how it could be used to track salinity, along with other parameters of interest. The email was forwarded to over 200 waterfront landowners throughout Virginia who have an interest in oysters. CBEFS has also been used to investigate environmental causes of hard clam and oyster mortality events for the commercial industry.
Chesapeake Bay Model Provides Users with Model-Data Comparisons for Hypoxic Volume
Chesapeake Bay Environmental Forecasting System
Recipient: Virginia Institute of Marine Science
Stakeholders from the Chesapeake Bay management community also expressed interest in expanding Dissolved Oxygen (DO) visualizations to include hypoxic volume, the volume of Bay water with DO less than 2 mg/L, which can be continuously tracked by CBEFS in real time throughout the summer. Estimating and tracking hypoxic volume in real time can improve understanding of the severity of hypoxia throughout the summer because the data used to estimate hypoxic volume is collected at most once every 2 weeks and is typically not publicly released for several months after collection because of required quality control protocols. As such, it is not possible to estimate the volume of hypoxic conditions in the Bay from the observed data until after those conditions have passed. An initial webpage and graphics were created to track hypoxic volume throughout the summer and relate the amount of hypoxia to years past. At the request of stakeholders and managers, graphics were then developed and added to the webpage to show model-data comparisons in near real time, comparing the model-estimated and data-estimated hypoxic volume throughout the summer.
Northeast Coastal Ocean Forecast System Studies Impacts of Offshore Wind Development
Northeast Coastal Ocean Forecast System (NECOFS)
Recipient: NERACOOS, The University of Massachusetts Dartmouth
The Bureau of Ocean Energy Management (BOEM) used the hindcasting and forecasting capabilities of NECOFS to provide comprehensive temporal and spatial simulations of wind energy impacts on proposed areas of offshore wind energy development. Additionally, a sub-domain of FVCOM that included proposed wind turbines with a boundary connected to NECOFS was developed.
Northeast Coastal Ocean Forecast System Mitigates Infrastructure Damage for Private Industry
Northeast Coastal Ocean Forecast System (NECOFS)
Recipient: NERACOOS, The University of Massachusetts Dartmouth
General Dynamics Bath Iron Works, located on Kennebec River Estuary, ME, has high concern for sea level rise since their critical infrastructure is at a high risk of flooding. They needed an improved short term 2-4 day water level forecast to prevent economic loss. The NECOFS team has been able to provide forecasts in their direct area to mitigate infrastructure and economic loss for the company.
University of Notre Dame Advances NOAA’s STOFS-2D Global Model
Integrated multi physics water level modelling from global to meter scales (ESTOFS)
Recipient: University of Notre Dame
The University of Notre Dame has played a significant role in advancing the NOAA STOFS-2D Global model. Improvements in 2022 included increased resolution in Alaska, the U.S. West Coast, and overall global mesh advancements. The 2023 upgrade included mesh improvements and enhanced station based water level adjustments. Most recently, the 2024 upgrade included expanding the tidal forcing from 8 constituents to 27 constituents, implementing baroclinic pressure gradient forcing from Global RTOFS, and incorporating hydrologic fluxes from the National Water Model.
LiveOcean Provides Nested Models Tailored to Shellfish Fisherman
LiveOcean Model Enhancement and Transition to Operations
Recipient: University of Washington
Following stakeholder feedback from shellfish fisherman, LiveOcean added high-resolution nested model forecasts focused on Aragonite Saturation State for Willapa Bay and Grays Harbor to the LiveOcean website. In 2023, LiveOcean was featured during an associated Community Event at Clatsop Community College. PI Parker MacCready and Jackson Blalock, the Pacific Conservation District’s Marine and Estuarine Resilience Program Manager, gave a joint talk about the down-scaled LiveOcean model output for Grays Harbor and Willapa Bay. Together they highlighted the ways new model capabilities, like wetting and drying, make the forecasts more accurate and useful for shellfish growers and other local coastal residents.
LiveOcean Contributes to the Pacific Northwest HAB Bulletin Targeted for Resource Managers
LiveOcean Model Enhancement and Transition to Operations
Recipient: University of Washington
The LiveOcean team combines observations of HAB species in the water and on the beach, made by a coalition of Tribal, state, and federal scientists, with short term model predictions of where surface water will go in the next three days. All this information is combined in a HAB Bulletin given to the resource managers so that they have the best possible science available to them in a format they can use.
Rutgers University Collaborates with Vessel Operators to Increase In-Situ Observations in Fishing Zones
Improving data assimilation algorithms for coupling for WCOFS & ECOFS
Recipient: Rutgers University
In 2024, the Rutgers team presented on the impact of fishing vessel collected bottom temperature data on the Doppio model’s data assimilation at the NOAA Cooperative Science Summit in Cape May, NJ, which elicited considerable interest from vessel operators keen to instrument the fleet operating out of NJ ports. This potentially may increase the subsurface in situ observing in active fishing zones significantly.
Documents
Workshop Documents
White Papers and Publications
- Luettich et al, The U.S. IOOS Super-regional Coastal Ocean Modeling Testbed: Implementation and Overview of Findings, Journal of Geophysical Research: Oceans Special Issue, Dec, 2013
- COMT Terms of Reference 120914
- Testbed Overview 120321
Presentations
Related Sites
COMT Publications
COMT 2013 Awardees
- Chen, C., Beardsley, R. C., Luettich Jr, R. A., Westerink, J. J., Wang, H., Perrie, W., & Toulany, B. (2013). Extratropical storm inundation testbed: Intermodel comparisons in Scituate, Massachusetts. Journal of Geophysical Research: Oceans, 118(10), 5054-5073.
- Durski, S. M., Kurapov, A. L., Allen, J. S., Kosro, P. M., Egbert, G. D., Shearman, R. K., & Barth, J. A. (2015). Coastal ocean variability in the US Pacific Northwest region: seasonal patterns, winter circulation, and the influence of the 2009–2010 El Niño. Ocean Dynamics, 65, 1643-1663.
- Fennel, K., Laurent, A., Hetland, R., Justić, D., Ko, D. S., Lehrter, J., … & Zhang, W. (2016). Effects of model physics on hypoxia simulations for the northern Gulf of Mexico: A model intercomparison. Journal of Geophysical Research: Oceans, 121(8), 5731-5750.
- Hope, M. E., Westerink, J. J., Kennedy, A. B., Kerr, P. C., Dietrich, J. C., Dawson, C., … & Westerink, L. G. (2013). Hindcast and validation of Hurricane Ike (2008) waves, forerunner, and storm surge. Journal of Geophysical Research: Oceans, 118(9), 4424-4460.
- Irby, I. D., Friedrichs, M. A., Friedrichs, C. T., Bever, A. J., Hood, R. R., Lanerolle, L. W., … & Xia, M. (2016). Challenges associated with modeling low-oxygen waters in Chesapeake Bay: a multiple model comparison. Biogeosciences, 13(7), 2011-2028.
- Joyce, B. R., Gonzalez‐Lopez, J., Van der Westhuysen, A. J., Yang, D., Pringle, W. J., Westerink, J. J., & Cox, A. T. (2019). US IOOS coastal and ocean modeling testbed: Hurricane‐induced winds, waves, and surge for deep ocean, reef‐fringed islands in the Caribbean. Journal of Geophysical Research: Oceans, 124(4), 2876-2907.
- Kerr, P. C., Martyr, R. C., Donahue, A. S., Hope, M. E., Westerink, J. J., Luettich Jr, R. A., … & Westerink, H. J. (2013). US IOOS coastal and ocean modeling testbed: Evaluation of tide, wave, and hurricane surge response sensitivities to mesh resolution and friction in the Gulf of Mexico. Journal of Geophysical Research: Oceans, 118(9), 4633-4661.
- Kerr, P. C., Donahue, A. S., Westerink, J. J., Luettich Jr, R. A., Zheng, L. Y., Weisberg, R. H., … & Cox, A. T. (2013). US IOOS coastal and ocean modeling testbed: Inter‐model evaluation of tides, waves, and hurricane surge in the Gulf of Mexico. Journal of Geophysical Research: Oceans, 118(10), 5129-5172.
- Kim, S. Y., Kurapov, A. L., & Kosro, P. M. (2015). Influence of varying upper ocean stratification on coastal near‐inertial currents. Journal of Geophysical Research: Oceans, 120(12), 8504-8527.
- Kurapov, A. L., Erofeeva, S. Y., & Myers, E. (2017). Coastal sea level variability in the US west coast ocean forecast system (WCOFS). Ocean Dynamics, 67, 23-36.
- Luettich Jr, R. A., Wright, L. D., Nichols, C. R., Baltes, R., Friedrichs, M. A., Kurapov, A., … & Howlett, E. (2017). A test bed for coastal and ocean modeling. Eos, 98.
- Luettich Jr, R. A., Wright, L. D., Signell, R., Friedrichs, C., Friedrichs, M., Harding, J., … & Baltes, R. (2013). Introduction to special section on the US IOOS coastal and ocean modeling testbed. Journal of Geophysical Research: Oceans, 118(12), 6319-6328.
- Moore, A. M., Jacox, M. G., Crawford, W. J., Laughlin, B., Edwards, C. A., & Fiechter, J. (2017). The impact of the ocean observing system on estimates of the California current circulation spanning three decades. Progress in Oceanography, 156, 41-60.
- Pringle, W. J., Gonzalez‐Lopez, J., Joyce, B. R., Westerink, J. J., & van der Westhuysen, A. J. (2019). Baroclinic coupling improves depth‐integrated modeling of coastal sea level variations around Puerto Rico and the US Virgin Islands. Journal of Geophysical Research: Oceans, 124(3), 2196-2217.
- Scully, M. E. (2016). The contribution of physical processes to inter‐annual variations of hypoxia in Chesapeake Bay: A 30‐yr modeling study. Limnology and Oceanography, 61(6), 2243-2260.
- Wiggert, J. D., Hood, R. R., & Brown, C. W. (2017). Modeling hypoxia and its ecological consequences in Chesapeake Bay. Modeling Coastal Hypoxia: Numerical Simulations of Patterns, Controls and Effects of Dissolved Oxygen Dynamics, 119-147.
- Zheng, L., Weisberg, R. H., Huang, Y., Luettich, R. A., Westerink, J. J., Kerr, P. C., & Akli, L. (2013). Implications from the comparisons between two‐and three‐dimensional model simulations of the Hurricane Ike storm surge. Journal of Geophysical Research: Oceans, 118(7), 3350-3369.
COMT 2018 Awardees
UCSC
- Anderson, C., Newton, J., Ruhl, H., Garfield, T., DeVogelaere, A., Moore, T., & Edwards, C. (2019). West Coast Ocean Forecast System (WCOFS) – Coastal Ocean Model Testbed (COMT) Stakeholder Engagement Workshop Summary Report, https://sccoos.org/wp-content/uploads/2020/12/COMTStakeholderEngagementWorkshop_REPORT.pdf
- Matranga, J. (2021). Thinning algorithms for remote sensing observations in support of ocean data assimilation, M.S. Thesis, University of California, Santa Cruz.
UNC-CH
- Bunya, S., Luettich Jr, R. A., & Blanton, B. O. (2023). Techniques to embed channels in finite element shallow water equation models. Advances in Engineering Software, 185, 103516.
NERACOOS
- Chen, C., Lin, Z., Beardsley, R.C., Shyka, T., Zhang Y., Xu Q., Qi, J., Lin, H., & Xu, D. (2020). Impacts of sea-level rise on future storm-induced coastal inundation over Massachusetts Coast, Natural Hazards, https://doi.org/10.1007/s11069-020-04467-x.
- Chen, C., Zhao, L., Gallager, S., Ji, R., He, P., Davis, C., Beardsley, R.C., Hart, D., Gentleman, W.C., Wang, L., Li, S., Lin, H., Stokesbury, K., & Bethoney, D. (2021). Impact of larval behaviors on dispersal and connectivity of sea scallop larvae over the northeast U.S. shelf. Progress in Oceanography, 195, 102604, https://doi.org/10.1016/j.pocean.2021.102604.
- Grogan, D. S., Zuidema, S., Prusevich, A., Wollheim, W. M., & Glidden, S. (2022). WBM: A scalable gridded global hydrologic model with water tracking functionality. Geoscientific model development discussions, 1(54).
- Li, S. (2022). Development of a coupled FVCOM-WRF model: applications for Hurricane
Sandy. Ph.D. dissertation, University of Massachusetts-Dartmouth, 184pp. - Li, S., Chen, C., Wu, Z., Beardsley, R. C., & Li, M. (2020). Impacts of oceanic mixed layer on hurricanes: A simulation experiment with Hurricane Sandy. Journal of Geophysical Research: Oceans, 125(11), e2019JC015851.
- Wang, D. (2022). Impacts of climate change on seasonal and interannual variabilities of the Beaufort gyre and freshwater content in the Arctic Ocean. Ph.D. dissertation, University of Massachusetts-Dartmouth, 201pp. (Note: Global-FVCOM is used to nest with NECOFS)
- Zhang, Z., Chen, C., Beardsley, R.C., Li, S., Xu, Q., Song, Z., Zhang, D., Hu, D., & Guo, F. (2020). A FVCOM study of the potential coastal flooding in Apponagansett Bay and Clark Cove, Dartmouth Town (MA). Natural Hazards, http://doi.org/10.1007/s11069-020-04102-9.
- Zang, Z., R. Ji, Z. Feng, C. Chen, S. Li, and C. S. Davis, 2021. Spatially varying phytoplankton seasonality on the Northwest Atlantic Shelf: A model-based assessment of patterns, drivers and implications. ICES Journal of Marine Science, fsab102, 1920-1934.
- Zang, Z., Ji, R., Liu, Y., Chen, C., Li, Y., Li, S., & Davis, C. S. (2022). Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine. Limnology and Oceanography Letters, 7(3), 277-285.
COMT 2021 Awardees
VIMS
- Bever, A.J., Friedrichs, M.A.M., & St-Laurent, P. (2021). Real-time environmental forecasts of the Chesapeake Bay: Model setup, improvements, and online visualization. Environmental Modelling and Software, 105036, https://doi.org/10.1016/j.envsoft.2021.105036
- Horemans, D.M.L., Friedrichs, M.A.M., St-Laurent, P., Hood, R.R., Brown, C.W. (2023). Forecasting Prorocentrum minimum blooms in the Chesapeake Bay using empirical habitat models. Frontiers in Marine Science, 10, https://doi.org/10.3389/fmars.2023.1127649
- Horemans, D.M.L., Friedrichs, M.A.M., St-Laurent, P., Hood, R.R., & Brown, C.W. (2024). Evaluating the skill of correlative species distribution models trained with mechanistic model output. Ecological Modeling, 491, 110692. https://doi.org/10.1016/j.ecolmodel.2024.110692
- St-Laurent, P., & Friedrichs, M.A.M. (2024). An atlas for physical and biogeochemical conditions in the Chesapeake Bay, SEANOE, https://doi.org/10.17882/99441
Notre Dame
- Contreras, M.T., Woods, B., Blakely, C., Wirasaet, D., Westerink, J.J., Cobell, Z, Pringle, W., Moghimi, S., Myers, E., Seroka, G., Lalime, M., Funakoshi, Y., Van der Westhuysen, A., Abdolala, A., Ma, Z., Lui, F., Valseth, E., & Dawson, C. (2023). A channel-to-basin scale ADCIRC based hydrodynamic unstructured mesh model for the US East and Gulf of Mexico Coasts, NOAA Technical Memorandum NOS CS 41, National Oceanic and Atmospheric Administration.
UW
- Broatch, E. M., & MacCready, P. (2022). Mixing in a Salinity Variance Budget of the Salish Sea is Controlled by River Flow. Journal of Physical Oceanography, 52(10), 2305-2323. doi:10.1175/jpo-d-21-0227.1.
- MacCready, P., & Geyer, W. R. (2024). Estuarine Exchange Flow in the Salish Sea. Journal of Geophysical Research: Oceans, 129(1). doi:10.1029/2023jc020369.
- MacCready, P., McCabe, R. M., Siedlecki, S. A., Lorenz, M., Giddings, S. N., Bos, J., Albertson, S., Banas, N. S., & Garnier, S. (2021). Estuarine Circulation, Mixing, and Residence Times in the Salish Sea. Journal of Geophysical Research: Oceans, 126(2). doi:10.1029/2020jc016738.
- Morzaria-Luna, H., Kaplan, I. C., Harvey, C. J., Girardin, R., Fulton, E. A., MacCready, P., Chasco, B., Horne, P., & Schmidt, M. (2022). Design and Parameterization of a Spatially Explicit Atlantis Ecosystem Model for Puget Sound. (NMFS-NWFSC-177).
- Sunday, J. M., Howard, E., Siedlecki, S., Pilcher, D. J., Deutsch, C., MacCready, P., Newton, J., & Klinger, T. (2022). Biological sensitivities to high-resolution climate change projections in the California current marine ecosystem. Global Change Biology, 28(19), 5726-5740. doi:10.1111/gcb.16317.
LSU
- Bao, D., Xue, Z. G., Warner, J. C., Moulton, M., Yin, D., Hegermiller, C. A., et al. (2022). A numerical investigation of Hurricane Florence-induced compound flooding in the Cape Fear Estuary using a dynamically coupled hydrological-ocean model. Journal of Advances in Modeling Earth Systems, 14, e2022MS003131. https://doi.org/10.1029/2022MS003131
- Xue, Z. Bao, D., Yin, D, & Warner, J. (2023). A novel dynamically coupled land-river-ocean modeling suite for hurricane-induced compound flooding. Coastal Sediments 2023: The Proceedings of the Coastal Sediments 2023. 2659-2668.
Rutgers - Moore, A. M., Arango, H. G., Wilkin, J., & Edwards, C. A. (2023). Weak constraint 4D-Var data assimilation in the Regional Ocean Modeling System (ROMS) using a saddle-point algorithm: Application to the California Current Circulation. Ocean Modelling, 186, 102262.
- Wilkin, J., Levin, J., Moore, A., Arango, H., López, A., & Hunter, E. (2022). A data-assimilative model reanalysis of the US Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models. Progress in Oceanography, 209, 102919.
