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

Documents

Workshop Documents

White Papers and Publications

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.

For Grantees