Abstract:

The model represents the 3D temperature, salinity, velocity and surface elevation of the Gulf of Mexico during the Grand Lagrangian Deployment (GLAD) time period from July through December 2012. This dataset includes the Global Navy Coastal Ocean Model (NCOM) run B06. This dataset was created by the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE). This research was made possible by a grant from BP/The Gulf of Mexico Research Initiative.

Suggested Citation:

Jacobs, Gregg. 2013. Gulf of Mexico NCOM forecasts during the GLAD experiment (July - December, 2012). Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N72Z13F4

Publications:

Jacobs, G. A., Bartels, B. P., Bogucki, D. J., Beron-Vera, F. J., Chen, S. S., Coelho, E. F., … Wei, M. (2014). Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD). Ocean Modelling, 83, 98–117. doi:10.1016/j.ocemod.2014.09.003

Huntley, H. S., Lipphardt, B. L., Jacobs, G., & Kirwan, A. D. (2015). Clusters, deformation, and dilation: Diagnostics for material accumulation regions. Journal of Geophysical Research: Oceans, 120(10), 6622–6636. doi:10.1002/2015jc011036

Jacobs, G. A., Huntley, H. S., Kirwan, A. D., Lipphardt, B. L., Campbell, T., Smith, T., … Bartels, B. (2016). Ocean processes underlying surface clustering. Journal of Geophysical Research: Oceans, 121(1), 180–197. doi:10.1002/2015jc011140

Wei, M., Jacobs, G., Rowley, C., Barron, C. N., Hogan, P., Spence, P., … Coelho, E. (2016). The performance of the US Navy’s RELO ensemble, NCOM, HYCOM during the period of GLAD at-sea experiment in the Gulf of Mexico. Deep Sea Research Part II: Topical Studies in Oceanography, 129, 374–393. doi:10.1016/j.dsr2.2013.09.002

Beron-Vera, F. J., & LaCasce, J. H. (2016). Statistics of Simulated and Observed Pair Separations in the Gulf of Mexico. Journal of Physical Oceanography, 46(7), 2183–2199. doi:10.1175/jpo-d-15-0127.1

Beron-Vera, F. J., Hadjighasem, A., Xia, Q., Olascoaga, M. J., & Haller, G. (2018). Coherent Lagrangian swirls among submesoscale motions. Proceedings of the National Academy of Sciences, 201701392. doi:10.1073/pnas.1701392115

Beron-Vera, F. J., Olascoaga, M. J., Wang, Y., Triñanes, J., & Pérez-Brunius, P. (2018). Enduring Lagrangian coherence of a Loop Current ring assessed using independent observations. Scientific Reports, 8(1). doi:10.1038/s41598-018-29582-5

Purpose:

To represent the synoptic positions of ocean features larger than about 100km. Features much smaller than this will exist in the model though may not be synoptically placed.

Data Parameters and Units:

Input files: COADS Wind or flux file in NRL format. tau[en]wd zero.[ab] Annual mean wind offset file. Output files: airtmp.[ab] Surface air temperature field in degrees C. precip.[ab] Precipitation field in m/s. radflx.[ab] Radiation heat flux field in W/m2 (positive into ocean). shwflx.[ab] Short-wave radiation heat flux field in W/m2 (positive into ocean). tauewd.[ab] Surface wind stress in N/m2 (tau x, positive eastwards). taunwd.[ab] Surface wind stress in N/m2 (tau y, positive northwards). vapmix.[ab] Water vapor mixing ratio in kg/kg. wndspd.[ab] Wind speed field at 10 m in m/s. The output data sets consist of atmospheric forcing in MKS units. Heat flux is positive into the ocean. The output files consist of the COADS fields interpolated to the HYCOM grid in HYCOM 2.1 array “.a” and header “.b” format.

Methods:

The model is a finite difference forward integration in time. The numerical model is the Navy Coastal Ocean Model (NCOM) (Barron, C.N., A.B. Kara, P.J. Martin, and R.C. Rhodes (2006), Formulation, implementation and examination of the vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM), Ocean Modelling, 11, 347-375.). A single-nested domain is constructed for the experiment covering the entire Gulf of Mexico. The domain is forced by boundary conditions from the operational global NCOM (Barron, C.N., A.B. Kara, P.J. Martin, and R.C. Rhodes (2006), Formulation, implementation and examination of the vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM), Ocean Modelling, 11, 347-375.). The vertical setup is uses with 34 sigma levels >and 16 Z levels beneath (50 total levels). Sigma levels cover the surface to 550m depth, and the Z levels cover the lower water column. The thinnest layer at the surface has a thickness of 0.5m, and deeper layers telescope to the thickest sigma layer of 85m at a depth of 510m. The high resolution in the surface is intended to properly represent submesoscale physics. Both model experiments are forced by the same atmospheric conditions from the Navy Operational Global Atmospheric Prediction System (Rosmond, T. E., J. Teixeira, M. Peng, T. F. Hogan and R. Pauley (2002), Navy Operational Global Atmospheric Prediction System (NOGAPS): Forcing for ocean models, Oceanography, 15, 99–108; J.S. Goerss (2009), Impact of satellite observations on the tropical cyclone track forecasts of the navy operational global atmospheric prediction system, Mon. Weather Rev., 137.) The surface wind stress is determined from the atmospheric model wind velocity. Surface heat fluxes are computed using bulk flux formulations that use the 10-m air-temperature and humidity along with the ocean model SST. Tidal potential forcing is applied to the inner domain, and tidal boundary conditions for water level and barotropic velocity are provided by the Oregon State University global Ocean Tide Inverse Solution (OTIS) (Egbert, G. D., and S. Y. Erofeeva (2002), Efficient inverse modeling of barotropic ocean tides. J. Atmos. Oceanic Tec., 19, 183-204.). Thus, locally generated internal tides are present in the model. Data assimilation is used to produce similar mesoscale structure in the experiment. In the analysis cycle each day, all data over the 24 hours prior to 00Z for the present day are used in the analysis. The analysis is accomplished through a 3D variational (3DVar) approach (Cummings, J.A., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Met. Soc. 131, 3583-3604.). Observation increments are computed by differencing observation values and model forecasts at the same time. The analysis increment is inserted into a 24 hour hindcast by rerunning the model over the prior 24 hours and adding the analysis divided by the number of time steps to the state variables throughout the 24 hour hindcast. This represents a correction to the slowly evolving state field rather than resetting the initial condition at 00Z. Direct insertion of the corrections and resetting the initial conditions can generate spurious internal and inertial waves that, in ocean models, require several days to damp out. The 24 hour forecast then provides the background for the next assimilation cycle. The horizontal covariance length scales used are based on latitudinally varying Rossby radius of deformation and vertical scales are based vertical gradients. The Rossby radius varies from 80 km at the southern extent of the domain to 31 km at the northern extent. A factor of 0.82 is used to scale the Rossby radius to provide the decorrelation length scales in the MVOI resulting in an average decorrelation scale of 45 km. Satellite SSH and SST observations are used to construct synthetic profiles through subsurface covariances (Fox, D. N., C. N. Barron, M. R. Carnes, M. Booda, G. Peggion and J. V. Gurley (2002), The Modular Ocean Data Assimilation System, Oceanography, 15, 22-28.) which are used in the 3DVar. Rosmond, T. E., J. Teixeira, M. Peng, T. F. Hogan and R. Pauley (2002), Navy Operational Global Atmospheric Prediction System (NOGAPS): Forcing for ocean models, Oceanography, 15, 99–108. Barron, C.N., A.B. Kara, R.C. Rhodes, C. Rowley and L.F. Smedstad (2007), Validation Test Report for the 1/8 Global Navy Coastal Ocean Model Nowcast/Forecast System, NRL Tech Report NRL/MR/7320--07-9019, Naval Research Laboratory, Washington, DC. P. J. Martin, C. N. Barron, L. F. Smedstad, T. J. Campbell, A. J. Wallcraft, R. C. Rhodes, C. Rowley, T. L. Townsend and S. N. Carroll, 2009: User's Manual for the Navy Coastal Ocean Model (NCOM) version 4.0 NRL Report NRL/MR/7320--09-9151 P. J. Martin, C. N. Barron, L. F. Smedstad, A. J. Wallcraft, R C. Rhodes, T. J. Campbell, C. Rowley and S. N. Carroll, 2008: Software Design Description for the Navy Coastal Ocean Model (NCOM) Version 4.0 NRL Report NRL/MR/7320--08-9149

Error Analysis:

P. J. Martin, J. W. Book, D. M. Burrage, C. D. Rowley and M. Tudor, 2009: Comparison of model-simulated and observed currents in the central Adriatic during DART Journal of Geophysical Research vol 114, C01S05, doi:10.1029/2008JC004842 A. B. Kara, C. N. Barron, P. J. Martin, L. F. Smedstad and R. C. Rhodes, 2006: Validation of interannual simulations from the 1/8 degree global Navy Coastal Ocean Model (NCOM) Ocean Modelling vol 11, 376-398 P. J. Martin, P. J. Hogan and J. G. Richman, 2013: Comparison of 1-D and 3-D Simulations of Upper-Ocean Structure Observed at the Hawaii Ocean Time Series Mooring NRL Report NRL/MR/7320--13-9443 P. J. Martin, E. Rogers, R. A. Allard, P. J. Hogan and J. G. Richman, 2012: Results from Tests of Direct Wave Mixing in the Ocean's Surface Mixed Layer NRL Report NRL/FR/7320--12-10216

Provenance and Historical References:

P. J. Martin, C. N. Barron, L. F. Smedstad, T. J. Campbell, A. J. Wallcraft, R. C. Rhodes, C. Rowley, T. L. Townsend and S. N. Carroll, 2009: User's Manual for the Navy Coastal Ocean Model (NCOM) version 4.0 NRL Report NRL/MR/7320--09-9151 P. J. Martin, C. N. Barron, L. F. Smedstad, A. J. Wallcraft, R C. Rhodes, T. J. Campbell, C. Rowley and S. N. Carroll, 2008: Software Design Description for the Navy Coastal Ocean Model (NCOM) Version 4.0 NRL Report NRL/MR/7320--08-9149

Individual Files: