Gulf of Mexico NCOM forecasts during the LASER experiment (January-April 2016)
Number of Cold Storage Files:
Cold Storage File Size:
Gulf of Mexico Research Initiative (GoMRI)
Gregg A. Jacobs
Naval Research Laboratory at Stennis Space Center / Ocean Dynamics and Prediction Branch
2016-01-15 to 2016-04-30
Modeling, Forecast, Currents, Temperature, Salinity, Circulation
The model represents the 3D temperature, salinity, velocity and surface elevation of the Gulf of Mexico during the Lagrangian Submesoscale Experiment (LASER) time period from January through April 2016. This dataset includes the Navy Coastal Ocean Model (NCOM). 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. River forcing is based on monthly climatological observed transports.
Jacobs, Gregg A.. 2017. Gulf of Mexico NCOM forecasts during the LASER experiment (January-April 2016). Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7HQ3WZR
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: COAMPS Wind, surface humidity and temperature ﬁles in NRL format. Output ﬁles: NetCDF files containing 3-D velocity, temperature and salinity, surface height and surface forcing fluxes. Latitude (degrees North), Longitude (degrees East), Depth (m), Time (hour since 2000-01-01 00:00 UTC), Tau (hours since analysis), Surface atmospheric pressure (pascal), Model surface temperature flux (deg C m/s), Model surface salinity flux (psu m/s), Model surface shortwave flux (deg C m/s), Model surface roughness (m), Grid x surface wind stress (newton/meter2), Grid y surface wind stress (newton/meter2), Water surface elevation (m), Eastward water velocity (m/s), Northward water velocity (m/s), Vertical water velocity (m/s), Water temperature (deg C), Salinity (psu), Sound speed (m/s)
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). A single-nested domain is constructed for the experiment covering the entire Gulf of Mexico with 1 km horizontal resolution. The domain is forced by boundary conditions from the operational global HYCOM (E. J. Metzger, P. G. Posey, P. G. Thoppil, T. L. Townsend and A. J. Wallcraft, 2015: Validation Test Report for the Global Ocean Forecast System 3.1 - 1/12° HYCOM/NCODA/CICE/ISOP NRL Report NRL/MR/7320--15-9579). The vertical setup uses 72 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. The model experiment is forced by the atmospheric conditions from the COAMPS system (Hodur, R. M. (1997), The Naval Research Laboratory's coupled ocean/atmosphere mesoscale prediction system (COAMPS), Mon Weather Rev, 125(7), 1414-1430. Data assimilation of available satellite and situ observations is conducted (Jacobs, G. A., B. P. Bartels, D. J. Bogucki, F. J. Beron-Vera, S. S. Chen, E. F. Coelho, M. Curcic, A. Griffa, M. Gough, and B. K. Haus (2014a), Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD), Ocean Model, 83, 98-117.) 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 (Jacobs, G. A., B. P. Bartels, D. J. Bogucki, F. J. Beron-Vera, S. S. Chen, E. F. Coelho, M. Curcic, A. Griffa, M. Gough, and B. K. Haus (2014a), Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD), Ocean Model, 83, 98-117.). 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.
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, 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 G. A. Jacobs, B. P. Bartels, D. Bogucki, F. J. Beron-Vera, S. Chen, E. Coelho, M. Curcic, A. Griffa, M. Gough, B. K. Haus, A. Haza, R. W. Helber, P. J. Hogan, G. Huntley,H. E. Ngodock, C. D. Rowley, S. R. Smith, P. L. Spence, P. G. Thoppil and M. Wei, 2014: Data assimilation considerations for improved ocean predictability during the Gulf of Mexico Grand Lagrangian Deployment (GLAD) Ocean Modelling vol 83 doi:10.1016/j.ocemod.2014.09.003 G. A. Jacobs, H. S. Huntley, A. D. Kirwan Jr., B. L. Lipphardt, T. Campbell, T. Smith, K. Edwards, and B. Bartels, 2015: Ocean processes underlying surface clustering Journal of Geophysical Research vol 120 doi://10.1002/2015JC011140
D’Asaro, E. A., Shcherbina, A. Y., Klymak, J. M., Molemaker, J., Novelli, G., Guigand, C. M., … Özgökmen, T. M. (2018). Ocean convergence and the dispersion of flotsam. Proceedings of the National Academy of Sciences, 201718453. doi:10.1073/pnas.1718453115
Haza, A. C., Paldor, N., Özgökmen, T. M., Curcic, M., Chen, S. S., & Jacobs, G. (2019). Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico. Journal of Geophysical Research: Oceans, 124(8), 5844–5869. doi:10.1029/2018jc014813