Abstract:

This dataset consists of selected modeling outputs in the Gulf of Mexico derived from HYCOM + NCODA Gulf of Mexico 1/25° reanalysis (GOMu0.04/expt_50.1). This dataset was used to evaluate the ability of the existing GoM model (HYCOM) in representing the impact of eddies on subsurface currents. The dataset is made up of daily averaged fields from 1993-01-01 through 2013-12-31 at seven depths, down-sampled to 0.24° horizontal resolution by selecting every sixth observation. The 100-m velocities are selected to represent the upper layer circulation, while the velocities at 1000 m, 1250 m, 1500 m, 2000 m, 2500 m, 3000 m represent the deep layer circulation.

Suggested Citation:

Yingli Zhu. 2020. Daily average velocity fields derived from HYCOM + NCODA Gulf of Mexico reanalysis, 1993-01-01 to 2013-12-31. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/n7-2zxg-x010

Purpose:

Observed subsurface currents have been shown to be impacted by surface mesoscale eddies, but how well this impact can be represented by numerical models needs further study. The purpose of this dataset is to evaluate the models' ability to simulate this impact.

Data Parameters and Units:

u (West-East velocity, [m/s]), v (South-North velocity, [m/s]), depth (Selected model depth, [m]), lon (west longitude, [decimal degree]), lat (north latitude, [decimal degree]), t (time, [decimal years]).

Methods:

The simulation experiment is run with HYCOM. Forcing come from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis. The open boundary conditions are provided by the global HYCOM reanalysis. Navy Coupled Ocean Data Assimilation (NCODA) system uses the model forecast as a first guess in a 3D variational scheme and assimilates available satellite altimeter observations (along-track obtained via the Naval Oceanographic Office (NAVOCEANO) Altimeter Data Fusion Center) satellite and in-situ Sea Surface Temperature (SST), as well as available in-situ vertical temperature and salinity profiles from XBTs, Argo floats, and moored buoys.

Provenance and Historical References:

Cummings, J. A., & Smedstad, O. M. (2013). Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II), 303–343. doi:10.1007/978-3-642-35088-7_13 Cummings, J. A. (2005). Operational multivariate ocean data assimilation. Quarterly Journal of the Royal Meteorological Society, 131(613), 3583–3604. doi:10.1256/qj.05.105 Fox, D. N., Teague, W. J., Barron, C. N., Carnes, M. R., & Lee, C. M. (2002). The Modular Ocean Data Assimilation System (MODAS). Journal of Atmospheric and Oceanic Technology, 19(2), 240–252. doi:10.1175/1520-0426(2002)019<0240:tmodas>2.0.co;2 Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D. and Liu, H., Huang, 25 B., Schemm, J. K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, RW, Rutledge, G., & Goldberg, M. (2010). The NCEP Climate Forecast System Reanalysis. Bulletin of the American Meteorological Society, 91(8), 1015–1058. doi:10.1175/2010bams3001.1

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