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HYCOM-TSIS 1/100º Gulf of Mexico Reanalysis Print
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Title: HYCOM-TSIS GOMb0.01
Resolution: 1/100º (~1km)
Domain: Extends from 98ºE to 77ºE in longitude and from 18ºN to 32ºN in latitude
Vertical resolution: 41 hybrid layers
Atmospheric forcing: Hourly CFSR and CFSv2 products
Lateral boundaries conditions: Daily means derived from the global HYCOM GOFS 3.1 reanalysis
Tidal forcing: 5 tidal constituents M2,S2,O1,K1,N2
Data assimilation system: The Tendral Statistical Interpolation (T-SIS) package (Srinivasan et al., 2022)
Institution: Center for Ocean-Atmospheric Prediction Studies (COAPS)
Date/Data Range: 2001-01-01 to Present
Experiment Sequence: expt_01.0 -> expt_02.3
HYCOM version: 2.3.01
Configuration: blkdat.input
Topography: regional.depth.nc, regional.grid.nc, regional.mask.nc

Description of the hindcasts:

The GOMb0.01 domain is set-up with the high resolution 1km bathymetry of the Gulf of Mexico (Velissariou, 2014) over a domain going from 98ºE to 77ºE in longitude and from 18ºN to 32ºN in latitude. With 41-hybrid layers in the vertical, the latest version of the HYCOM model (2.3.01: github.com/HYCOM/HYCOM-src) is forced at the surface with the CFSR hourly atmospheric forcing from 2001 to 2011 and CFSv2 from 2012 onward. The lateral open boundaries are relaxed to daily means of the global HYCOM GOFS 3.1 reanalysis. Five tidal constituents (M2,S2,O1,K1,N2) are applied at the surface through a local tidal potential and at the boundaries with the Browning-Kreiss boundary conditions. Tidal data are extracted from the Oregon State University (OSU) tidal models: the TPXO9 atlas (Egbert and Erofeeva, 2002).

The GOMb0.04 domain and simulation are based on the same bathymetry as the GOMb0.01 but box-car averaged on the 1/25º grid. Initial conditions, atmospheric forcing and boundary conditions are set-up the same way as in the 1km domain.

The Tendral Statistical Interpolation (T-SIS) package (Srinivasan et al., 2022, www.tendral.com/tsis) is used with HYCOM to produce the hindcast. The basic functionality of the package is a multivariate linear statistical estimation given a predicted ocean state and observations. To optimize the system’s performance for the HYCOM Lagrangian vertical coordinate system, subsurface profile observations are first layerized (re-mapped onto the model hybrid isopycnic-sigma-z vertical coordinate system) prior to assimilation. The analysis procedure then updates each layer separately in a vertically decoupled manner. A layerized version of the Cooper and Haines (1996) procedure is used to adjust model layer thicknesses in the isopycnic-coordinate interior in response to SSH anomaly innovations. Prior to calculating SSH innovations, the mean dynamic topography (MDT) is added back into the altimetry observations. A MDT derived from a 20-year free run of the GOMb0.04 configuration is used for converting SLA to SSH. The multi-scale sequential assimilation scheme is based on a simplified ensemble Kalman Filter (Evensen, 2003; Oke et al., 2002) and is used to combine the observations and the model to produce best estimates of the ocean state at analysis time. This state is then inserted incrementally into HYCOM over 9 hours. The analysis is done daily at 18Z.

In the 1/100º, since the observations that are fed to the assimilation system (TSIS) do not have high enough sampling when compared to the grid spacing, the analysis is performed on the 1/25º grid. The GOMb0.01 ocean state is box-car averaged at 1/25º to remove the small-scale variability and given to TSIS as the ocean state. The assimilation system then performs the analysis at 1/25º and provides an increment that is then interpolated back to 1/100º and added to the GOMb0.01 ocean state.

Description of the observations used by TSIS:

The TSIS assimilative system accepts SLA, SST and profiles. For these hindcasts, we assimilate remotely sensed SLA and SST as well as in-situ T/S, considered to be the most reliable observations. Along-track SLA from four operational satellite altimeters (T/P, Jason 1,2, Envisat, GFO and Cryosat) constitute the most important data set for constraining the model. The data are available from Collecte Localisation Satellites (CLS) from January 1993 to present. These data are geophysically corrected for tides, inverse barometer, tropospheric, and ionospheric signals (Le Traon and Ogor, 1998; Dorandeu and Le Traon, 1999). For the sea surface temperature, we use the SST (Foundation Temperature) Level 4 product from NAVOCEANO (GHRSST) and NOAA/NODC (AVHRR) which integrates several individual sensors and provides a gridded field with error estimates. ARGO drifters are also used to constrain the sub-surface density structure when available over the hindcast period.

# Type Provider/Source Frequency Spatial Characteristics
1 Sea Level Anomalies CLS Daily Along track
2 Sea Surface Temperature (Foundation Temperature) NAVOCEANO, NOAA/NODC Daily Gridded
3 Argo-floats argo Daily Point

Outputs available:

Variable Netcdf variable Freq. Resolution Dimensions
Wind (CFSR/CFSv2) wnd_ewd, wnd_nwd Hourly Interpolated to 1/100º Sea Surface (2d.nc)
U and V Velocity u, v Hourly 1/100º 40 Fixed Levels (3z.nc)
Vertical Velocity w_velocity Hourly 1/100º 40 Fixed Levels (3z.nc)
(in-situ) Temperature water_temp Hourly 1/100º 40 Fixed Levels (3z.nc)
Salinity salinity Hourly 1/100º 40 Fixed Levels (3z.nc)
Sea Surface Height ssh Hourly 1/100º Sea Surface (2d.nc)
U and V Barotropic Velocity u_barotropic_velocity, v_barotropic_velocity Hourly 1/100º Vertical-averaged (2d.nc)
Surface Mixed Layer Thickness (dpmixl) mixed_layer_thickness Hourly 1/100º Sea Surface (2d.nc)

Experiment numbers:

YYYY: year, DDD: day, HH: hour, NN: Netcdf type (i.e. 2d or 3z)
010_archv.YYYY_DDD_HH_NN.nc: 2001_001_00 to 2017_152_18
023_archv.YYYY_DDD_HH_NN.nc: 2017_152_19 to *PRESENT*


(Cooper, M., and K. Haines, 1996)
Altimetric assimilation with water property conservation. J. Geophys. Res.,24, 1059–1077.
(Dorandeu, J., and P. Y. Le Traon, 1999)
Effects of global mean atmospheric pressure variations on mean sea level changes from TOPEX/Poseidon. Journal of Atmos. and Ocean. Techn., 16.9, 1279-1283.
(Egbert, Gary D., and Svetlana Y. Erofeeva, 2022)
Efficient inverse modeling of barotropic ocean tides. Journal of Atmos. and Ocean. Techn., 19.2, 183-204.
(Evensen, G., 2003)
The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343—367.
(Le Traon, P. Y. and Ogor, F., 1998)
ERS-1/2 orbit improvement using T/P: The 2 cm challenge, J. Geophys. Res., 103, 8045–8057.
(Oke, P. R., Allen, J. S., Miller, R. N., Egbert, G. D., Kosro, P. M., 2002)
Assimilation of surface velocity data into a primitive equation coastal ocean model. J. Geophys. Res. , 107, doi:10.1029/2000JC000511.
(Srinivasan, A., T.M. Chin, E.P. Chassignet, M. Iskandarani, and N. Groves, 2022)
A statistical interpolation code for ocean analysis and forecasting. J. Atmos. Oce. Tech., 39(3), 367-386, doi:10.1175/JTECH-D-21-0033.1.
(Velissariou, Panagiotis. 2014)
Gulf of Mexico High-Resolution (0.01° x 0.01°) Bathymetric Grid - Version 2.0, February 2013. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7X63JZ5



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