Geophysical Fluid
Dynamics Laboratory

gfdl's home page > products and services > data portal > ocean data assimilation experiment

goals and description

The main goal of this project is to develop a state-of-the-art data assimilation system that incorporates near-realtime data with which we can provide the community a high quality ocean state product. This assimilation system consists of an Ensemble Kalman Filter applied to GFDL's coupled climate model (CM2.1).


The ocean component of the coupled data assimilation (CDA) is the fourth version of the Modular Ocean Model (MOM4) configured with 50 vertical levels (22 levels of 10-m thickness each in the top 220 m) and 1° horizontal B-grid resolution, telescoping to 1/3° meridional spacing by 1° near the equator.

Below is a cartoon from Zhang et al. [2007]. It illustrates how a two-step data assimilation procedure works for updating the estimate of the probability distribution of a single state variable x given a single observation y in the ensemble adjustment Kalman filter (EAKF) under the least squares framework. The right-hand column represents step 1: updating the probability density function (PDF) at the observation location as a new observation comes in (denoted by the thick-dotted arrow labeled STEP 1). The solid arrow 1 denotes that the prior PDF at the observation location is squashed by a new observation (denoted by the bottom-right dashed curve) and the solid arrow 2 represents the shift of the prior ensemble mean at the observation location due to the new observation. The thick-dotted arrow extending from the right-hand column to the left-hand column denotes step 2: using the correlation distribution (shaded region) to distribute the observation increments to impacted grid points. The solid arrow 3 represents the process of updating the PDF of a grid point.



from Zhang et al. [2007]


Time series of the anomalies of the Niño-3.4 ocean temperature for the control (denoted CTL), the ODA (denoted ASSIM), and the truth. Curves in the bottom panel are the vertical averages over the top 250 m for the control (blue), the ODA (red), and the truth (black). The upper (lower) bounds of the control-ODA spread are plotted by the green-dashed (pink dashed) lines in the bottom panel. The control (model climatological) spread is estimated by six 25-yr nonoverlapping time series and the ODA spread is computed by six ensemble members in the filter. All anomalies are computed using the truth's climatology, and the contour interval for the first three panels is 0.5°C. More details about CDA system can be found in Griffies et al. [2004], Zhang et al. [2007] and Chang et al. [2008].

from Zhang et al. [2007]



A marked improvement in the data assimilation's skill is seen when the Argo observational data is included (Chang et al. [2008]). Argo is a global array of 3,000 free-drifting profiling floats that measures the temperature and salinity of the upper 2000 m of the ocean. This allows, for the first time, continuous monitoring of the temperature, salinity, and velocity of the upper ocean, with all data being relayed and made publicly available within hours after collection ( http://www-argo.ucsd.edu).

Positions of the floats that have delivered data within the last 30 days

data access

On the form below, select the "Required Function" to obtain the requested information. "Access Datasets"
provides access to complete files.

File Description	Required Function
Monthly Ensemble Means 1979-2008	Please Select Required Function Access Datasets via OpenDAP Access Datasets via LAS(not available at the moment) Access Datasets via HTTP Access Datasets via FTP

organization of data

The data available online is ocean model ensemble means formulated into monthly time series in one, five and ten
year chunks. The data is available on its native grid (tripolar grid) or interlolated onto a latitude/longitude grid.
CF conventions are followed for the naming of variables. Variables:

• mld_OD- mixed layer depth (m)
• mpe_OF- precipitation minus evaporation (m s-1)
• river_OF- river water flux (m s-1)
• sfc_hflux_OF- surface heat flux (W m-2)
• so_O1- salinity (psu)
• tau_x_OF- zonal wind stress (N m-2)
• tau_y_OF-meridional wind stress (N m-2)
• thetao_01- potential temperature (K)
• uo_01- eastward sea water velocity (m s-1)
• vo_01- northward sea water velocity (m s-1)
• wo_OD- upward Sea water velocity (m s-1)

File naming follows this stencil: variable_StartYearMonth-EndYearMonth.nc. For example, the timeseries
containing the year 1979 potential temperatures would be thetao_O1.197901-197912.nc. The same variable
but timeseries spanning the years 1979 through 1983 would be thetao_O1.197901-198312.nc .

analysis and validation

(from Chang et al. [2008])

Rms temperature erros over the top 1500 m relative to the World Ocean Atlas 2001. Temperature error in deg C
for (left) No-Assimilation (1979-2002)[see the fig. 3 of Gnanadesikan et al., 2006], (center) Assimilation
(1979-2002), and (right) Assimilation (2003-2007) results. Large assimilation effect can be found in the
temperature field both in the 20th and 21st century.

Rms salinity erros over the top 1500 m relative to the World Ocean Atlas 2001. Salinity error in psu for (left) No-Assimilation
(1979-2002)[see the fig. 3 of Gnanadesikan et al., 2006], (center) Assimilation (1979-2002), and (right) Assimilation
(2003-2007) results. Salinity field has been improved again during the Argo era (2003-2007).

Time series of global temperature and salinity
at 15 m, 205 m, 618 m, and 1364 m depth for
the Argo period (2003-2007). Blue (Black,
Green, and Red) lines indicate the results
from GFDL_CDA (WOA01, WOA05, and WOA21c
(OI Argo dataset)). All time series averaged over
the global ocean (0-360, 70S-70N) have been
estimated by sub-sampling based on the
monthly variability of data-sparse regions
of Argo profiles. More detail about data
analysis and assimilation validation can be
found in Chang et al. [2008].

images and animations

WPac Sea surface height anomalies and 10m wind for 1982

Tropical Pacific, ensemble means for the period 1979-1998.      

period	description	format
1982	WPac Sea surface height anomalies and 10m wind	avi
1997	WPac Sea surface height anomalies and 10m wind	avi
1997	EPac Sea surface height anomalies and 10m wind	avi

bibliography

1. Chang, Y.-S., A. Rosati, S. Zhang, and M. J. Harrison, 2008: Objective analysis of monthly temperature sna aslinaity for the world oncean in the 21st century: Comparison with WOA and application to assimilation validation. Submitted to Journal of Geophysical Research

Abstract / PDF


2. Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Monthly Weather Review, 135(10), 3541-3564.

Abstract / PDF


3. Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji, 2005: Initialization of an ENSO Forecast System using a parallelized ensemble filter. Monthly Weather Review, 133(11), 3176-3201.

Abstract / PDF


4. Zhang, S., J. L. Anderson, A. Rosati, M. Harrison, S. P. Khare, and A. Wittenberg, 2004: Multiple time level adjustment for data assimilation.  Tellus, 56A(1), 2-15.

Abstract / PDF


5. Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: A Technical Guide to MOM4. GFDL Ocean Group Technical Report No. 5, Princeton, NJ: NOAA/Geophysical Fluid Dynamics Laboratory, 342 pp.

Abstract / PDF


6. Galanti, E., E. Tziperman, M. Harrison, A. Rosati, and Z. Sirkes, 2003: A study of ENSO Prediction using a hybrid coupled model and the adjoint method for data assimilation. Monthly Weather Review, 131(11), 2748-2764.

Abstract / PDF


7. Rosati, A., and M. J. Harrison, 1997: Ocean modelling and data assimilation at GFDL. In CAS/JSC Working Group on Numerical Experimentation - Research Activities in Atmospheric & Oceanic Modelling, Geneva, Switzerland: WMO/ICSU/IOC World Climate Research Programme, Report No. 25, WMO/TD-No. 792, 8.59 - 8.60.


8. Harrison, M. J., A. Rosati, R. Gudgel, and J. Anderson, 1996: Initialization of coupled model forecasts using an improved ocean data assimilation system. In Preprints, 11th Conference on Numerical Weather Prediction, Boston, MA: American Meteorological Society, 7.


9. Rosati, A., R. Gudgel, and K. Miyakoda, 1996: Global ocean data assimilation system. In Modern Approaches to Data Assimilation in Ocean Modeling, The Netherlands: Elsevier Science, 181-203.

Abstract


10. Pinardi, N., A. Rosati, and R. C. Pacanowski, 1995: The sea surface pressure formulation of rigid lid models. Implications for altimetric data assimilation studies. Journal of Marine Systems, 6, 109-119.

Abstract / PDF


11. Rosati, A., R. Gudgel, and K. Miyakoda, 1995: Decadal analysis produced from an ocean data assimilation system. Monthly Weather Review, 123(7), 2206-2228.

Abstract / PDF


12. Derber, J., and A. Rosati, 1989: A global oceanic data assimilation system. Journal of Physical Oceanography, 19(9), 1333-1347.

Abstract / PDF