Outline GODAE overview and status Links with Argo Use of Argo data in GODAE Conclusions - perspectives. Argo and GODAE. P.Y. Le Traon with contributions from the GODAE IGST Argo Symposium, November 12-14 2003. The Global Ocean Data Assimilation Experiment.
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GODAE overview and status
Links with Argo
Use of Argo data in GODAE
Conclusions - perspectives
P.Y. Le Traon with contributions from the GODAE IGST
Argo Symposium, November 12-14 2003
The Global Ocean Data Assimilation Experiment
Objective:To provide a practical demonstration of real-time operationalglobal oceanography
Includes the main operational and research institutions from Australia, Japan, the United States, the United Kingdom, France, Norway, Europe
Main demonstration phase : 2003 to 2005 - Consolidation phase 2006 - 2007
Climate and seasonal forecasting, marine safety, fisheries, the offshore industry, Navy applications and management of shelf/coastal areas are among the expected beneficiaries of GODAE
The integrated description of the ocean that GODAE will provide will also be highly beneficial to the research community
Organisation : Patrons group : representatives of agencies, International GODAE Steering Team (IGST) (8 meetings), Project office, Melbourne
Strategic Plan (2001) and Implementation Plan (2002, under revision)
Rationale, required inputs and outputs, evaluation
Implementation: GODAE components, work plan
Main activities (2002/2003)
Pilot projects (Argo, GHRSST)
=> develop a specific component (with a broad perspective) (global in-situ observing system for Argo, global high resolution SST fields for GHRSST)
Prototype systems (N Atlantic, N Pacific, equatorial Pacific, Global)
Continue development of ocean state estimation methodologies
Development of data/product serving capability and product assessment and intercomparison
From prototype systems to GODAE Target
1st prototype (1/3°): operational since January 2001. One bulletin per week since the beginning. Successful demonstration. Multivariate in Fall 2003.
2nd prototype : North Atlantic/Med Sea 1/15° prototype routinely operated since January 2003. A 2° global ocean component added in Spring.
3rd prototype = GODAE target. ¼° global ocean configuration. Summer 2004.
White/black line is the frontal analysis of MCSST observations performed at
NAVOCEANO. Black line represents data more than four days old.
DIADEM/TOPAZ and regional high resolution modelling for offshore applications
Global => Regional => Coastal
(physics and ecosystems)
A new COOP/GODAE pilot project ?
Courtesy of G. Evenssen
An Example of Forecast Skill (1)
60-day Prediction of a Kuroshio Meander
Temperature (°C) at 115m
30 April 1998
29 June 1998
Japan Meteorological Agency COMPASS-K Ocean Prediction System (Kuragano & Kamachi, 2002)
Comparison of NCOM, NLOM and SeaWIFS near the Arabian Peninsula
Global NLOM : Oct 3 2002
Global NCOM: Oct 3 2002
Sep 30-Oct 7 2002
Mixed-layer heat budget: contrasting various climate events
ECCO Lee et al. 2002
Courtesy T. Lee
Data and Product Servers
Specialized servers : SSALTO/DUACS (altimetry), Coriolis (in-situ), etc
GODAE Monterey server established to provide real-time in situ observations, atmospheric forcing, assimilation products
ECMWF analyses/forecasts available through ESSC (Reading University)
Product servers for the different GODAE centers
=> use of OpenDaP (DODS) and Live Access Server (LAS)
In early 2001 several assimilation centres made their assimilation products available on the web in “real-time”:
Mercator, NRL, DIADEM/TOPAZ, FOAM
Intercomparison - Metrics
Distributed via OPENDAP & LAS
Distributed via OPENDAP
Distributed on FTP
Distributed on FTP
Intercomparison/Internal MetricsProduct organised in 4 classes
T, S, U, V, SSH, MLD, BSF, TX, TY, Qtot ,E-P-R
(2D and 3D interpolated fields on regular common horizontal grids and standard reduced depth levels, NetCdf CF format)
July mean salinity at 200m depth (ci=0.2psu)
Ready for the intercomparison
(MERSEA Strand 1 project)
Use of Argo data by GODAE
All GODAE modelling/assimilation centers use Argo data !
Start using T profiles with exisiting assimilation techniques (e.g. XBT) and/or use of Argo data for model validation.
Development of multivariate data assimilation techniques (2002 – on going) :
=> modelling of the background model error (multivariate) covariance
=> crucial for an effective use of Argo data in models
=> and for improving S fields when assimilating T fields
Salinity profile assimilation (2002 – on going)
Impact studies (starting) : results with and without Argo data assimilation
Use of deep velocity data for model validation. Research on velocity/trajectory assimilation.
How to best combine high resolution altimeter/SST data with sparse Argo data is still a research issue. GODAE groups are working on it.
Systematic validation with available T and S profiles
(here two different runs of the first MERCATOR prototype system)
Model-XBT/Argo temperature differences averaged from 100-500m (first six months of 2001)
Red: model is warmer. Blue: Model is cooler than XBT observations.
PSY1v2 : altimetry + Argo (Coriolis) with a fully multivariate scheme versus PSY1v1 (altimetry only)
(see MERCATOR poster – Bahurel et al)
Salinity at 1000 m
Salinity section, 29-10-2003
PSY1V2 : comparison with in-situ data not yet assimilated 22-10-2003
First assimilation increments Aug02
(averaged over upper 300m)
S1 + S2
Use of Argo float velocity to validate intermediate circulation of the Japan COMPASS-K assimilation systemsee posters by Iwao et al., and Kamachi et al.
Impact of salinity data on FOAM
Differences between model and observations yet to be assimilated
T & S assim
Improvement of upper ocean T by assimilating T profiles data
ECCO – T. Lee
Improvement of T relative to unconstrained model at
Rms Misfit (Temperature)
Rms Misfit (Salinity)
No Is Situ
Impact of Argo data on MERCATOR
T and S forecasts vs Argo data not yet assimilated
(2 month period – September/October 2003)
0 2 4 6 8 10 12 14 16 18 20 °C
Merging of altimeter, SST and Argo data using statistical techniques
(see poster Guinehut et al.) – MERCATOR/ARMOR
Instantaneous T field at 200 m (02/10/02) from, (a) individual in-situ T data, (b) synthetic T (i.e. from downward extrapolation of altimetry/SST), (c) the merging of synthetic and in-situ T data and (d) the mapping of in-situ T data.
Observations used to validate the regression method – year 2002
Use of altimetry (and SST) to infer 3D Temperature fields
Rms of the in-situ T fields (red) relative to the Levitus climatology.
Rms of the differences between the in-situ T fields and the synthetic profiles deduced from total SLA (green) and steric SLA (blue), and from a simple (SLA) or a multiple (SLA + SST) regression method.
With simple statistical techniques, about half of variance of T fields can be deduced from altimetry (mesoscale). Results will be improved with advanced data assimilation techniques.
Merging altimetry and Argo : validation with external data sets
Separate Argo data into two sets (1 and 2)
Compare T profiles deduced from altimetry, Argo (set 1) and altimetry+Argo (set 1) with set 2 Argo data
North Atlantic – year 2002
Red: Rms of T field (anomalies/Levitus)
Rms error in predicting subsurface T anomalies using only in-situ profiles from set 1 (Turquoise), using synthetic profiles (Green) and using in-situ combined to synthetic profiles (Blue).
Assimilation techniques should be developed further to better merge the high resolution altimeter/SST data with Argo data (joint GODAE/Argo task)
As GODAE develops, feedback on Argo initial design is expected. Meanwhile it is important to keep the global coverage objective of Argo (GODAE global models are progressively replacing regional models).
Reanalysis activities : require high quality delayed mode data sets
Salinity data required (with/without drift correction)
Deep velocity data and fields from Argo are needed for validation but also for improving the use of altimetry (mean dynamic topography estimation).
Left, altimeter/in-situ SLA scatter plot. Right, altimeter/in-situ SLA differences. Observations correspond to one month of data (26/08/03 25/09/03) and dynamic height are calculated relatively to 700 m. (Unit: cm)
Provides a characterisation of errors and non-steric sea level signals
(should be carried out over long time series)
Impact of salinity errors on Altimetry/Argo comparison (North Atlantic) (Larnicol et al., 2003)
Delta temperature (800m)
Delta salinity (800m)
Surface Sea Temperature 22/10/2002
5 7 9 11 13 15 17 19 21 °C
T field from (a) the XBT section, (b) Levitus, (c) the vertical projection of SLA/SST (d) the combination of synthetic and Argo data. Rms error = 0.5°C to be compared with 1°C if Levitus is used.