The impact of argo data on ocean and climate forecasting
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The impact of Argo data on ocean and climate forecasting. Matt Martin, Mike Bell. Contents: 1. Introduction 2. Data assimilation and Argo data 3. Indirect impact of Argo data 4. Summary. Operational system. Hindcast system. NWP 6 hourly fluxes. T+24 forecast used in QC.

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The impact of argo data on ocean and climate forecasting

The impact of Argo data on ocean and climate forecasting

Matt Martin, Mike Bell


1. Introduction

2. Data assimilation and Argo data

3. Indirect impact of Argo data

4. Summary

1 foam system

Operational system

Hindcast system

NWP 6 hourly fluxes

T+24 forecast used in QC

Obs QC & processing


Forecast to T+120

Automatic verification

Real-time data

Product dissemination

1. FOAM system

FOAM = Forecasting Ocean Assimilation Model

  • Operational real-time deep-ocean forecasting system

  • Daily analyses and forecasts out to 5 days

  • Hindcast capability (back to 1997)

  • Relocatable high resolution nested model capability

Foam model configurations

1° (operational since 1997)

1/9° (pre-operational since April 2002) Data available from

1/3° (operational since 2001)

FOAM model configurations

Operational data assimilation
Operational data assimilation

  • Operational models assimilate:

    • Temperature profiles (including ARGO data)

    • In situ and satellite SST (2.5º AVHRR)

    • Satellite altimeter SSH (Jason-1, GFO, ERS-2)

    • SSMI-derived seaice data from CMC

  • Sequential scheme based upon the Analysis Correction scheme of Lorenc et al. (1991)

  • Operational upgrade implemented on 28th October 2003 includes:

    • Implementation of salinity assimilation

    • Significant developments to original system

    • Upgraded QC of data from ENACT project

2 data assimilation and argo data
2. Data assimilation and Argo data

(a) Impact of salinity Argo data using simple assimilation scheme.

(b) Impact of Argo data in operational models.

(c) Comparison between impact of withholding Argo and other data types.

A investigation of impact of salinity data
(a) Investigation of impact of salinity data

  • Aim: To investigate the impact of the salinity data assimilation prior to implementation

  • 5-month runs of the operational global 1º model

    • Running for Jan - May 2003

    • Forced by 6-hourly NWP surface fluxes

    • Initial state taken from operational model

    • Assimilating only Argo data - no other data types

  • Experiments run:

    • Assimilating temperature and salinity profiles

    • Assimilating temperature profiles only

    • Assimilating salinity profiles only

    • Control run assimilating no data

Results 1





RMS T Error (ºC)

RMS S Error (PSU)










Depth (m)

Depth (m)

T assim

T & S assim


No assim

S assim

Results (1)

  • RMS errors against observations that have not yet been assimilated for final month of integrations over entire globe



Results 2

No assim

T assim

T & S assim

S assim




Results (2)

  • Monthly mean salinity field differences (PSU) from Levitus climatology at 1000m for May (Levitus - model)

B preparation for operational implementation
(b) Preparation for operational implementation

  • Aim: To ensure that new operational system is working correctly and making better use of Argo data

  • Parallel suite trial running since August 2003

  • Upgraded version of operational FOAM suite

    • Global 1º and 1/3º North Atlantic models

    • Running daily at 05:00

    • Accessing only real-time data

    • Analysis and forecast cycle uses new assimilation scheme

    • Initial state from operational models

Impact of salinity data on operational models

New operational model




Impact of salinity data on operational models

  • Salinity data assimilated in upgraded system, but not in previous operational system

  • Salinity differences (PSU) from Levitus climatology on 15th September at 300m (Levitus - model):

Previous operational model

Impact of temperature data on operational models
Impact of temperature data on operational models

  • Temperature data not assimilated below 1000m in current operational system

  • Temperature differences (ºC) from Levitus on 15th September at 1500m (Levitus - model):

Previous operational model

New operational model




C data withholding experiments impact of argo and altimeter data
(c) Data withholding experiments – impact of Argo and altimeter data

  • The reference integration assimilates all data

  • Other integrations withhold selected data types

  • Details of experiment:

    • 1/9º North Atlantic model

    • integrations from Jan – Mar 2003

    • initial state from operational models

    • rms differences calculated using profile data before their assimilation

    • old assimilation scheme used

Impact of withholding argo and altimeter data from foam

Impact of withholding Argo and altimeter data altimeter datafrom FOAM

  • Differences between model and observations yet to be assimilated

  • FOAM 12km N Atlantic model driven by 6 hourly fluxes from Jan-Mar 2003

  • SST, XBT and Pirata data are also assimilated

  • old assimilation scheme

Impact of withholding different data types in seasonal forecasting
Impact of withholding different data types in seasonal forecasting

From ECMWF seasonal forecasting system (HOPE model, OI scheme)

Potential temperature RMS differences from experiment with all data assimilated, 1998-2003

Upper 300m of ocean

From A. Vidard

No moorings

No Argo


3 indirect impact of argo
3. Indirect impact of Argo forecasting

(a) Improved estimation of error covariances.

(b) Mixed layer improvements.

A model error covariances estimated using pairs of observed temperature profiles
(a) Model Error Covariances estimated forecastingusing pairs of observed temperature profiles

  • Use collocated observation and model forecast values to estimate covariance values – bin together to have enough statistical information

  • Assume separability of the error covariance, i.e. horizontal and vertical correlations can be calculated separately.

  • Assume the forecast errors arise from two distinct sources:

    • errors in the internal model dynamics => “mesoscale” errors

    • errors in the atmospheric forcing => “synoptic” scale errors

  • Fit a combination of 2 SOAR functions to the (obs-f/c) covariance values to estimate the variance and horizontal correlation scales of the two forecast error components.

  • The observation error variance is the difference between the total (obs-f/c) mean square error and the total forecast error variance.

  • Modelling the covariance
    Modelling the covariance forecasting

    Schematic of method

    Example at 30W, 40N for SSH.

    Mesoscale length scale = 37km

    Synoptic length scale = 560km

    Circles - (obs-forecast) covariances

    Dotted line - synoptic scale function

    Dashed line- mesoscale function

    Solid line - sum of the two functions

    Temperature profile error covariances
    Temperature profile error covariances forecasting

    Variance Length

    Meso 1.80 47 km

    Synop 0.5 1060 km

    Variance Length

    Meso 0.9 59 km

    Synop 0.1 540 km


    Depth = 55 m

    Depth = 240 m


    Variance Length

    Meso 0.6 40 km

    Synop 0.2 500 km

    Depth = 800 m

    Mesoscale error variances for ssh cm and sst k for 1 3 o foam atlantic model
    Mesoscale error variances for SSH (cm forecasting²) and SST (K²) for 1/3o FOAM Atlantic model

    SST Variance

    SSH Variance

    B 1d mixed layer assessments using argo data
    (b) 1D Mixed Layer Assessments using Argo data forecasting

    • Initialise T & S profiles using Argo observation

    • Use model and surface forcing to integrate forward for 10 days

    • Estimate error in forecast using next Argo observation

    • Run for 1 year for many different Argo floats

    • Use this framework to test assimilation strategy

    Argo ob


    Forecast error statistics

    Surface fluxes



    10 day forecast


    Argo ob

    Assimilation in the mixed layer timeliness of assimilation

    60 forecasting




    10 days

    1 day

    6 hours


    No assimilation

    5 days

    12 hours

    1 hour

    Assimilation in the mixed layerTimeliness of assimilation

    MLD rms errors (m)

    “Kraus-Turner” scheme

    “Large” scheme

    • For Large et al. model, the accuracy of the forecast decreases if the increments are nudged in over more than 1 hour

    • A large number of other factors can also be explored e.g. vertical resolution, time sampling of fluxes

    Summary and future work
    Summary and future work forecasting

    • Argo salinity data assimilation improves salinity fields, even with simple scheme

      • Further work to improve salinity assimilation, i.e. use isopycnal coordinates for analysis

    • Both Argo and altimeter data assimilation improve the fit of the analyses to independent temperature data with Argo data having the largest impact in FOAM

    • Argo data also has significant usage for improving the data assimilation methods used, i.e. error covariances

    • Use Argo data to help improve the assimilation of altimeter data

    • Compare results in FOAM with other centres to make the most of the Argo data