Http:// OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements

http://www.mercator.eu.org OSE meeting GODAE, Toulouse 4-5 June 2009

Interest of assimilating future Sea Surface Salinity

measurements from SMOS and Aquarius missions in an

operational ocean forecasting system

Benoît TranchantCERFACS/Mercator Ocean, Toulouse (France)

Charles-Emmanuel Testut, Lionel Renault, Nicolas Ferry

http://www.mercator.eu.org


What can be expected from SSS data products for DA in an operational context?

1. Improving error statistics

• of SSS

• of SST/SSS relationships

2. Improving observation operators with different SSS products

Simplification of observation operator : Resolution in space and time corresponding to the scales resolved by the model (i.e. model dependent)

3. Need for stronger links between data segment operations and data assimilation developments

Impact studies: OSE, OSSE


• The main objectives were to:1. Understand the most efficient way to assimilate SSS satellite data in

order to extract the best reliable information in the context of the Mercator Ocean forecasting system

2. Evaluate the potential impact of two different observing systems3. Know the level of the observation error from which associated SSS

data have a significant influence on the data assimilation system SAM2.

• Method:– Performing Observing System Simulation Experiments (OSSEs) with

simulated AQUARIUS and SMOS SSS data over 1 year (2003).1. Sensitivity studies to level products L2/L32. Sensitivity studies to observation errors

» Accuracy of SMOS level-2 products? Observation errors specification ?

3. Sensitivity studies to observing systems» Relative skills of SMOS and AQUARIUS products?

Incremental benefit of their combination?

OSSE Overview


Main characteristics of SMOS and Aquarius missions

Science Satellite Mission SMOS(ESA)

Aquarius(NASA and the Space Agency of

Argentina )

Scientific Objectives Observation of Soil moisture and SSS

Observation of SSS

Measurements goals - Accuracy of 0.5-1.5 PSU for a single observation- Accuracy of 0.1 PSU for a 10-30 days average and for an open ocean area of 200 km x 200 km

- Global monthly 150-kilometer resolution SSS maps with an accuracy of 0.2 PSU

Temporal and spatial resolutions Global coverage every 3 days and ~45 km resolution

Global coverage every 7 days and ~150 km resolution

SMOS

Aquarius


Simulated SSS L2 products:Instantaneous SSS at pixel scale

Reference data (SSS, SST, W)

TB

Noise on TB

Retrieved SSS, SST, W

Direct model

Inverse model

Auxiliary data (SSS, SST, W)

2

4

6

1

5

7

3

Estimated SMOS L2 SSS

Original SSS from PSY2v1 (1/15°) interpolated on SMOS L2 grid (40kmx40km)

Observation error (noise)

Map of the difference (retrieved – reference SSS) for 10th of January

Reconstructed SMOS L2 SSS error for January 2003

=

+

Algorithm to characterize the L2 SSS error, see Boone et al., 2005 or Obligis et al., 2008.


Characteristics of simulated SSS data

Simulated SSS products computed by CLS

Level (spatial and time resolution)

Observation error range (RMS in PSU)

SMOS L3P Level 3 SMOS (map of 200kmx200km, 10 days)

0,02 - 0,5

SMOS L2P Level 2 SMOS (40kmx40km along tracks , daily) – pixel scale

0,2 - 2,5

Aquarius L2P Level 2 Aquarius (100kmx100km along tracks, 1 daily) – pixel scale

0,1 - 1,5

SMOS L3 SMOS L2 Aquarius L2


• REFERENCE or CONTROL RUN• Hindcast experiment with in-situ, SST and altimeter data assimilation

over 2003.• OPA model: MNATL(1/3°) covering North Atlantic from 20°S to 70°N.• ECMWF daily forcing fluxes

• DATA ASSIMILATION SCHEME: SAM2• Based on a SEEK filter : Reduced Order Kalman Filter (modal space)• 3D multivariate background error covariances: 140 seasonal 3D

modes (ψ,T,S) calculated from an hindcast experiment (7 years)• Innovation vector: FGAT method (SLA and in situ data), observation

operator adapted for largest scales (SST and SSS)

• TRUTH• The native sea surface salinity (SSS) located on the SMOS L2 data

points • The native SSS comes from the North Atlantic and Mediterranean

high resolution (1/15°) MERCATOR OCEAN prototype named PSY2V1 re-sampled at a 1/3° (univariate assimilation of SLA, with a relaxation term to SST and SSS ).

OSSE ingredients


1. The assimilation of Level 2 SMOS products seems to be a better approach than the assimilation of Level 3 SMOS products.

2. The SSS constraint from SMOS L3 has a positive impact. Indeed, in comparison to the REF experiment, this simulation has both slightly reduced the bias and the variance of the difference with the “truth”

Spatial average of the mean in psu (left) and variance in psu2 (right) of difference between three different estimates: control run or REF (red dashed line), SMOS L3 (black solid line), SMOS L2 (blue solid line) and “truth” every ten days during the year 2003 for the overall domain

1.Sensitivity to level products

Va

rian

ce o

f d

iffe

ren

ce(P

SU

2)

Me

an

of

diff

ere

nce

(PS

U)


2.Sensitivity to observation errors

1. The initial observation errors associated with the SMOS L2 products given by CLS (Boone et al., 2005) are satisfactory

2. This level of observation error specification defines a threshold (minimum requirement) to have a significant impact on the MERCATOR operational forecasting system. It allows to reduce the difference from about 0.5 to 0.3 PSU rms.

Variance of difference for S

MO

S L2_x

Over 2003

SMOS L2_22xerror

SMOS L2_11xerror

SMOS L2_0.50.5xerror

10 days unit

Va

rian

ce o

fdiff

ere

nce

(PS

U2 )

Main limitations of results:

The threshold found in this study is only valid for these sets of operational data using these sets of observation errors into the MERCATOR ocean forecasting system (1/3°).

Benoit Tranchant

bias in the labrador sea


The combination of the two L2 Products has a weak impact in comparison to the SMOS L2 simulation

3.Sensitivity to observing systems

Three possible explanations

•The daily data coverage is very different between these two products

stronger constraint of SSS SMOS L2 compared to the AQUARIUS L2 Products.

•The observation error associated to Aquarius L2P is effectively lesser than that of SMOS L2P but not for the same surface.

•The spatial resolution: the Aquarius L2P are only able to constraint the scale associated to the Aquarius grid.

one part of the signal associated to small scales (< 100 km) is not taken into account.

Va

rian

ce o

fdiff

ere

nce

(PS

U2 )

Benoit Tranchant

logical results compared to the previous results


Focus on the data coverage

Each ocean grid point is observed by: • Aquarius measurements every ~7 days• SMOS measurements every ~3 days

The decorrelation scales (in days) corresponding to a time correlation of 0.4 from a re-analysis at 1/3° is generally less than 4 days (atmospheric exchanges) in the North Atlantic.

CONCLUSIONA full coverage in the North Atlantic at a sufficient time frequency (4-5 days) is usefull to the SSS assimilation problem in a eddy-permitting model (spatial resolution <1/3°)

Temporal decorrelation scales from a re-analysis (MNATL 1/3°) over 11 years (Greiner et al., 2004)

day

3.Sensitivity to observing systems

Benoit Tranchant

SSS with a rapid variation of the errorif you want to control it, you have to observe it sufficiently// altimetryif we want to control the E-P fluxes, it would be better it is not the same conclusions fora low resolution model


To sum up

REF SMOS L3 SMOS L2 SMOS L2_0.5 SMOS L2_2. AQUARIUS AQUARIUS+SMOS L2

-- - 20% - 36% - 41% - 20% - 10% - 37%

What is the real gain (%) of assimilating remotely sensed SSS data (comparison to the REF experiment) in term of RMSE

RMSE : The root mean square of error/difference (RMSE) between assimilation experiments and “truth” averaged overall the domain.


Error balance

This figure shows the time evolution of the mean and the RMS of the SSS increment for allexperiments. 1.The mean of the increment is quite close to zero for all the experiments. 2.The RMS of the SSS increment has the same behaviour/amplitude in REF, in SMOS L3 and SMOS L2 . constraint coming from the assimilated SSS (SMOS L3 and SMOS L2) is relatively relevant to improve the SSS increment pattern. 3.These results show :

1. SSS observation error variance and particularly its ratio with regard to the error of the other assimilated data sets seems relatively consistent : there are a compromise between SSS, SLA and SST increments

2. Our scheme takes into account the new SSS constraint coming from another data source even if it is far from other operational data, Contributions of new SSS data to the SSS increment have not disrupted the existing equilibrium between all errors.


What is the best strategy to optimally use the future SMOS and Aquarius data in the context of ocean prediction systems, from the perspective of monitoring the mesoscale ocean circulation?

• The use of the synthetic SMOS L2 product gives satisfactory improvement in the model results, since it provides a measurable impact of the quality of ocean analyses from operational systems.

• The SSS observation error variance as specified by Boone et al., (2005) and particularly its ratio with regard to the error of the other data sets assimilated seems appropriate.

• The impact of the Aquarius L2 Products is weak compared to the SMOS L2 Products. The combination of the two L2 Products had thus a small effect on final results

BUT• Simulated SSS data comes from SSS field relatively far from the other

assimilated data (operational data). • The assimilation system does not correct any fluxes, in particular the E-P

fluxes Underestimate information coming from SSS.

Conclusions


•Tranchant, et al. (2008), Expected impact of the future SMOS and Aquarius Ocean surface salinity missions in the Mercator Ocean operational systems: New perspectives to monitor ocean circulation, Remote Sensing of Environment, 112, pp 1476-1487.

•Obligis et al.. (2008) Benefits of the future Sea Surface Salinity measurements from SMOS. generation and characteristics of SMOS geophysical products, IEEE Trans. Geoscience and Remote Sensing, vol. 46, issue 3, 746-753.

•Tranchant et al.(2008), Data assimilation of simulated SSS SMOS products in an ocean forecasting system, Journal of operational Oceanography, Vol. 2008, No 2, August 2008., pp 19-27(9).

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Http:// OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements