SMOS SSS and wind speed

J. Boutin, X. Yin, N. Martin

-Optimization of roughness/foam model-Comparison of new-old ECMWF wind speeds-SSS anomaly in the eastern equatorial Pacific

Thanks to ARGANS for their advices for adapting the L2OS processor for our tests!

-Optimization of roughness/foam models-Comparison of new-old ECMWF wind speeds-SSS anomaly in the eastern equatorial Pacific

Motivation: SMOS Model 1 - ARGO SSS (3-31 August 2010; asc orbits)

versus wind speed (center of orbit)

(Boutin et al., submitted to TGRS, 2011)

Problem at high wind speed!

Adjustment of some parameters of roughness and foam modeling

100.225log ( /2)23 *

0*

1.25( )

kku

S k a kg

Roughness:Omnidirectional wave spectrum Durden & Vesecki,1985 :

Foam coverage (from Monahan & O'Muircheartaigh 1986): 10 exp( 0.0861 )cF bU T

a0? Original publication: a0=0.004; DV2, a0=0.008

b? c? original publication: b=1.95×10-5, c=2.55 ; ΔT =Tsea-Tair (neglected in this first step study); in first SMOS SSS1 processing, F=0: no foam.

Foam emissivity (Stogryn, 1972): assumed to be correct

~0.2K/m/s

Dinnat et al., IJRS, 2002, Radio Science, 2003

At 15°C, a 0.1K Tb variation can be generated by :

-0.2pss SSS variation or

- 0.5m/s wind speed variation

10m equivalent neutral wind speed (m/s)

Nadir

Th_30°

DV2

3m/s<U<7m/sWave spectrum parameter

a0 (prior=0.004 – 0.008)

8m/s<U<17m/sFoam coverage model

b, c

Model parameters fitting

Wind induced component of emissivity fitted from SMOS data corrected from flat sea emission, atmospheric effects, galactic noise, Faraday rotation

(20-55° in step of 5°)Incidence angle (°)

Incidence angle (°)Radiometric accuracy Along track in the AFFOV

0

5K

0

5K

SMOS Tbs: Tbs along track (~ no mixing of polarization) in the AFFOV (good radiometric accuracy) from 19 ascending orbits in August (low galactic noise) in the South Pacific (far from land) from 50°S to 0°N – Inciden

ce angles from 20° to 55°

SMOS data used in the fit

19 half orbits of SMOS in the southern Pacific. 2 sources of wind speeds considered: SMOS-ECMWF and/or SSMI from RemSS:

1. ECMWF WS2. ECMWF WS with the differences between ECMWF and SSMI WS restricted to

be less than 2 ms-1

3. SSMI WS colocated at +-0.5h +-50km

Wind Speed data

Wave spectrum

a0

(original 0.004; 0.08 used in

SMOS model 1 defined befo

re launch)

Foam coverage exponent

b

(original 1.95×10-5; not used in SMO

S model 1 defined before launch)

Foam coverage abc

issa

c

(original 2.55; 0 in SMO

S model 1 defined before

launch)

M1 (ECMWF; N~23

7500)0.0050 2.42×10-8 4.86

M2 (ECMWF only if E

CMWF-SSMI WS <2m/

s; N~127000)

0.0062 2.20×10-9 5.67

M3 (SSMI; N~137000) 0.0070 2.90×10-9 5.51

Fitted parameters

Comparison of SMOS-foam coverage model with existing parametrizations

(all ECMWF-SMOS WS)

(ECMWF only if WS consistent with SSM/I)(SSM/I)

X X X XX X X

X X XX

X X

XX

X XX

X

Pre-launch model 1

SMOS data +/-1std

New rough/foam model 1

SMOS measured and simulated emissivity per ECMWF wind speed clas

ses

H & V and various incidence angles in

AFFOV

H pol. 20° V pol. 20°

H pol. 30° V pol. 30°

H pol. 40° V pol. 40°

H pol. 50° V pol. 50°

H pol. 55° V pol. 55°

ECMWF WS ECMWF WS

Comparisons w.r.t climatology (similar to ARGO analysed map)

Old model 1 (DV2)

New parametrization for roughness and foam coverage

Monthly averages,Ascending orbits, Pacific Ocean,August 2010

SSS North-South profile,

1. The tropical Southern Pacific ocean (20°S10°S- 140°W110°W) far away from continent and island characterized by relative stable moderate wind speed and high SST; mean (standard deviation) of SST and SSS are 24.5 (1.0) °C and 36.2 (0.3) pss

2. The high latitude Southern Pacific ocean (50°S45°S- 180°W100°W) characterized by very variable wind speed and low SST; mean (standard deviation) of SST and SSS are 9,8 (1.8) °C and 34,4 (0.2) pss

Comparison with ARGO SSS

SMOS SSS retrieved with the pre-launch model 1

SMOS SSS with the new model M1

in red for the tropical Southern Pacific and in green for the high latitude Southern Pacific

Comparison with ARGO measurementsAugust; ascending orbits

Less biases than pre-launch model at high wind speed but still large scatter: We trust more retrievals between 3 and 12m/s (=> flag in L2OS processor)

Summary

SMOS data evidence that Tb(U) is non linear

A reasonnable fit to SMOS data is obtained when introducing a foam coverage parametrization close to Monahan and Muircheartaigh (1986), (this foam coverage may be peculiar to L-band and depends on the foam emissivity model)

Parameter for the DV wave spectrum (a0) equal 0.005 (DVx2 replaced by DVx1.25!)

New model to be introduced in next release of L2OS processor

-Optimization of roughness/foam models-Comparison of new-old ECMWF wind speeds-SSS anomaly in the eastern equatorial Pacific

1. L2OS V317 and 5 half orbits in May 2011(only one half orbit shown here as an example)

2. New roughness/foam model 1 (Yin et al. submitted to TGRS 2011, presented before); 1.5 m/s error on wind components

3. Only grid points in 50S-0S within ±300km of the swath center and flagged as good quality have been used

Influence of new ECMWF wind speedsData and Methods

Old and new ECMWF wind speed

Wind speed (new-old) (m/s) Wind speed (new) (m/s)

Orbit: 20110512T152208_20110512T161609

Orbit: 20110512T152208_20110512T161609

Std=0.51m/s Std=0.72m/s

Retrieved wind speed (new –old)

ECMWF wind speed (new-old)

SMOS retrieval not able to correct for whole large WS errors (otherwise (new-old) retrieved WS=0)) but correct part of them: over 5 orbits: std(new-old)retrieved WS / std(new-old) ECMWF WS =0.7

WS retri(new-old), med = -0.09SSS retri(new-old), med = -0.033, std=0.25

Orbit: 20110512T152208_20110512T161609

New ECMWF WS leads to changes in SSS; improvement in retrieved SSS still needs to be assessed by comparison with ground truth (5 orbits, not enough because of high noise).

-Optimization of roughness/foam models-Comparison of new-old ECMWF wind speeds-SSS anomaly in the eastern equatorial Pacific

SystematicSMOS ErrorThere:Roughness ?SST ?

From N. Reul talk at EGU 2011

From Boutin, Lorant et al. :SMOS SSS > ARGO SSS together with

SSMI WS<SMOS retrieved WS < ECMWF WS

ECMWF WSSSMI WS

SSSsmos-SSSargo : 1.578 psu WSssmi : 1.8 m/sWSecmwf : 6.85 m/sWSsmos: 4.23m/s(time difference : -0.306 h)

(August, ascending orbits, S. Pacific, new direct rough/foam model)

SMOS SSS <300km ECMWF-SMOS WS

Equator SSS anomaly corresponds to a SMOS retrieved wind speed lower than ECMWF

1 month (170 orbits in Pacific Ocean)

SMOS, with WS retrieved

Monthly SMOS SSS (center swath +/-250km)

SMOS, without retrieving WS ARGO/ISAS

Monthly wind speed

ECMWFSSMI radiometric WS (RSS monthly product)(SSMI 19/22/37 GHz)

SMOS retrieved WS

Radiometric wind speeds lower than ECMWF in that region caracterized with large surface currents

2-31st August SSS latitudinal profiles (asc. orbits only)

Center track+/-300km

Border trackOutside +/-300km

SSSsmos-climatology

center border

Wind Pb

Why?Could it be an artefact of LO cal?

LO cal latitudinal distribution superimposed on SSS latitudinal

biases (from Guillermo)

-1psu

1psu

Summary

SMOS data evidence that Tb(U) is non linear => new model including foam

SMOS retrieved wind speed within new and old ECMWF wind speeds (the retrieval corrects for part of the ECMWF inaccuracies)

SMOS retrieved wind speed is partly (but not entirely) corrected for inconsistencies between ECMWF wind speeds and radiometric wind speeds => it remains flaws in SMOS SSS when large WS discrepancies => when looking at SMOS SSS anomalies; check first the consistency between ECMWF and SMOS retrieved WS!

Need to find a tradeoff between giving more freedom to the WS in the retrieval and not degrade SSS

Possible artifacts linked to LO calibration frequency needs to be better looked at

30/5-1/6-2011 QWG 5

How to deconvolve SMOS data in order to estimate bistatic coefficients for galactic

noise correction? Limits of feasibility?

Error estimates?

Jean-Luc Vergely (ACRI-ST)

Jacqueline Boutin (LOCEAN)

Thanks to N. Reul, J. Tenerelli for helpful discussions

30/5-1/6-2011 QWG 5

The Galactic noise problem

Problem: At present none of our theoretical models able to simulate it correctly:

Tenerelli & Reul, 2010

Up to now, empirical determination hampered by problems of SMOS biases, uncertainty in roughnes/foam models etc… but this is improving

30/5-1/6-2011 QWG 510/11-04-2011 Progress meeting

Bistatic coefficient estimation

The galactic noise signal seen in SMOS data comes from sky emission convoluted with bistatic coefficients that depend on the wind speed (the stronger the wind speed, the larger area of the sky significantly affects the scattered galactic noise signal with a lower influence of the specular direction).

Development of numerical simulations for studying : • Feasibility of estimating bistatic coefficients from SMOS data. • Determination of the error on the bistatic coefficient estimator.

Finding SMOS specular measurements with same relative geometry and WS

SMOS noisy simulated data (averaged)

Deconvolution with strong a priori knowledge

Residual TBs

Bistatic coefficients estimated from SMOS simulated data: a first try

Modeled SMOS data using new bistatic coefficients

Assumptions :

Incident galactic noise is not polarized. WEF applied before reflection

Bistatic retrieval :

Non parametric Bayesian approach with a priori correlation length.

SMOS simulated data no noise assuming bistatic coeff.

Retrieved bistatic coefficient

30/5-1/6-2011 QWG 5

SummaryWe presented the methodology we are developping for estimating

scattered galactic noise and its associated error - To identify situations well sampled by SMOS data for tuning theoretical model and for quantifying errors

Preliminary tests based on simulated data indicate that it will better work at low-moderate wind speeds (deconvolution over a smaller region; situations often sampled)

Need for L1 reprocessed data including correct sun correction (otherwise our estimates will be biased by sun!)

Only a piece of studies about galactic noise as it probably won’t cover all the situations, but it will add constraints!

More using SMOS data at future QWG!Given our present knowledge, we recommend a

careful flagging for reprocessing