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Ocean studies (v5 reprocessed SSS)-South Pacific Maximum Salinity-North Atlantic Maximum Salinity (SPURS)-Variability of SSS: effects of rain/roughness/interpolation smoothing

Several topicsVersion 6 versus version 5

-v602 in May 2011 (land/ice contamination; ARGO colocations)-v610 in December/descending (sun?)

Ice coverageRain-Roughness effect/ 2-step algo

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Version 6 versus version 5

J. Boutin, N. Martin, X. Yin-L1v602 (land & ice contamination worse in

L1v602 than in v5.5 not shown in this presentation); colocs with ARGO.

-Sun impact on descending orbits in December with L1v610?

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Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed

Land and ice contamination worse in L1 v602!

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Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed

Far from continents (>700km), comparisons with ARGO : colocs +/-9days, +/-50km ; 10 -25 Mai 2011

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Mai 2011 – L1 v602 + L2 v6 versus v5 reprocessed

Far from continents, comparisons with ARGO : colocs +/-9days, +/-50km ; 10 -25 Mai 2011

Orbit A [45S;45N] : v5 : N= 1674 mean=-0.16 std=0.417 v6ref : N= 1674 mean=-0.13 std=0.396 v6a3tec : N= 1674 mean=-0.13 std=0.396

Orbit D [45S;45N] : v5 : N= 1605 mean= 0.04 std=0.448 v6ref : N= 1605 mean= 0.06 std=0.456 v6a3tec : N= 1605 mean=-0.05 std=0.398

Orbit A+D [45S;45N] : v5 : N= 1943 mean=-0.07 std=0.360 v6ref : N= 1943 mean=-0.05 std=0.346v6a3tec : N= 1943 mean=-0.10 std=0.321

L1 v602 gives equal or very slightly improved performance wrt v5; L2 v6a3tec improves much more

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Are descending orbits in the northern hemisphere in December less noisy in v610

than in v5 repr. ?

X. Yin, J. Boutin

Look at std(Tbsmos-Tbmodel) wrt radiometric accuracy over one descending orbit 20121217T032055_20121217T041408

between 5°N and 35°N

Flags of border, sun point and suntails are applied. The gridpoints in the FOV with number of measurements less than 30 are forced to

be blank.

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(in v5, most retrieved SSS north of 5°N in Dec are flagged December/Descending! )

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Std(Txsmos-Txmodel)/Radiometric accuracyV5 V6

V6 less noisy than v5 but still some remaining anomalous signatures

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Std(Tysmos-Tymodel)/Radiometric accuracyV5 V6

V6 less noisy than v5 but still some remaining anomalous signatures

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Std(Txsmos-Txmodel)V5 V6

V6 less noisy than v5 but still some remaining anomalous signatures

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Std(Tysmos-Tymodel)V5 V6

V6 less noisy than v5 but still some remaining anomalous signatures

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SMOS-ECMWF ice coverage wrt OSTIA ice coverage

J. Boutin, N. Martin,C. Banks

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Motivation• At the ESA LPS 2013 Chris Banks suggests that

SMOS was biased close to ice as detected by OSTIA

• In v5 (and v600), in L2OS processor threshold for ice concentration is not put to 0% but to 30%

• =>look at difference between considering a threshold=0% or 30%

• => look at difference between SMOS-ECMWF & OSTIA ice concentration

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Data & Method

• All tests are performed on 15 May 2011• All SMOS-ECMWF ice concentrations along

SMOS orbits on 15 May are averaged

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SMOS-ECMWF & OSTIA coverage around Antarctica

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SMOS-ECMWF – OSTIA ice concentration

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Zoom on some regions of 0% & 30% threshold on SMOS/ECMWF ice concentration

Ice concentration between 30% and 0% concerns often a band of ~1° in latitude or even larger; this is not negligible and from a physical point of view it is necessary to consider 0%!

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SMOS SSS close to ECMWF-SMOS ice mask (purple 0%, black 30%)

Probably due to other sortings, SMOS SSS is not retrieved very close to SMOS-ECMWF ice on 15 May asc orbits

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SMOS SSS close to ECMWF-SMOS ice mask (purple 0%, black 30%)

The southern limit of SMOS retrieval is closer to OSTIA than to ECMWF ice concentration =0%

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Conclusion

Study in progress; some strange across track biases close to ice (TBC) but it was v6!!! It would be safer to put threshold to 0% instead of 30% on percentage of ECMWF ice coverage (although it seems that other tests in L2OS already discard data within 0 and 30%)

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Rain-Roughness effect : what two-step algorithm tells us

X. Yin, J. Boutin, N. Martin

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Two-step retrieval

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rWSOP - WSSSMIS

rWStwostep - WSSSMIS

24

In regions with large ECMWF-SSMI WS differences, 2-step retrieved SMOS WS closer to SSMI WS; 2-step method more sensitive to problems near continents and RFI regions

WSECMWF - WSSSMIS

North Eq PacEast Eq Pac

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rWSOP - WSSSMIS

rWStwostep - WSSSMIS

25

In regions with large ECMWF-SSMI WS differences, 2-step retrieved SMOS WS closer to SSMI WS; 2-step method more sensitive to problems near continents and RFI regions

WSECMWF - WSSSMIS

East Eq Pac region

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Statistics in August 2010 –Ascending orbits(EEP contains low WS that may create an additional problem)

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SMOS SSS – ARGO SSS5°N-15°N Pacific Ocean

Operational SSS Two step SSS

SMOS SSS [1cm]– ARGO SSS [5m] correlated with SSM/I rain rates (within -80mn;+60mn from SMOS passes)

Boutin et al., Ocean Science, 2013

Still strong correlation with RR when an alternative SMOS roughness correction scheme is used (poster Yin et al., 4P-117)

Method described in 4P-117

Change in roughness by raindrop: a second order effect wrt vertical stratification (account for less than 20% of observed SSSsmos-SSSargo difference)

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Conclusions

As suggested by 2-step algorithm, rain induced roughness is an order of magnitude smaller than rain-stratification effect

2-step algorithm very useful to detect peculiar roughness (not wel modelled with ECMWF WS) but very sensitive to land contamination, RFI...

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Scientific studies that used v5 reprocessed SSS

-South Pacific Maximum Salinity-North Atlantic Maximum Salinity (SPURS)-Variability of SSS: effects of rain/roughness/interpolation smoothing

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In press

Figure 1. (a) Mean 1990-2011 modelled mixed-layer salinity. The blue lines represent the Matisse Ship routes of 2010 and 2011 discussed in the main text. (b) Mean Evaporation – Precipitation (E-P) based on ERAi; units are mm/day. Overplotted as arrows are the mean modelled surface currents. The 0 isohyet is shown on both panels with a bold black solid line.

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(Hasson et al, JGR, 2013 in press)

Comparison between near-surface salinity data derived from (black line) the TSG instrument installed on board M/V Matisse and the collocated SSS: (dashed line) modelled and (dotted line) SMOS values. The Matisse salinity values were obtained during 20-27 February 2011 along the northern shipping line shown in Figure 1a.

SMOS SSS, modelled SSS and ship SSS

Model: ORCA025.L75-MRD911 run by french Drakkar group

Over 8 VOS TSG transects (averaged over 20-30km): For a collocation radius of 9 days and 50 km, std difference :Between SMOS and in-situ SSS : 0.20Between modelled and in-situ SSS : 0.26(mean biases -0.08 and 0.07, respectively)

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(Hasson et al, JGR, 2013 in press)

Mean mixed-layer salinity budgets in the high-salinity regions bounded by the 35.6, 36 and 36.4 isohalines

Salinity budget as derived from model analysis

Evaporation-Precipitation (~0.7pss yr-1) balanced by the horizontal salinity advection (~--0.35 pss yr-1) and processes occurring at the mixed layer base (~-0.35 pss yr-1).

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(Hasson et al, JGR, 2013 in press)(a) Monthly and (b) annual mean positions of the modelled 36-isohaline. On both panels the colored dots and stars show respectively the barycentre of the modelled and SMOS-derived 36 isohalines

Seasonal and interannual displacement of 36 isohalines

Model: ORCA025.L75-MRD911 run by french Drakkar group

Very consistent seasonal variation of longitudinal displacement between SMOS and modelled SSS (according to ship SSS, max of modelled SSS could be too north)

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SMOS-LOCEAN activities in SPURS region(Hernandez et al., Kolodziejczyk et al., 2013, in prep. JGR-SMOS-

AQUARIUS)

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SMOS senses mesoscale variability (≠ ISAS)(Hernandez et al., Kolodziejczyk et al., 2013, in prep. JGR)

SMOS vs TSG SSS anomaly (0.25° resol.) ISAS vs TSG SSS anomaly (0.25° resol.)

Ship data from 07/2011 to 12/2012: 14 transects

SMO

S –c

limat

o SS

S

ISAS

–cl

imat

o SS

S

TSG –climato SSS TSG –climato SSS

Once monthly biases are corrected, SMOS senses variability with a RMSE= 0.14

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SMOS and in situ salinity: rain, roughness and interpolation

effectsJ. Boutin1, N. Martin1, J.L. Vergely2, X.

Yin1, G. Reverdin1, F. Gaillard3, O. Hernandez 1, N. Reul4

1 LOCEAN/CNRS, Paris, 2 ACRI-st, Paris, 3 LPO/IFREMER, Brest, 4 LOS/IFREMER, Toulon, France

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ISAS~5m depth

~3°smoothing

SMOS1cm depth

100x100km 2

averages

ITCZ

SPCZ

Motivation:SMOS S1cm maps versus in situ S~5m maps

CATDS-CEC/LOCEAN_v2013 product

SSS August 2010 Rain Rate (SSMI)

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Outline

-Assessment of Levenberg and Marcquardt (LM) retrieved SSS error: SMOS SSS variability within 100km-1month in and out of rainy regions: comparison with SMOS SSS error predicted by LM.

-Alternative roughness retrieval: the 2-step algorithm of X.Yin =>weak effect of rain-roughness on SMOS-ARGO SSS: first order is vertical stratification

-Monthly SMOS SSS maps wrt in situ derived maps (ISAS):-differences linked to rain-stratification -differences linked to interpolation smoothing

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Data & Methods

SATELLITE

SMOS SSS

ESA v5 reprocessing

SSS at 1cm depth ; ~40km resolution or averaged (CATDS-CEC/LOCEAN_v2013 product available

at www.catds.fr) ; focus on August 2010 monthly maps

Rain Rate:

-SSM/I (RemSSS: www.ssmi.com) 0.25° resolution; SMOS SSS colocated within -80mn, +40mn

IN SITU SSS

ARGO INDIVIDUAL PROFILES

‘SSS’ between 10m and 4m depth; Colocation with SMOS within +/-5days, +/-50km CORIOLIS

GDAAC: http://www.coriolis.eu.org

ARGO + TSG OPTIMAL INTERPOLATED SSS MAPS (ISAS)

Monthly maps from In-Situ Analysis System v6 – Correlation scale ~300kmhttp://wwz.ifremer.fr/lpo/SO-Argo/Products/Global-Ocean-T-S

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Method for building SMOS SSS maps(CATDS LOCEAN_CEC_v2013)

• Monthly SSS average over 100x100km2

– weighted by each SSSj resolution (Rj) and theoretical uncertainty on retrieved SSS (ej) derived by the L.M. algorithm ( depends on NTb, dTb/dSSS,...)

• Mean theoretical error on single SMOS SSS is computed with same weights

ESSS =eSSS

e e

e

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SMOS SSS theoretical error & observed variability

Mean theoretical error on single SMOS SSS (~43km resolution): eSSS

Standard deviation of L2OS SMOS retrieved SSS over 1 month in 100x100km2: SSS

(if no natural variability, no bias on SMOS SSS, errorSSS~SSS)

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SMOS SSS theoretical error & variability:SSS - eSSS

Large SSS close to the coast (large and variable SMOS SSS biases)

750km

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SMOS SSS theoretical error & variability:SSS - eSSS

Large SSS close to the coast (large and variable SMOS SSS biases) but also in tropical regions

Influence of rain on SSS – eSSS ? Several possible causes:-natural variability (rain is an intermittent process)-rain-roughness artefact on SSS retrieval

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SMOS SSS theoretical error & variability:Correlation with rain

Tropical Pacific 5N-15NSSS – eSSS ~0.6 * RR :

- SSSvar2 ~ SSS2 - eSSS2 For RR=0.5mm/hr & eSSS=0.6 => SSS natural variability ~0.6

order of magnitude need to be further checked with in situ ground truths

SSS – eSSS versus monthly SSMI Rain Rate

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SMOS SSS – ISAS SSS

After masking areas close to continents and RFIS:SMOS SSS-ISAS SSS

Global Ocean: moy=-0.16 (std=0.36)30°S-30°N: moy=-0.18 (std=0.28)

What is the impact of a correction for rain stratification effect as seen on SSSsmos-SSSargo?

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SMOS S1cm – ARGO S~5m =fn(RR)

Zone ITCZ (Jul-Sep2010) Rain aRR+b r NSsmosA-Sargo SSMI -80mn +60mn -0,17RR-0,16 -0,49 7152Zone SPCZ (Jun10-Feb11)SsmosA-Sargo SSMI -80mn +60mn -0,22RR-0,26 -0,51 4305SsmosD-Sargo SSMI -80mn +60mn -0,19RR-0,29 -0,40 2716SsmosA-Sargo TRMM 3h before -0,15RR-0,20 -0,33 8352SsmosD-Sargo TRMM 3h before -0,16RR-0,23 -0,32 5537

SMOS S~5m ~ SMOS S1cm + 0.19 RR

Boutin et al. 2013

Region close to ITCZ Region close to SPCZ

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SSScorr-SSS SSScorr-SSSisas

Effect of Rain correction on monthly SSS A small correction (not the same color scales!!)

Without rain correction: moy=-0.16 (std=0.36)moy=-0.18 (std=0.28)

With rain correction: moy=-0.15 (std=0.36)moy=-0.17 (std=0.28)

SSSsmos – SSSisas :

Global Ocean: 30°S-30°N:

Vertical stratification of S linked to rain is not responsible for the main differences between SSSsmos-SSSisas – Influence of ISAS optimal interpolation (~300km)???

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Global: moy=-0.09 (std=0.22)

30°N-30°S: moy=-0.09 (std=0.21)

moy=-0.16 (std=0.36)

moy=-0.18 (std=0.28)

SSSsmos_like_ISAS - SSSisas SSSsmos- SSSisas

Effect of ISAS interpolation

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Summary-SMOS SSS theoretical error in very good agreement with the SMOS SSS observed variability within 100x100km2 and one month, except close to continents, RFIs and in rainy regions.-In rainy regions, salinity natural variability is expected to be large (temporally, spatially and vertically):

-SMOS suggests S1cm variability due to time/space variability over one month ~0.6 for a monthly RR=0.5mm/hr. This order of magnitude should be checked in future studies with surface ground truth.

-When averaged over one month, the rain induced vertical salinity difference between 5m & 1cm is small (<0.1).

-When SMOS SSS interpolated similarly to in situ SSS : precision on monthly SMOS SSS smoothed at ~3° resolution ~0.2 between 30°N-30°S.

-