SMOS Science Workshop, Arles, 27-29 th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC ERROR DIAGNOSTIC J. Gourrion, R. Sabia, M. Portabella,

SMOS Science Workshop, Arles, 27-29th Sept, 2011

IMPROVING SMOS SALINITY RETRIEVAL:

SYSTEMATIC ERROR DIAGNOSTIC

J. Gourrion, R. Sabia, M. Portabella, S. Guimbard, J. Tenerelli

SMOS-BEC, ICM/CSIC

CLS


Introduction

Systematic errors in the SMOS

reconstructed brightness temperature images

identified rapidly after launch (J.Tenerelli)

Data from March, 2010

X-pol Y-pol

ξξ

ηη


(*) AGP: antenna gain pattern

Image reconstruction

non-identical AGP(*)

imperfectly known AGP(*)

Imperfect calibration

Error correction

Foreign sources removal

Measured visibilities

Level 0

Calibrated

visibilities

Level 1A Level 1B

SMOS TB

Introduction

Systematic TB errors: why ?

… as anticipated by Camps [1998], Anterrieu [2003]


Ocean Target Tranformation

1st approach: overall systematic error correction in the antenna frame

To avoid systematic inconsistencies between data and model during

inversion, this fully empirical approach is convenient to optimize the

retrieved salinity fields for a given instrumental and modeling state.

This approach is operationally used in the L2OS processor




Number of scenes

Temporal variability

Latitudinal variability

from Gourrion et al. 2011, submitted to GRSL

DPGS data from August 2010,Ascending passes




Summary

The overall error pattern has 2 components:

Azimuthally-distributed systematic errors

likely due to antenna patterns

Incidence angle-dependent systematic errors

data ? model ?

The estimated pattern is highly variable with the

dataset used to compute it

Inconsistent with “systematic” errors



2nd approach: specific error correction

• Characterize systematic errors in the antenna frame independently of forward models

• Get a stable estimate of the systematic error pattern

• Separate azimuthally distributed errors (antenna pattern-related) from other errors (data or model). Mandatory for consistent model improvement tasks and combination of measurements at same incidence but different location in the image

Objectives




Requirements




• Select specific geophysical conditions (U, SST, SSS) at individual (xi,eta) points using thresholds on auxiliary parameters

Methodology

Wind speed : U = U0 ± 0.5 m/s

Sea surface salinity and temperature such that dielectric properties are nearly

homogeneous: Tbflat = <Tb

flat> ± 0.5 * ΔU





Methodology

• Sky reflections

Courtesy of J. Tenerelli





Methodology

• Rotate polarization frame from antenna (X/Y) to surface (H/V), geometry+Faraday

• From the mean scene, fit its incidence angle (θ) dependence to obtain a simplified one-parameter empirical model

• Average TBH/V(ξ,η) – TB

model(θ) in the antenna frame

• Rotate back from surface to antenna polarization frame (geometry)

• Sky reflections




Robustness (1): varying wind speed

X-pol

Y-pol

6 m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s

10oS > lat > 30oS

Between 5 and 11 m/s, pattern discrepancy is lower than 0.1 K r.m.s.

|U-U0| < 1 m/s

16-days datasets




Robustness (1): varying wind speed

X-pol

Y-pol

6 m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s

10oS > lat > 30oS


|U-U0| < 1 m/s





Robustness (2): varying latitude range[35oS,10oS]-[55oS,35oS]

X-pol

Y-pol

6 m/s 8 m/s 10 m/s 12 m/s

Strong discrepancy between different latitude bands due to varying sun alias location and imperfect sun removal procedure




Robustness (2): varying latitude range[35oS,10oS]-[55oS,35oS]

X-pol

Y-pol

6 m/s 8 m/s 10 m/s 12 m/s

Depending on sun alias location, strong discrepancy between different latitude bands may appear due to imperfect sun tails removal procedure

Nov 2010Aug 2010




Robustness (3): ascending / descending

X-pol Y-pol

[35oS,10oS], 8 m/s

Faraday rotation is poorly accounted using the auxiliary TEC information.Ascending and descending passes cannot be combined together. 1st Stokes is affected by galactic contamination in descending passes

1st Stokes




Robustness (4): varying temporal window

The number of observations used in estimating the error

pattern is crucial regarding its robustness

Patterns obtained over different but consistent geophysical conditions

can be combined to further increase the

robustness

1st Stokes

6 m/s

8 m/s

10 m/s

12 m/s




Robustness (4): varying temporal window

X- and Y-pol patterns are contaminated by a rotation-related pattern

Y-pol

X-pol

1st Stokes

Patterns determined over different time periods cannot be safely averaged together



Difference between both approaches

X-pol Y-pol

30o

50o

40o

20o

50o

40o

30o

20o

The 1st approach OTT includes systematic discrepancy with incidence angle between data and models which origin are presently not identified.


Summary

The present OTT as implemented at DPGS is

dependent on imperfect forward models

variable from one dataset to the other (~0.5 K)

contaminated by residual sun correction errors (~0.5 K near sun tails)

An alternative method to estimate systematic error patterns is proposed

Galactic contribution intensity drives the choice of the dataset

Stable over various geophysical conditions (~0.1 K for 5 < U < 11 m/s)

Importance of data selection

Difficulty to mix ascending/descending passes (Faraday, Galactic)

Further work:

compare with other low-galactic datasets (A/D), Faraday from T3

Next plenary workshop foreseen in March 2012

Additional institutions and countries are welcome!

SMOS-Mission Oceanographic Data Exploitation

SMOS-MODE

[email protected]

SMOS-MODE supports the network of SMOS ocean-related R&D


SMOS-MODE – SMOS-Mission Oceanographic Data Exploitation

[email protected]

•SMOS-MODE supports the network of SMOS ocean-related R&D

• Meetings• Workshops• Training school• Short term scientific missions

•Overall Aim:

• To coordinate pan-European teams to define common protocols to produce high-level salinity maps and related products, and broaden expertise in their use for operational applications.

• To bridge remote sensing and applications communities

•14 countries represented so far. Co-chairs:

• Antonio Turiel, SMOS Barcelona Expert Centre (SMOS-BEC), Barcelona, Spain • Nicolas Reul, IFREMER, Brest, France

•Next plenary workshop foreseen in March 2012

Additional institutions and countries are welcome!

Documents

SMOS Science Workshop, Arles, 27-29 th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC ERROR DIAGNOSTIC J. Gourrion, R. Sabia, M. Portabella,