SMOS Science Workshop, Arles, 27-29 th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC...
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Transcript of SMOS Science Workshop, Arles, 27-29 th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC...
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
(*) 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]
SMOS Science Workshop, Arles, 27-29th Sept, 2011
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
1st approach: overall systematic error correction in the antenna frame
Number of scenes
Temporal variability
Latitudinal variability
from Gourrion et al. 2011, submitted to GRSL
DPGS data from August 2010,Ascending passes
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
1st approach: overall systematic error correction in the antenna frame
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
Requirements
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
• 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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
• Select specific geophysical conditions (U, SST, SSS) at individual (xi,eta) points using thresholds on auxiliary parameters
Methodology
• Sky reflections
Courtesy of J. Tenerelli
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
• Select specific geophysical conditions (U, SST, SSS) at individual (xi,eta) points using thresholds on auxiliary parameters
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
Between 5 and 11 m/s, pattern discrepancy is lower than 0.1 K r.m.s.
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
2nd approach: specific error correction
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
SMOS Science Workshop, Arles, 27-29th Sept, 2011
Ocean Target Tranformation
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.
SMOS Science Workshop, Arles, 27-29th Sept, 2011
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
SMOS-MODE supports the network of SMOS ocean-related R&D
SMOS Science Workshop, Arles, 27-29th Sept, 2011
SMOS-MODE – SMOS-Mission Oceanographic Data Exploitation
•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!