model-independent estimation of systematic errors in smos brightness temperature images

SPCM-9, Esac, May 3rd, 2012

MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS

TEMPERATURE IMAGES

J. Gourrion, S. Guimbard, R. Sabia, M. Portabella, V. Gonzalez, A. Turiel, J. Ballabrera, C. Gabarró, F. Perez, J. Martinez

SMOS-BEC, ICM/CSIC

[email protected]


Introduction

Forwardmodel

Auxiliarydata

Level 2

Retrieved SSS

Retrieval scheme

Reconstructed TBs

Level 1B/C

Corrected TBs (OTT)

Optimal Salinity retrieval for given dataset and given forward model

Adjust measurements to model on average

Reduce overall SSS biases

Forwardmodel

Auxiliarydata

Y-pol

ξ

η


Introduction

SMOS-retrieved SSS biases due to forward model imperfections at high wind speed

from Guimbard et al. 2012, TGRS

Forward model errors (roughness, galactic, Faraday, …) contribute to a variability of the estimated pattern of about 0.5 K


OTT - Current approach

Number of scenes

Temporal variability

Latitudinal variability

from Gourrion et al. 2012, GRSLDPGS data from August 2010,Ascending passes

The estimated pattern varies with the dataset used – typically 0.5 KThis includes the variability of model errors.

Overall misfit between data and model: stability


OTT uncertainty: 0.5 K

Introduction

Forwardmodel

Auxiliarydata

Level 2

Retrieved SSS

Retrieval scheme

Reconstructed TBs

Level 1B/C

Further salinity improvement requires forward model improvement

Need for a model independent correction

Might be valid forOcean/Ice/Land images

Corrected TBs (OTT)


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

mandatory for consistent model improvement tasks

• Get a stable estimate of the systematic error pattern variability tipically lower than 0.5 K

Objectives

OTT - New approach

Our ocean results are compared with those obtained by F.Cabot using SMOS data acquired over ice at Dome-C


Strategy (Ocean – Ice)

• Use a dataset with low geophysical/environmental variability

data selection (U,SSS,SST,galaxy) – stable target, single point at Dome-C

OTT - New approach

June 2010

Dec. 2010

June 2011




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

OTT - New approach


H-pol TB H-pol TB



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

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

OTT - New approach





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

• Rotate back to get the expected X/Y TBs for all selected data

OTT - New approach





• From the mean scene, fit its incidence angle (θ) dependence to obtain a simplified one-parameter empirical model – H/V and get the anomaly

• Rotate back to get the expected X/Y TBs for all selected data

• Compute the anomaly, mean difference between data and model

OTT - New approach

X-pol TB anomaly Y-pol TB anomaly


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

June 2010

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

December 2010

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

June 2011

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

December 2011

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

June 2010

Robustness (1): varying wind speed

(XX+YY)/2

Between 5 and 11 m/s, pattern discrepancy is lower than 0.05 K r.m.s.

|U-U0| < 1 m/s

18-days datasets

OTT - New approach


Robustness (2): varying time period

RMS differences over 1 year interval lower than 0.15 K Related to residual calibration errors or instrument stability ?

(XX+YY)/2

OTT - New approach

Same latitudinal band

Same season

Same celestial reflections

Same sun location

[55oS, 35oS]

June 2011 - 2010

[35oS, 0oS]

December 2011 - 2010

January 2012 - 2011

[35oS, 0oS]


Robustness (3): comparing Ocean/Ice results

Ocean

OTT - New approach

Y-pol

X-pol

Ice

Results over ice provided by F.Cabot


Robustness (3): comparing Ocean/Ice resultsOcean

OTT - New approach

Y-pol

X-polIce

from F.CabotIce

with Ocean method Ice

with modified Ocean method

We can define a method so that differences in Ocean/Ice results are not methodological


Robustness (3): comparing Ocean/Ice results

High consistency between Ocean-derived and Ice-derived systematic error patterns

Residual differences to be understood. Reconstruction errors ? Ongoing work …

Ocean

OTT - New approach

Y-pol

X-pol

Ice


Summary

Near-future improvement in SMOS salinity products will come with forward model adjustment (roughness, Faraday, galactic reflection, …)

Model improvement tasks require a specific approach for systematic error correction

Model-independent

Stability lower than 0.5 K

The approach proposed, apart from being model-independent, is

stable when estimated from datasets with different geophysical conditions (< 0.1 K r.m.s)

stable over time, in the limit of instrument stability (< 0.15 K)

promising consistency with independent results obtained over ice surfaces at Dome-C (F.Cabot) the pattern is robust


Summary

Further work:

Investigate origin of residual Ocean/Ice inconsistencies (inc. angle)

Forward model improvement

Revisit roughness contribution

Faraday rotation: ongoing work

model-independent estimation of systematic errors in smos brightness temperature images

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