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MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES

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 gourrion@icm.csic.es. Introduction.

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MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES

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  1. 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 gourrion@icm.csic.es

  2. Introduction OptimalSalinityretrievalforgivendataset and given forward model Level 1B/C CorrectedTBs (OTT) Reconstructed TBs Retrievalscheme Y-pol Forward model Forward model Level 2 Retrieved SSS Adjustmeasurementstomodelonaverage η Reduce overall SSS biases Auxiliary data Auxiliary data ξ

  3. 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

  4. OTT - Current approach Overallmisfitbetween data and model: stability Temporal variability Number of scenes Latitudinal variability from Gourrion et al. 2012, GRSL DPGS data from August 2010, Ascending passes The estimated pattern varies with the dataset used – typically 0.5 K This includes the variability of model errors.

  5. Introduction Furthersalinityimprovementrequiresforward modelimprovement Level 1B/C CorrectedTBs (OTT) Reconstructed TBs Retrievalscheme Forward model Level 2 OTT uncertainty: 0.5 K Retrieved SSS Needfor a modelindependentcorrection Might be validfor Ocean/Ice/Landimages Auxiliary data

  6. OTT - New approach Objectives • 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 Our ocean results are compared with those obtained by F.Cabot using SMOS data acquired over ice at Dome-C

  7. OTT - New approach Strategy (Ocean – Ice) • Use a dataset with low geophysical/environmental variability • data selection (U,SSS,SST,galaxy) – stable target, single point at Dome-C Dec. 2010 June 2010 June 2011

  8. OTT - New approach Strategy (Ocean – Ice) • Use a dataset with low geophysical/environmental variability • Rotate from antenna (X/Y) polarization frame • to surface (H/V) - geometry+Faraday

  9. OTT - New approach Strategy (Ocean – Ice) • Use a dataset with low geophysical/environmental variability • 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 H-pol TB H-pol TB

  10. OTT - New approach Strategy (Ocean – Ice) • Use a dataset with low geophysical/environmental variability • 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 • Rotate back to get the expected X/Y TBs for all selected data

  11. OTT - New approach Strategy (Ocean – Ice) • Use a dataset with low geophysical/environmental variability • 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 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 X-pol TB anomaly Y-pol TB anomaly

  12. OTT - New approach June 2010 Robustness (1): varyingwindspeed 6 m/s – 8 m/s 12 m/s – 8 m/s 10 m/s – 8 m/s 18-days datasets |U-U0| < 1 m/s June 2010 (XX+YY)/2 December 2010 6 m/s 12 m/s 8 m/s 10 m/s June 2011 6 m/s – 8 m/s 12 m/s – 8 m/s 10 m/s – 8 m/s December 2011 6 m/s – 8 m/s 12 m/s – 8 m/s 10 m/s – 8 m/s 6 m/s – 8 m/s 12 m/s – 8 m/s 10 m/s – 8 m/s Between 5 and 11 m/s, pattern discrepancy is lower than 0.05 K r.m.s.

  13. OTT - New approach Robustness (2): varying time period Same latitudinal band Same season Same celestial reflections Same sun location June 2011 - 2010 December 2011 - 2010 January 2012 - 2011 (XX+YY)/2 [35oS, 0oS] [35oS, 0oS] [55oS, 35oS] RMS differences over 1 year interval lower than 0.15 K Related to residual calibration errors or instrument stability ?

  14. OTT - New approach Robustness (3): comparingOcean/Ice results Ice Ocean X-pol Y-pol Results over ice provided by F.Cabot

  15. OTT - New approach Robustness (3): comparingOcean/Ice results Ice from F.Cabot Ice with Ocean method Ice with modified Ocean method Ocean X-pol Y-pol We can define a method so that differences in Ocean/Ice results are not methodological

  16. OTT - New approach Robustness (3): comparingOcean/Ice results Ocean Ice X-pol Y-pol High consistency between Ocean-derived and Ice-derived systematic error patterns Residual differences to be understood. Reconstruction errors ? Ongoing work …

  17. 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

  18. Summary • Further work: • Investigate origin of residual Ocean/Ice inconsistencies (inc. angle) • Forward model improvement • Revisit roughness contribution • Faraday rotation: ongoing work

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