SPCM-9, Esac, May 3 rd, 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

SPCM-9, Esac, May 3 rd, 2012 Introduction Forward model Auxiliary data 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 Forward model Auxiliary data Y-pol ξ η

SPCM-9, Esac, May 3 rd, 2012 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

SPCM-9, Esac, May 3 rd, 2012 OTT - Current approach Number of scenes Temporal variability 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. Overall misfit between data and model: stability

SPCM-9, Esac, May 3 rd, 2012 OTT uncertainty: 0.5 K Introduction Forward model Auxiliary data Level 2 Retrieved SSS Retrieval scheme Reconstructed TBs Level 1B/C Further salinity improvementrequiresforward model improvement Need for a model independent correction Might be valid for Ocean/Ice/Land images Corrected TBs (OTT)

SPCM-9, Esac, May 3 rd, 2012 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

SPCM-9, Esac, May 3 rd, 2012 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 June 2011

SPCM-9, Esac, May 3 rd, 2012 Strategy (Ocean – Ice) Use a dataset with low geophysical/environmental variability Rotate from antenna (X/Y) polarization frame to surface (H/V) - geometry+Faraday OTT - New approach

SPCM-9, Esac, May 3 rd, 2012 H-pol T B 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 OTT - New approach

SPCM-9, Esac, May 3 rd, 2012 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 OTT - New approach

SPCM-9, Esac, May 3 rd, 2012 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 OTT - New approach X-pol T B anomalyY-pol T B anomaly

SPCM-9, Esac, May 3 rd, m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s June m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s December m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s June m/s – 8 m/s 10 m/s – 8 m/s 12 m/s – 8 m/s December m/s 8 m/s10 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-U 0 | < 1 m/s 18-days datasets OTT - New approach

SPCM-9, Esac, May 3 rd, 2012 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 [55 o S, 35 o S] June [35 o S, 0 o S] December January [35 o S, 0 o S]

SPCM-9, Esac, May 3 rd, 2012 Robustness (3): comparing Ocean/Ice results Ocean OTT - New approach Y-pol X-pol Ice Results over ice provided by F.Cabot

SPCM-9, Esac, May 3 rd, 2012 Robustness (3): comparing Ocean/Ice results Ocean OTT - New approach Y-pol X-pol Ice from F.Cabot Ice with Ocean method Ice with modified Ocean method We can define a method so that differences in Ocean/Ice results are not methodological

SPCM-9, Esac, May 3 rd, 2012 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

SPCM-9, Esac, May 3 rd, 2012 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

SPCM-9, Esac, May 3 rd, 2012 Summary  Further work:  Investigate origin of residual Ocean/Ice inconsistencies (inc. angle)  Forward model improvement  Revisit roughness contribution  Faraday rotation: ongoing work