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SMOS Ocean Salinity Retrieval Level 2

SMOS Ocean Salinity Retrieval Level 2. Marco Talone, Jérôme Gourrion, Fernando Martin Porqueras, Nicolas Reul, Joe Tenerelli, Marcos Portabella, and the SMOS Barcelona Expert Centre (SMOS-BEC) Team talone@icm.csic.es.

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SMOS Ocean Salinity Retrieval Level 2

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  1. SMOS Ocean Salinity RetrievalLevel 2 Marco Talone, Jérôme Gourrion, Fernando Martin Porqueras, Nicolas Reul, Joe Tenerelli, Marcos Portabella, and the SMOS Barcelona Expert Centre (SMOS-BEC) Team talone@icm.csic.es Course on Earth Observation Understanding of the Water CycleFortaleza, Brasil, November 1-12, 2010

  2. Motivation

  3. Ocean salinity monitoring: motivation/overview • SSS variations governed by: • E-P balance • freezing/melting ice • freshwater run-off • Key oceanographic parameter (density) • Thermohalinecirculation and heat redistribution

  4. Ocean salinity monitoring: motivation/overview Historical lack of SSS observations 10-m depth salinity field reconstructed from Argo floats data. There are still “holes” and spatial resolution is low SSS time-series Surface salinity distribution is closely tied to E-P patterns

  5. Ocean salinity monitoring: motivation/overview • Oceanographic models already assimilate SST and SSH from satellite data, while SSS is still climatologic • The absence of any specific treatment of salinity in ocean models can lead to significant errors: • Near-surface currents errors [Acero-Schetzer et al., 1997] • Tropical dynamics [Murtugudde and Busalacchi, 1998] • Dynamic height difference [Maes et al., 1999; Ji et al., 2000] • Spurious convection [Troccoli et al., 2000] • ENSO predictions [Ballabrera-Poy et al., 2002]

  6. SMOS, general features

  7. SMOS satellite – general features • 1.4 GHz, L-band (unique payload) • Optimum SSS sensitivity • Reasonable pixel dimension • Atmosphere almost transparent • Synthetic Aperture Radiometer (MIRAS) • Sun-synchronous LEO orbit, 3 days revisit time • 69 elements array, Y-array: arms 120º apart • Field Of View (EAF FOV) about 1000 km • Dual-pol / Full-pol • Multi-angular capabilities • Spatial Resolution: 32 (boresight) - 100 km • Full scene acquired every 2.4 s • Variable number of observations according to the satellite sub-track distance • Different measurements of TB corresponding to a single SSS under different incidence angles

  8. SMOS satellite – Field Of View boresight nadir Radiometric Accuracy and Radiometric Sensitivity (quality of the measurement) [calculated using SEPS] Incidence Angle and Spatial Resolution [calculated using SEPS]

  9. SMOS satellite – Field Of View • Due to MIRAS geometry Nyquist criterion is not satisfied • 3 FOV can be defined: • Hexagon resolved by MIRAS • Alias-Free FOV • Extended Alias-Free FOV

  10. SMOS processing chain

  11. SMOS processing chain Raw data Measurements Observations Global map Data fusion Level 0 Level 1 Level 2 Level 3 Level 4 Data Assimilation

  12. SMOS processing chain Level 0 Raw data Level 1A Calibrated Visibilities Level 1B TB Fourier components Level 1C TB geocoded (ISEA4H9) Level 2 Salinity Maps (single-overpass) Level 3 Spatio-temporal averaged SSS Level 4 Merged product • Scientific requirements for salinity retrieval • Global Ocean Data Assimilation Experiment (GODAE, 1997) • 0.1 psu, 200 km, 10 days • Salinity and Sea Ice Working Group (SSIWG, 2000) • 0.1 psu, 100 km, 30 days • SMOS (Mission Requirements Document v5, 2002) • 0.1 psu, 200 km, 30 days • lower accuracy, higher resolution products (e.g. 100 km, 10 days or single passes) are useful for applications other than climate and large scale studies ISEA DGGs (Discrete Global Grids)

  13. From Level 1C to Level 3 Level 1C Level 1C pre- processing Level 2 post- processing Level 3 quality control & filtering SSS inversion Level 1C

  14. Level 1C What does a radiometer measure? Boltzmann constant G accounts for the receiver’s gain and the antenna pattern receiver temperature is the only term dependent on the observed scene it is also referred as Apparent Temperature because sum of various contributions

  15. Level 1C - Forward Models ionosphere atmosphere surface

  16. Level 1C - Forward Models 3 MODELS Two-scale model IFREMER Brest, France Small Slope Approximation (SSA) model LOCEAN, Paris, France Empirical Model ICM, Barcelona, Spain roughness contribution flat sea contribution Klein & Swift (1977) dielectric model at microwave frequencies The total dynamic of TB is 2-4 K TB sensitivity to SSS increases with SST

  17. Level 1C - Forward Models Brightness temperature as measured by SMOS TY TX

  18. From Level 1C to Level 3 Level 1C Level 1C pre- processing pre- processing Level 2 post- processing Level 3 quality control & filtering SSS inversion Level 2 pre-processing

  19. Level 1C – Errors and inaccuracies Several different phenomena contribute to the final The main error sources for the SSS retrieval are: • The forward Tb models • The estimation of the antenna pattern • The estimation of the galactic noise • Radio Frequency Interference • Land contamination Some of them are solved by pre- and post-processing techniques

  20. Level 2 pre-processing Ocean Target Transformation Average instrumental spatial pattern against ocean target, to be subtracted from measurements prior to SSS retrieval. [J. Tenerelli, Tech Note, 2010] INCLUDED IN THE CURRENT PROCESSING • An accurate filtering of the snapshots must be applied to discard land and/or Radio Frequency Interferences (RFI) contaminations. • Ascending and descending passes must be considered separately. • Finally, many orbits are used to increase the robustness of the estimation.

  21. Level 2 pre-processing Strong systematic patterns are found in SMOS TB measurements Features are clearly associated to brightness temperature transition: Sky/Land Alias Free/Extended Alias Free Field of View

  22. Level 2 pre-processing The use of a forward model can introduce error due to inaccuracies in its definition Unhomogeneities in the geophysical parameter statistical distribution in the FOV affect the estimation of the OTT INCLUDED IN THE NEXT REPROCESSING (july) Model-free OTT – X pol Model-free OTT – Y pol

  23. Level 2 pre-processing Sea Surface Temperature [°C] Wind Speed [m/s] Histograms are calculated for all the pixel of the reconstructed brightness temperature image (black lines). A selection of the grid point used in the averaging is performed to homogenize all the histograms the most internal one (red line)

  24. Level 2 pre-processing STANDARD HOMOGENIZED The average sea surface salinity, sea surface temperature, and wind speed inside the FOV are shown for the standard OTT and the “homogenized” OTT.

  25. Level 2 pre-processing The difference between the “homogenized” and “no-homogenized” OTT. Model-free OTT – X pol Standard OTT – X pol MODE L -FREE STANDARD Model-free OTT – Y pol Standard OTT – Y pol 1 - 2 °C for sea surface temperature and 0.5 - 1 m/s for wind speed up to 0.6 - 0.8 K (peak to peak) in the estimation of the bias spatial pattern.

  26. Level 2 pre-processing External Brightness Temperature Calibration Average instrumental temporal pattern (scene-dependent bias) against ocean target, to be subtracted from measurements prior to SSS retrieval. [Camps et al., Radio Science 2005] IN TESTING PHASE

  27. Level 2 pre-processing • Radio Frequency Interference (RFI) • Spurious stable or intermittent man-made interferences. • Receiver Co-Channel Interference + Receiver Adjacent Signal Interference - the signal itself or its tails can fall within the receiver’s RF passband. • Receiver Out of Band Interference - the signal is outside the receiver’s RF passband, nevertheless spurious signals due to the mixer stage. • Transmitter Fundamental and Harmonic Emissions - the Transmitter Transfer Function. • Transmitter Noise - thermal noise generated in the various stages of the processing. • Transmitter Intermodulation - local mixing of a transmitter’s output emission with that of another transmitter or any other component of the instrument. • Concerning SMOS the strongest interference come from WiFi networks and Radar • As expressed in the Technical Note on “L-band RFI detected in SMOS data over the world oceans” by Nicolas Reul of IFREMER.

  28. Level 2 pre-processing By estimating the impulsional response of the RFI, this can be eliminated from the scene, as done for the Sun effects. [Camps, 2010] INCLUDED IN THE NEXT REPROCESSING (july)

  29. From Level 1C to Level 3 Level 1C Level 1C pre- processing pre- processing Level 2 Level 2 post- processing Level 3 quality control & filtering quality control & filtering SSS inversion SSS inversion Level 2

  30. Quality control and filtering Quality control is performed on both measurement and gridpoint basis • Distance from the coast: • Ice • Suspect ice • Heavy rain • Sea condition • Number of valid measurements • Sunglint • Moonglint • Galactic noise • position in the FOV: • RFI Land < 40 km 40 km - 200 km > 200 km OK Retrieved but Flagged Discarded AF EAF Border FOV Aliased FOV

  31. SSS Inversion The problem state variables observations forward model The solution • exact algebraic solution, • relaxation, • least squares estimation, • truncated Eigenvalue expansion, • Bayes’ theorem, • etc … • maximum likelihood, • maximum posteriori probability, • minimum variance, • minimum measurement error • etc …

  32. SSS Inversion - theoretical background Bayesian approach posterior probability uncertainty of observations andforward model knowledge about the state variables ASSUMING AND INDEPENDENT (ERRORS UNCORRELATED)

  33. SSS Inversion - theoretical background Maximum Likelihood Estimation Errors are generally assumed Gaussian 1. Forward Model (GMF, Geophysical Model Function) is assumed perfect 2. Errors are assumed uncorrelated is diagonal

  34. SMOS SSS Inversion SMOS Sea Surface Salinity Retrieval Cost Function observables part Background term is minimized iteratively YES min NO INITIALIZATION

  35. From Level 1C to Level 3 Level 1C Level 1C pre- processing pre- processing Level 2 Level 2 post- processing post- processing Level 3 Level 3 quality control & filtering quality control & filtering SSS inversion SSS inversion Level 2 post-processing

  36. Level 2 post-processing External Sea Surface Salinity Calibration Correcting for the mean uncertainty introduced by the forward model inaccuracies as done for rain radar calibration (Seo and Breidenbach, 2002) using as ancillary in-situ database the ARGO array of buoys. [Talone et al., IEEE TGARS 2008] IN TESTING PHASE

  37. Level 1C problems @Level2 RFI Galactic Noise Land contamination

  38. Level 1C problems @Level2 - RFI North Pole case: Radar Average SSS in April – ASCENDING PASSES Average SSS in April – DESCENDING PASSES SOURCE anti-missile radar protection array from Alaska all along the Northern Canada pointing to the horizon climatological SSS

  39. Level 1C problems @Level2 - RFI Due to the signal processing in SMOS, a point strong source generates 60-degrees spaced tails, like a star. First Stokes’ parameter in brightness temperature (I=TX+TY). One-month averaging, only descending passes. Retrieved SSS

  40. Level 1C problems @Level2 - RFI Due to the signal processing in SMOS, a punctual strong source generates 60-degrees spaced tails, like a star. First Stokes’ parameter in brightness temperature, both the ascending and descending passes of the month of May

  41. Level 1C problems @Level2 - RFI RFI has effects several kilometers from the source. Sources on land frequently affects SSS retrieval. First Stokes’ parameter in brightness temperature, both the ascending and descending passes of the month of May

  42. Galactic Noise Scattering model for ocean surface reflection of downwelling celestial radiations Very geographic, pass-type & incidence angle dependent [Nicolas Reul, IFREMER, 2010]

  43. Level 1C problems @Level2 – Land cont. The image reconstruction algorithm in SMOS is almost a FFT. Any sharp transition introduce singularities and its inversion introduce errors. Land’s brightness temperature is 300 K, while the average sea surface brightness temperature is 120 K.

  44. Level 2 Products

  45. Level 2 Products SMOS Level 2 User Data Product – UDP is available, one file per semi-orbit, on: http://eopi.esa.int/esa/esa (a data request form must be filled first) proc version end YYYYMMDDThhmmss start YYYYMMDDThhmmss SM_OPER_MIR_OSUDP2_20101023T140558_20101023T145957_316_001_1.zip SM_OPER_MIR_OSUDP2_20101023T140558_20101023T145957_316_001_1.HDR header in XML SM_OPER_MIR_OSUDP2_20101023T140558_20101023T145957_316_001_1.DBL binary data file

  46. Level 2 Products Different programs are available to open, display, partially process, and export SMOS Level 2 data, among them: BEAM software, including SMOS-box plug-in can be downloaded from www.brockmann-consult.de Binary .DBL files can be read by using ad-hoc programs (C, Matlab, Fortran…), exported data can feed any program you are most used to (IDL, Matlab, ODV…) Details on DBL file structure can be found in the L2 Product Specification Document, on: www.smos-bec.csic.es

  47. Level 2 Product

  48. Level 2 Product • 3 retrieved SSS • 3 theoretical uncertainties associated to the 3 retrievals • Acard • theoretical uncertainty associated to the retrieval of Acard • Wind Speed • theoretical uncertainty associated to the retrieval of WS • Sea Surface Temperature • theoretical uncertainty associated to the retrieval of SST • Modeled Brightness Temperature at 42.5° pol H (surface) • theoretical uncertainty associated to TBH • Modeled Brightness Temperature at 42.5° pol V (surface) • theoretical uncertainty associated to TBV • Modeled Brightness Temperature at 42.5° pol X (antenna) • theoretical uncertainty associated to TBX • Modeled Brightness Temperature at 42.5° pol Y (antenna) • theoretical uncertainty associated to TBY • Control Flag: Several quality flags one for retrieval (4) • Dg_chi2: Retrieval fit quality index, one for retrieval (4)

  49. Level 2 Product • Dg_chi2_P: chi2 high value acceptability probability, • one for retrieval (4) • Dg_quality: Descriptor of SSS uncertainty, one for • retrieval (4) • Dg_num_iter: number of iterations until convergence, • one for retrieval (4) • Dg_num_meas_L1c: number of measurements at L1c • Dg_num_meas_valid: number of valid measurements • after discrimination • Dg_border_fov: number of grid-points at the border of the • FOV • Dg_eaf_fov: number of grid-points in the Extended • Alias-Free FOV • Dg_af_fov: number of grid-points in the Alias-Free FOV • Dg_sun_tails: number of grid-points affected by sun • reflection tails • Dg_sunglint_area: number of grid-points affected by sun • reflection • Dg_sunglint_fov: number of grid-points with reflected sun • in the FOV

  50. Level 2 Product • Dg_sunglint_L2: number of grid-points with reflected sun • in the FOV, as computed at L2 • Dg_suspect_ice: number of grid-points with suspected ice • Dg_galactic_Noise_Error: number of grid-points affected by • galactic noise • Dg_galactic_Noise_Pol: number of grid-points affected by • polarized galactic noise • Dg_moonlight: number of grid-points with reflected • moonlight in the FOV • Science_Flags: several geophysical flags • Dg_sky: number of gridpoints with specular direction • toward a strong galactic source • Land_Sea_Mask: Land/Sea descriptor

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