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SMOS Ocean Salinity Retrieval Level 3

SMOS Ocean Salinity Retrieval Level 3. Marco Talone, Jérôme Gourrion, Joaquim Ballabrera, Marcos Portabella, and the SMOS Barcelona Expert Centre (SMOS-BEC) Team. talone@icm.csic.es. Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil , November 1-12, 2010.

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SMOS Ocean Salinity Retrieval Level 3

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  1. SMOS Ocean Salinity RetrievalLevel 3 Marco Talone, Jérôme Gourrion, Joaquim Ballabrera, 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. SMOS processing chain Raw data Measurements Observations Global map Data fusion Level 0 Level 1 Level 2 Level 3 Level 4 Data Assimilation

  3. 100km 10 days 1 psu @L2 / sqrt( (6 * 6) * 3 )= 0.1 psu spatio - temporal averaging from level 2 to level 3 Motivation The SMOS level 2 product consists on files containing half-orbit data (from pole to pole) on the ISEA4H9 (Icosahedral Snyder Equal Area projection, level 4, resolution 9) grid defined at level 1. Spatial resolution is 15.74 km not manageable!! not manageable!! each point is revisited by the satellite, at least, every3 days In addition to that , the observational error linked to the SSS data is larger than acceptable for scientific purposes. The complex procedure needed to retrieve SSS from brightness temperatures (Tb) recorded by the MIRAS radiometer is subject to a lot of uncertainties than contaminate the final SSS value. Estimates quality and reliability is improved by degrading the spatial and/or temporal resolution

  4. SMOS requirements • 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 • Smith, N., and M. Lefrebvre, The global ocean data assimilation experiment (GODAE); monitoring the oceans in the 2000s: An integrated approach, in Proceedings of the Symposium on the Global Ocean Data Assimilation Experiment (GODAE), 1997. 44 pp. • SMOS MRD, Smos Mission Requirements Document, avilable at • www.cp34-smos.icm.csic.es/img enlaces/SMOS MRD V5.pdf

  5. Level 3 products

  6. Level 3 products Note: Three versions of each product will be generated using ascending, descending, and bothtypes of orbits. Each absolute salinityvalue will be accompanied by its anomaly(difference between the absolute value and a predefined temporal mean), this predefined mean value, and a computation error valueboth for the absolute and mean values.

  7. Level 2 data discrimination

  8. L2 data discrimination • Orbit Selection: Ascending/Descending/Both • Across-Track distance: maximum distance from boresight of the points to be included in the L3 calculation • Quality of the measurement/retrieval: sigma, chi2, chi2_P, outliers, sunglint, moonglint, gal_noise, TEC, ice, rain, num_meas_low • Number of valid observations • Geophysical conditions

  9. Level 3 processing

  10. OptimalInterpolation An example: Lets estimate the temperature of a room by using 2 thermometers: a 0.2 C- and 0.5 C-precision thermometers. The first one measures 27.7, while the second one 28.0. How can we use the Least Square Algorithm to estimate both the real temperature and its uncertainty? Variable to estimate = temperature Observations = and Analysis result =

  11. OptimalInterpolation Assuming Gaussian statistics, centered in the mean value (27.7 and 28.0) and with a standard deviation equal to the precision of the instrument (0.2 and 0.5)

  12. OptimalInterpolation Assuming independent observations, the joint probability is the product of the probabilities: Maximizing is equivalent to minimizing

  13. OptimalInterpolation The solution can be easily found as: 27.7 is not the arithmetic average of the two measurements (27.9), but the result of a weighted average where the weight is inversely proportional to the error in the measurement. Reducing the error in the measurement is equivalent to increasing its weight in the averaging.

  14. OptimalInterpolation Using a more general notation: Cost Function Distance from the observations Covariance matrix of the error on the observations

  15. OptimalInterpolation This formulation has been obtained assuming Gaussian incorrelated errors on the observations. In this case the least square estimate is equivalent to the maximum likelihood estimate.

  16. OptimalInterpolation The analytic solution of is Assuming the presence of a background field, the cost function becomes covariance matrix of the error on the observations distance from the reference covariance matrix of the error on the reference distance from the observations analysis result Background field observation Observations minus information from the background Minimizing this cost function results in find the field which is most similar to the reference and, at the same time, with the lowest distance from the observations.

  17. Examples

  18. Climatologic SSS

  19. SMOS Level 3 Averaged SMOS Level2 data as provided by DPGS Data Processing Ground Segment

  20. SMOS Problems at L3 Anomaly with respect to climatology RFI at level 3 the land-sea transition effect is evident

  21. L3 synthesis – Asc. vs. Desc. vs All

  22. L3 synthesis – Asc. vs. Desc. vs All

  23. L3 synthesis – Asc. vs. Desc. vs All Fresher when ice/land enters in the FOV Saltier when it exits

  24. Level 3 Products

  25. Level 3 Products SMOS Level 3 User Data Product are distributed by CP34: www.cp34-smos.icm.csic.es Some of the entities participating in the CP34 are: CDTI Instituto de Ciencias del Mar ICM INDRA INSA GMV Universitat de València Universitat Politècn. de Catalunya SMOS Barcelona Expert Centre

  26. Level 3 Products proc version end YYYYMMDDThhmmss start YYYYMMDDThhmmss SM_VAL__M___OFCAF5_20101001T000144_20101101T004906_200_005_1.zip SM_VAL__M___OFCAF5_20101001T000144_20101101T004906_200_005_1.HDR header in XML SM_VAL__M___OFCAF5_20101001T000144_20101101T004906_200_005_1.DBL binary data file As for Level 2, different programs are available to open, display, and export SMOS Level 3 products, among them CP34View CP34 View software, is distributed through the CP34 website www.cp34-smos.icm.csic.es 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 L3 Product Specification Document, on: www.cp34-smos.icm.csic.es

  27. Level 3 Products Variables at Level 3, for each SSS retrieval (3): level 3 SSS variance at level 2 anomaly error reference (mean to compute anomaly) error on the reference background (climatology/model) error on the background fg_discarded: some L2 discarded fg_num_meas_low fg_quality fg_radiom fg_inversion (chi2) fg_range fg_sigma fg_rain fg_ice fg_tec fg_geophysical fg_num_meas_valid fg_L3_gn_pol (galactic noise) fg_L3_invert (marq) fg_averall (too many fg_quality at L2) fg_coast fg_suspect_ice fg_sst_front fg_sss_front fg_high_wind fg_low_wind fg_high_sst fg_low_sst fg_high_sss fg_low_sss fg_sea_state (young sea) fg_failed (level 3 algorithm failed) fg_background (L3 SSS far from background) fg_background_quality (high background error) fg_error_ratio (1-4 quantifies fg_background)

  28. Level 3 Products

  29. SMOS Land Cover Tool Tool from GMV to display and export SMOS product to Google Earth files

  30. Thank you! Course on Earth Observation Understanding of the Water CycleFortaleza, Brasil, November 1-12, 2010 Marco Talone, Jérôme Gourrion, Joaquim Ballabrera, Marcos Portabella, and the SMOS Barcelona Expert Centre (SMOS-BEC) Team. talone@icm.csic.es

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