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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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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 2/29 SMOS processing chain Level 1Level 0Level 2Level 3 Level 4 Data Assimilation MeasurementsObservationsGlobal mapData fusionRaw data

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 3/29 not manageable!! Motivation Estimates quality and reliability is improved by degrading the spatial and/or temporal resolution Spatial resolution is 15.74 km 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. not manageable!! observational error is larger than acceptable lot of uncertainties than contaminate the final SSS value 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. each point is revisited by the satellite, at least, every 3 days

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 4/29 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

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 5/29 Level 3 products ProductDescriptionΔSΔSΔTΔT Product 1Maximum spatial resolution (to locate salinity gradients). Only temporal averaging using a weighted (by measurements uncertainty) averaging method over the L2 products. ISEA4H9 15.74km 10d every 3d Product 1BHigh spatial resolution. Spatio-temporal averaging of using weighted averaging method over the L2 products. ISEA4H8 31.48km 3d every 3d Product 2GODAE. Spatial and temporal averaging using optimal interpolation method over the L2 products. 200km10d every 10d

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 6/29 Level 3 products ProductDescriptionΔSΔSΔTΔT Product 3Climatology-like. Spatial and temporal averaging using optimal interpolation method over the L2 products. 100km30d every 10d Product 3aSeasonal average. Same as product 3 but temporal averaging by season (JFM, AMJ, JAS and OND) over Product 3. 100km3M every 3M Product 3bYearly average. Same as product 3 but temporal averaging by natural year over Product 3. 100km1Y every 1Y Note: Three versions ascending, descending, and both Note: Three versions of each product will be generated using ascending, descending, and both types of orbits. absolute salinity anomaly mean value, error value Each absolute salinity value will be accompanied by its anomaly (difference between the absolute value and a predefined temporal mean), this predefined mean value, and a computation error value both for the absolute and mean values.

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 7/29 Level 2 data discrimination

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 8/29 L2 data discrimination 1.Orbit Selection: Ascending/Descending/Both 2.Across-Track distance: maximum distance from boresight of the points to be included in the L3 calculation 3.Quality of the measurement/retrieval: sigma, chi2, chi2_P, outliers, sunglint, moonglint, gal_noise, TEC, ice, rain, num_meas_low 4.Number of valid observations 5.Geophysical conditions

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 9/29 Level 3 processing

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 10/29 Optimal Interpolation 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 =

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 11/29 Optimal Interpolation 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)

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 12/29 Optimal Interpolation Assuming independent observations, the joint probability is the product of the probabilities: Maximizing is equivalent to minimizing

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 13/29 Optimal Interpolation 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.

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 14/29 Optimal Interpolation Using a more general notation: Cost Function Distance from the observations Covariance matrix of the error on the observations

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 15/29 Optimal Interpolation least square estimatemaximum likelihood estimate 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.

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 16/29 Optimal Interpolation Assuming the presence of a background field, the cost function becomes distance from the observations covariance matrix of the error on the observations 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. distance from the reference covariance matrix of the error on the reference The analytic solution of is analysis result Background field Observations minus information from the background observation

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 17/29 Examples

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 18/29 Climatologic SSS

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 19/29 SMOS Level 3 Averaged SMOS Level2 data as provided by DPGS Data Processing Ground Segment

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 20/29 SMOS Problems at L3 Anomaly with respect to climatology at level 3 the land-sea transition effect is evident RFI

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 21/29 L3 synthesis – Asc. vs. Desc. vs All

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 22/29 L3 synthesis – Asc. vs. Desc. vs All

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 23/29 L3 synthesis – Asc. vs. Desc. vs All Fresher when ice/land enters in the FOV Saltier when it exits

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 24/29 Level 3 Products

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 25/29 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

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 26/29 Level 3 Products 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 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 start YYYYMMDDThhmmss end YYYYMMDDThhmmss proc version

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 27/29 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)

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 28/29 Level 3 Products

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Course on Earth Observation Understanding of the Water Cycle Fortaleza, Brasil, November 1-12, 2010 29/29 SMOS Land Cover Tool Tool from GMV to display and export SMOS product to Google Earth files

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Thank you! Course on Earth Observation Understanding of the Water Cycle Fortaleza, 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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