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1 © ACRI-ST, all rights reserved – 2012 TEC estimation Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN)
2 © ACRI-ST, all rights reserved – 2012 Issue : how to improve TEC estimation ? Aim of this study : To list different possibilities of TEC retrieval To propose an algorithm in order to improve the TEC estimation.
3 © ACRI-ST, all rights reserved – 2012 About TEC… In L1c products : TEC is considered as constant on a snapshot. Magnetic field orientation is considered as constant on a snapshot. In L2 processing : TEC is considered as constant along a dwell SSS estimator is robust even if TEC is not well estimated (SSS depends principally on Stokes 1) TEC estimation is very sensitive to calibration (OTT). TB sensitivity to TEC : SMOS TBs are sensitive to TEC according to the latitude (depending on orbit type). In descending orbits, around 30° south, TEC gradient is very large. At this position, SMOS TBs are sensitive to TEC variations. In ascending orbits, low TEC values and no strong gradients. So, TEC variation impact should be relatively marginal on SSS estimation in most of the cases. TEC estimation impact on WS estimation ? But, possibility to extract TEC using SMOS data (from TX, TY and/or A3).
4 © ACRI-ST, all rights reserved – 2012 TEC retrieval TEC estimation could use Bayesian approach because : - TEC is a priori well known in most of the cases - outlier could give wrong Faraday rotation. Bayesian approach allows to put a weight on the TEC a priori knowledge. - possibility to manage a spatial (xi,eta) or (lat,lon) correlation length for TEC estimation Use only measured TBs sensitive to TEC : TB filtering In order to remove outliers : computation should be done after TB filtering (RFI, coast, ice, other outliers…). Require OTT correction.
5 © ACRI-ST, all rights reserved – 2012 TEC estimation : first approach (1) Using collocated TBs (TX,TY,A3) : -TEC estimation from a set of (TX,TY,A3) without forward model -assuming St3 emission at ground level ≈ 0 -Tg(2.(Faraday+geomrot))=A3/(TX-TY) Require: -TBs interpolation -OTT correction -magnetic field Could be done offline or in the L2OS processor. Disadvantages : -Very noised estimation of TEC -problem over land where TX is close to TY (forest). Coast problems ? TEC smoothing in the (xi, eta) plan or (lat, lon) coordinates in order to introduce spatial coherence.
6 © ACRI-ST, all rights reserved – 2012 Faraday rotation from SMOS data using 42.5 browse products (descending orbits, 11/2011) Large Faraday rotation with strong TEC gradient Low Faraday rotation Lon Lat Faraday=atan(A3/(TX-TY))/2 at xi=0 BUT, if TX-TY vanishes (low incidence angle, forest …), A3/(TX-TY) has a very bad statistics behavior (non gaussian). -> we should use modeled TX-TY and not measured TX-TY in order to estimate Faraday rotation or TEC if using A3/(TX-TY) TEC estimation : first approach (2) Forest : TX-TY vanishes
7 © ACRI-ST, all rights reserved – 2012 TEC variations along the dwell line using ocean forward model. Using TBs organized in dwell line : -Depends on the modeled TBs -> only on ocean target. -Faraday retrieval along the dwell line with a large correlation length according to the incidence angle or the eta position. Require: -OTT correction (for instance reference ocean TB in the latitude where the TBs are not sensitive to Faraday rotation). -a priori Faraday rotation -> L1c products Could be done in the L2OS processor : associated to the retrieval scheme Disadvantages : -TEC spatial coherence is not used -No TEC retrieval on land surface -add parameters in the L2OS retrieval scheme. Instead of one TEC value, n TEC values shall be estimated. TEC estimation : second approach.
8 © ACRI-ST, all rights reserved – 2012 TEC estimation : third approach (1) TEC(lat,lon) at the altitude 400 km along the orbit using ocean forward model. Using A3 at high incidence angles in the afFOV. Selected A3 close to xi=0 (across track TEC variations are negligible ?). TEC estimation according to the (lat, lon) position using only A3 at high incidence angles. SMOS TEC surface (lat,lon) V sat TEC altitude Smoothing of TEC estimation according to the latitude (at the altitude 400 km) in order to decrease the noise. TEC interpolation for any SMOS measurement according to the intersection of the los with the TEC area. Extrapolation close to the coast ? Intersection los/TEC area los
9 © ACRI-ST, all rights reserved – 2012 Details on TEC(lat,lon) retrieval using A3 : Select measured A3 for a FOV (xi, eta) position along the whole half orbit. Compute modeled A3mod and dA3/dTEC using a priori TEC 0 from L1c and ECMWF data Correct TBs from OTT (from external OTT or from OTT estimated at adequate latitude) Convert (xi, eta) -> (lat, lon) using knowledge of TEC altitude for each measured A3 Compute Smooth TEC estimation according to the latitude. Possibility to use TX and TY but less sensitive to TEC than A3 (in the afFOV at high incidence angles). TEC/Faraday estimation : third approach (2)
10 © ACRI-ST, all rights reserved – 2012 TEC retrieved from A3 L1c TEC Lat TEC TEC/Faraday estimation : third approach (3)
11 © ACRI-ST, all rights reserved – 2012 TEC/Faraday conclusions Possibility to extract TEC/Faraday from SMOS data. Some targets are not adapted for TEC retrieval (Forest, coast, area contaminated by RFI/outliers, insensitive regions …etc). Important to consider the spatial coherence of the TEC estimation. Possibility to extract the TEC using A3, TX or TY because St3 ground is likely negligible. OTT correction is required. Possibility to consider, for each measurement, a TEC estimation which is associated to the latitude position of the intersection between los and TEC area -> better than the current solution which affects a mean TEC value for the whole dwell line. -> third approach seems the best one.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity Using TEC estimated from Stokes 3 24 October 2012 ACRI-st, LOCEAN & ARGANS SMOS+polarimetry.
21-23/04/2015PM27 ACRI-ST ARGANS LOCEAN TEC follow-up.
SMOS L2 Ocean Salinity – PM#25 1/20 Level 2 Ocean Salinity May 2013 A3TEC.
1 © ACRI-ST, all rights reserved – 2012 Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)
Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)
1 © ACRI-ST, all rights reserved – 2012 Isotropic RFI detection Jean-Luc Vergely (ACRI-ST) Claire Henocq (ACRI-ST) Philippe Waldteufel (LATMOS)
Errors on SMOS retrieved SSS and their dependency to a priori wind speed X. Yin 1, J. Boutin 1, J. Vergely 2, P. Spurgeon 3, and F. Gaillard 4 1. LOCEAN.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L1 -> L2OS tools 12 February 2014 ARGANS & SMOS L2OS ESL.
SMOS L2 Ocean Salinity Commissioning Plan, 07/05/2009 Level 2 Ocean Salinity Processor Commissioning Plan 7 May 2009 ARGANS ACRI-ST ICM-CSIC.
SMOS QWG-6, ESRIN October 2011 OTT generation strategy and associated issues 1 The SMOS L2 OS Team.
New model used existing formulation for foam coverage and foam emissivity; tested over 3 half orbits in the Pacific foam coverage exponent modified to.
UPDATE ON BIAS TRENDS, DIRECT SUN CORRECTION, AND ROUGHNESS CORRECTION Joe Tenerelli May 10, 2011.
Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.
SMOS Quality Working Group Meeting #2 Frascati (Rome), September 13 th -14 th,2010 SMOS-BEC Team.
Impact of sea surface roughness on SMOS measurements A new empirical model S. Guimbard & SMOS-BEC Team SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity status 4 February 2013 ARGANS.
SMOS QWG-9, ESRIN October 2012 L2OS: Product performance summary v550 highlights 1 The SMOS L2 OS Team.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L2OS planning 2 July 2014 ARGANS & SMOS L2OS ESL 1.
Optimization of L-band sea surface emissivity models deduced from SMOS data X. Yin (1), J. Boutin (1), N. Martin (1), P. Spurgeon (2) (1) LOCEAN, Paris,
21-23/04/2015PM27 J-L Vergely, J. Boutin, N. Kolodziejczyk, N. Martin, S. Marchand SMOS RFI/Outlier filtering.
SMOS L1v620-L2v613 versus L1v505- L2v550 validation May 2011 Nicolas Martin, Jacqueline Boutin LOCEAN 26 May 2014.
SMOS QWG-5, 30 May- 1 June 2011, ESRIN Ocean Salinity 1 1.Commissioning reprocessing analysis 2.New processor version: improvements and problems detected/solved.
SMOS L2 Ocean Salinity – PM#25 1/20 Level 2 Ocean Salinity May 2013 OTT post-processor.
SMOS-BEC – Barcelona (Spain) LO calibration frequency impact Part II C. Gabarró, J. Martínez, V. González, A. Turiel & BEC team SMOS Barcelona Expert Centre.
USING SMOS POLARIMETRIC BRIGHTNESS TEMPERATURES TO CORRECT FOR ROUGH SURFACE EMISSION BEFORE SALINITY INVERSION.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity PM # November 2010 ARGANS & L2OS LOCEAN, Paris.
SMOS QWG-11, ESRIN 4-5 July 2013 L2OS v600 status and evolution 1 The SMOS L2 OS Team.
EXTENDING THE LAND SEA CONTAMINATION CHARACTERIZATION TO THE EXTENDED ALIAS- FREE FIELD OF VIEW Joe Tenerelli (CLS) and Nicolas Reul (IFREMER) SMOS Quality.
Faraday Rotation David Le Vine Aquarius Algorithm Workshop March 9-11, 2010.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity v63x product design evolution 22 April 2015 ARGANS & SMOS L2OS ESL 1.
UPDATE ON GALACTIC NOISE CORRECTION Joe Tenerelli SMOS Quality Working Group #9 ESA ESRIN 24 October 2012.
Universitat Politècnica de Catalunya CORRECTION OF SPATIAL ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES L. Wu, I. Corbella, F. Torres, N. Duffo, M. Martín-Neira.
Progress Meeting #27, April 2015, Barcelona SPAIN T3.2 Retrieval algorithm Estrella Olmedo BEC team SMOS Barcelona Expert Centre Pg. Marítim de la.
AN INITIAL LOOK AT THE IMPACT OF THE NEW ANTENNA LOSS MODEL Joe Tenerelli SMOS QUALITY WORKING GROUP #4 7-9 March 2011.
QWG-10 ESRIN 4-6 February 2013 Quality control study for SMOS data / Flags analysis C. Gabarró, J. Martínez, E. Olmedo M. Portabella, J. Font and BEC team.
SMOS SSS and wind speed J. Boutin, X. Yin, N. Martin -Optimization of roughness/foam model -Comparison of new-old ECMWF wind speeds -SSS anomaly in the.
Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.
PART 1: A Brief Comparison of Time- Latitude First Stokes Bias Structure in v505 and v620 PART 1: A Brief Comparison of Time- Latitude First Stokes Bias.
PART 2: A QUICK COMPARISON OF V504 AND V620 GLOBAL MAPS Joe Tenerelli SMOS Calibration Meeting 18 26/05/2014.
Tests on V500 Sun On versus Sun Off 1)Tbmeas. –Tbmodel in the FOV X. Yin, J. Boutin Inputs from R. Balague, P. Spurgeon, A. Chuprin, M. Martin-Neira and.
SMOS L2 Ocean Salinity L1OP v620 border flag width 23 May 2014 ARGANS & L2OS ESL
About L2OS v6 improvement wrt L2OS v5 N. Martin – J.L. Vergely - J. Boutin Descending orbits results In L2 v6 => latitudinal biases are reduced wrt L2.
SMOS L2 Ocean Salinity Reprocessed constant calibration L1c OTT drift study 13 April 2011 ARGANS & L2OS ESL ascendingdescending.
TEC introduced high ratios of std(Tbmeas-Tbmodel)/ radio_accuracy X. Yin, J. Boutin LOCEAN.
MIRAS performance based on OS data SMOS MIRAS IOP 6 th Review, ESAC – 17 June 2013 Prepared by: J. Font, SMOS Co-Lead Investigator, Ocean Salinity – ICM-CSIC.
L2OS RFI status Nicolas Lamquin, Jean-Luc Vergely Jacqueline Boutin Paul Spurgeon ICM, Barcelona, 16/17 May 2013.
IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010.
Space Reflecto, November 4 th -5 th 2013, Plouzané Characterization of scattered celestial signals in SMOS observations over the Ocean J. Gourrion 1, J.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L2OS v611 status 12 February 2014 ARGANS & SMOS L2OS ESL.
SMOS L2 Ocean Salinity L2OS RFI study 9 May 2011 ARGANS & L2OS ESL.
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