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.

Slides:



Advertisements
Similar presentations
SMOS L2 Ocean Salinity – Reprocessing Level 2 Ocean Salinity Reprocessing 17 September 2008.
Advertisements

SMOS L2 Ocean Salinity Level 2 Ocean Salinity L20S Tool Box Architecture & Release 27 June 2014 ARGANS & SMOS L2OS ESL.
1 © ACRI-ST, all rights reserved – 2012 Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)
UPDATE ON BIAS TRENDS, DIRECT SUN CORRECTION, AND ROUGHNESS CORRECTION Joe Tenerelli May 10, 2011.
AN INITIAL LOOK AT THE IMPACT OF THE NEW ANTENNA LOSS MODEL Joe Tenerelli SMOS QUALITY WORKING GROUP #4 7-9 March 2011.
UPDATE ON SMOS LONG-TERM BIASES OVER THE OCEAN AND ROUGH SURFACE SCATTERING OF CELESTIAL SKY NOISE Joe Tenerelli SMOS L2OS Progress Meeting Arles, France,
REVIEW OF OBSERVED BIAS TRENDS OVER THE OCEAN AND POTENTIAL IMPACT OF PROCESSOR EVOLUTION Joe & Nicolas IFREMER/CLS ESL Quality Working Group #5 May 30-31,
F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November ,
SMOS-BEC – Barcelona (Spain) CP34/BEC L3-L4 maps internal production chain BEC team SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona.
26/11/2012 – Observatoire de Paris Analysis of wind speed evolution over ocean derived from altimeter missions and models M. Ablain (CLS)
SMOS L2 Ocean Salinity Level 2 Ocean Salinity Using TEC estimated from Stokes 3 24 October 2012 ACRI-st, LOCEAN & ARGANS SMOS+polarimetry.
1 © ACRI-ST, all rights reserved – 2012 TEC estimation Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN)
Clima en España: Pasado, presente y futuro Madrid, Spain, 11 – 13 February 1 IMEDEA (UIB - CSIC), Mallorca, SPAIN. 2 National Oceanography Centre, Southampton,
How do model errors and localization approaches affects model parameter estimation Juan Ruiz, Takemasa Miyoshi and Masaru Kunii
GLOBAL BIASES IN THE DWELL-LINE MEAN STOKES PARAMETERS FROM SMOS FOR NOVEMBER 2010 Joe Tenerelli 25 February 2011.
The Aquarius Salinity Retrieval Algorithm Frank J. Wentz and Thomas Meissner, Remote Sensing Systems Gary S. Lagerloef, Earth and Space Research David.
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.
IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010.
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.
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Ifremer Planning of Cal/Val Activities during In orbit commisioning Phase N. Reul, J. Tenerelli, S. Brachet, F. Paul & F. Gaillard, ESL & GLOSCAL teams.
SMOS Validation Rehearsal Campaign Workshop, 18-19/11/2008, Noordwijkerhout, The Netherlands SMOS Validation Rehearsal Campaign Mediterranean flights C.
Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin (LOCEAN)
MWR Roughness Correction Algorithm for the Aquarius SSS Retrieval W. Linwood Jones, Yazan Hejazin, Salem Al-Nimri Central Florida Remote Sensing Lab University.
SMOS Science Workshop, Arles, th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC ERROR DIAGNOSTIC J. Gourrion, R. Sabia, M. Portabella,
OSTST Hobart 2007 – SLA consistency between Jason-1 and TOPEX data SLA consistency between Jason-1 and TOPEX/Poseidon data M.Ablain, S.Philipps,
Progress Meeting #27, April 2015, Barcelona SPAIN T3.2 Retrieval algorithm Estrella Olmedo BEC team SMOS Barcelona Expert Centre Pg. Marítim de la.
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.
UPDATE ON THE SUN GLINT Joe Tenerelli Ocean Data Lab SMOS Level 2 OS Progress Meeting 26 SMOS Barcelona Expert Centre Barcelona, Spain April 2015.
A. Montuori 1, M. Portabella 2, S. Guimbard 2, C. Gabarrò 2, M. Migliaccio 1 1 Dipartimento per le Tecnologie (DiT), University of Naples Parthenope, Italy.
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.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L2OS planning 2 July 2014 ARGANS & SMOS L2OS ESL 1.
SMOS AlgoVal meeting #16, Brest, 8-9 July, 2009 Computing Tb BOA and Tb surf C. Gabarró, J. Font SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta.
1 / 13 Current activities at ICM-SMOS-BEC J. Gourrion, C. Gabarró, R. Sabia, M. Talone, V. González, S. Montero, S. Guimbard, F. Pérez, J. Martínez, M.
1 Spectral filtering for CW searches S. D’Antonio *, S. Frasca %&, C. Palomba & * INFN Roma2 % Universita’ di Roma “La Sapienza” & INFN Roma Abstract:
SPCM-9, Esac, May 3 rd, 2012 MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES J. Gourrion, S. Guimbard, R. Sabia,
Dependence of SMOS/MIRAS brightness temperatures on wind speed and foam model Xiaobin Yin, Jacqueline Boutin LOCEAN & ARGANS.
Level 2 Algorithm. Definition of Product Levels LevelDescription Level 1 1A Reconstructed unprocessed instrument data 1B Geolocated, calibrated sensor.
OS-ESL meeting, Barcelona, February nd, 2011 OTT sensitivity study and Sun correction impact J. Gourrion and the SMOS-BEC team SMOS-BEC, ICM/CSIC.
EXTENDING THE LAND SEA CONTAMINATION CHARACTERIZATION TO THE EXTENDED ALIAS- FREE FIELD OF VIEW Joe Tenerelli (CLS) and Nicolas Reul (IFREMER) SMOS Quality.
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,
Introduction Martin et al. JGR, 2014 CAROLS airborne Tbs indicate slightly lower wind influence than predicted by model 1 at high WS In model 1 previous.
SMOS-BEC – Barcelona (Spain) Revealing Geophysically-Consistent Spatial Structures in SMOS Surface Salinity Derived Maps Marcos Portabella, Estrella Olmedo,
ESTIMATION OF OCEAN CURRENT VELOCITY IN COASTAL AREA USING RADARSAT-1 SAR IMAGES AND HF-RADAR DATA Moon-Kyung Kang 1, Hoonyol Lee 2, Chan-Su Yang 3, Wang-Jung.
SMOS QWG-6, ESRIN October 2011 OTT generation strategy and associated issues 1 The SMOS L2 OS Team.
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 v63x product design evolution 22 April 2015 ARGANS & SMOS L2OS ESL 1.
USING SMOS POLARIMETRIC BRIGHTNESS TEMPERATURES TO CORRECT FOR ROUGH SURFACE EMISSION BEFORE SALINITY INVERSION.
SMOS-BEC – Barcelona (Spain) LO calibration frequency impact Part II C. Gabarró, J. Martínez, V. González, A. Turiel & BEC team SMOS Barcelona Expert Centre.
Cal/Val Discussion. Summary No large errors in rain, freshening observed by Aquarius can be significant and real (up to about 3 hours on average after.
SMOS-BEC – Barcelona (Spain) Assessment of impact of new ECMWF cycle 38r2 BEC team SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona.
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.
Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop March GSFC.
SMOS Quality Working Group Meeting #2 Frascati (Rome), September 13 th -14 th,2010 SMOS-BEC Team.
SMOS QWG-9, ESRIN October 2012 L2OS: Product performance summary v550 highlights 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.
T. Meissner and F. Wentz Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014 Seattle. Washington,
SMOS-BEC – Barcelona (Spain) Variable LO freq. Cal. analysis LO at 2min from to BEC team SMOS Barcelona Expert Centre Pg. Marítim de.
21-23/04/2015PM27 J-L Vergely, J. Boutin, N. Kolodziejczyk, N. Martin, S. Marchand SMOS RFI/Outlier filtering.
1 Rosalia Daví 1 Václav Vavryčuk 2 Elli-Maria Charalampidou 2 Grzegorz Kwiatek 1 Institute of Geophysics, Academy of Sciences, Praha 2 GFZ German Research.
SMOS L2 Ocean Salinity – PM#25 1/20 Level 2 Ocean Salinity May 2013 A3TEC.
QWG-10 – 4-6 February 2013 – ESRIN (Italy) SMOS Level3 and Level 4 Research Products Provided by the Barcelona Expert Center Jordi Font and BEC team SMOS.
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.
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.
UPDATE ON GALACTIC NOISE CORRECTION Joe Tenerelli SMOS Quality Working Group #9 ESA ESRIN 24 October 2012.
Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.
Universitat Politècnica de Catalunya CORRECTION OF SPATIAL ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES L. Wu, I. Corbella, F. Torres, N. Duffo, M. Martín-Neira.
Ocean Salinity Science 2014, 26–28 November, Exeter (UK) J. Ballabrera, N. Hoareau, M. Portabella, E. Garcia-Ladona, A. Turiel SMOS Barcelona Expert Centre.
GPM Microwave Radiometer Vicarious Cold Calibration
Presentation transcript:

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 3.Present performance 4.Future evolution: ongoing studies

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Land sea contamination correction J. Martínez, V. González, C. Gabarró, J. Gourrion and BEC–TEAM SMOS Barcelona Expert Centre Pg. Marítim de la Barceloneta 37-49, Barcelona SPAIN URL:

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 3 Land contamination  Impact of correction implemented by Deimos on the strong halo around continental surfaces  to avoid multiplying the first Fourier parameter by the element of area (sqrt(3) * Distance_ratio * Distance_ratio/2)  L1PP run at BEC without and with correction  71 ascending orbits, 71 descending from August 2010  Tb at 42.5º; filtering 40 < Tb < 200  Tb maps: average per ISEA GP and then average for 1º*r*cos(lat).  SSS semi-orbits (problem in running several orbits at a time)

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Tb ascending maps

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Tb descending maps

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 6 Impact on SSS  SSS 3 semi-orbits  Run with patched L1PP and L2OS 3.17  Specific OTT computed from uncorrected and corrected L1

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 7 Uncorrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 8 Corrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 9 Uncorrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN 10 Corrected

SMOS QWG-5, 30 May – 1 June 2011, ESRIN Conclusion  The correction has removed the first order problem (strongest signal)  Back to the original scene dependant bias issue (A. Camps 2005)? 11

SMOS QWG-5, 30 May – 1 June 2011, ESRIN  Pre-launch semi-empirical roughness model (SSS3) was derived from data obtained during the WISE experiments ( ) on an oil platform in the NW Mediterranean  New fitting using actual SMOS data (residual after removing the rest of modelled emission components)  Guimbard et al., “SMOS semi-empirical ocean forward model adjustment” submitted to TGRS SMOS special issue 12 New semi-empirical roughness model

SMOS QWG-5, 30 May – 1 June 2011, ESRIN New semi-empirical roughness model 13

QWG-5, Frascati, May st, 2011 OTT sensitivity study J. Gourrion, M. Portabella, R. Sabia, S.Guimbard SMOS-BEC, ICM/CSIC

QWG-5, Frascati, May st, 2011 OTT sensitivity  DPGS OTT  Impact on OTT quality of different factors: 1.Number of snapshots used 2.Temporal variability and apparent drift 3.Latitudinal variability  Alternative OTT estimation strategy  Method and preliminary results

QWG-5, Frascati, May st, 2011  For a 16-days period dataset (Aug. 3 rd – Aug 18 th ), about snapshots are available after comprehensive filtering (land, outliers, descending overpasses)  N OTTs are computed by randomly selecting n snapshots among all available. (N-1) rms difference of the N OTTs are then computed.  N decreases with increasing n, leading to N=2 when n=6000, i.e., about half of the total amount in the 16-days period.  For consistency, the same experiment is repeated for two additional 16-days periods (Aug. 19 th – Sep 3 rd, Sep. 4 th – Sep 19 th ). The overall rms values are obtained by averaging the 3 16-day period scores.  As expected, OTT robustness depends on number of snapshots used. Current operational OTT has a 0.25K error only due to sampling. OTT sensitivity Impact of number of snapshots

QWG-5, Frascati, May st, 2011 OTT sensitivity Temporal variability  A 48-days period dataset (August-Sept 2010) is used and split into 8-days subsets. Same filtering than previous experiment.  The reference situation is given by the first 8-days subset.  For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n =  The OTT rms increase (relative to reference) indicates an increasing data inconsistency with time, i.e., apparent drift.

QWG-5, Frascati, May st, 2011 Ocean/ice transition Salinity ? Rain ? Roughness residuals ? New model 3 SSA/SPM model OTT sensitivity Latitudinal variability  A 16-day period dataset (Aug. 3 rd – Aug 18 th ) is used and split into 6° latitudinal band subsets.  The reference situation is given by the [36° S, 30° S] latitudinal band subset.  For each subset, a fixed number of snapshots are randomly selected to compute an OTT, n = 610.  The OTT rms differences (relative to reference) mainly indicate potential forward model and auxiliary data errors.

QWG-5, Frascati, May st, 2011 OTT sensitivity OTT as mean departure from full forward model: summary  OTT robustness significantly depends on sampling. Current OTT computation should use a larger number of snapshots.  Temporal inconsistencies due to non-modelled instrumental/reconstruction instability and imperfect Foreign Sources modelling  Latitudinal inconsistencies due to imperfect modelling or auxiliary parameters  OTTs estimated from different datasets will vary depending on the distribution of sampled geophysical conditions  With current OTT methodology, the data are adjusted to reproduce the mean forward model behaviour (e.g., angular dependency): updated forward models are NOT independent from pre-launch versions (used to compute the OTT)

QWG-5, Frascati, May st, 2011 OTT sensitivity New and prelaunch forward models have similar angular dependence

QWG-5, Frascati, May st, 2011 OTT sensitivity  Objective: Estimate systematic errors in the antenna frame while avoiding use of forward models as much as possible  Main differences with current OTT:  do not use forward models  do not assume that geophysical variability is negligible BUT  select specific environmental conditions (U,SST,SSS,low galactic,…)  MEAN angular dependency is fitted with a simple polynomial function and removed from the mean scene to obtain the systematic error pattern  Work in progress: only five days of data processed in this study. New OTT estimation method: basics (1)

QWG-5, Frascati, May st, 2011 OTT sensitivity  Data are averaged over a large number of epochs: local geophysical anomalies are spread over the whole average image. They contribute more to systematic angular (varying with incidence angle) biases and less to residual variability WHILE OTT estimation method accounts for systematic angular biases  Auxiliary information only used for data selection before OTT computation (except for Faraday rotation effects)  X/Y data are rotated to obtain H/V scenes, while H/V OTTs are rotated back to get X/Y OTTs New OTT estimation method: basics (2)

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: comparison INCONSISTENT ANGULAR DEPENDENCE BETWEEN SMOS DATA AND PRE-LAUNCH FORWARD MODELS

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: stability (1) Selecting different wind speed conditions RMS VALUES CONSISTENT WITH EXPECTED VALUES FROM NUMBER OF SAMPLES – GRANULAR PATTERNS

QWG-5, Frascati, May st, 2011 OTT sensitivity Alternative OTT estimation method: stability (2) Impact of atmospheric corrections

QWG-5, Frascati, May st, 2011 OTT sensitivity New OTT estimation method: summary  Adequate data selection techniques + mean angular dependence removal allows to obtain ROBUST OTT estimates WITHOUT introducing systematic errors from imperfect forward model and auxiliary information  Temporal drift effects still need to be accounted for.  Angular dependence of the corrected images is consistent with the original SMOS data  Work in progress:  Use more data  Further analyze latitudinal and temporal variations  New GMF fit using new OTT  Near-future work will compare the goodness of either additive or multiplicative formulations.