EXTENDING THE LAND SEA CONTAMINATION CHARACTERIZATION TO THE EXTENDED ALIAS- FREE FIELD OF VIEW Joe Tenerelli (CLS) and Nicolas Reul (IFREMER) SMOS Quality.

Slides:



Advertisements
Similar presentations
BIAS TRENDS IN THE 1-SLOPE (REPROCESSING) AND CALIBRATED L1 BRIGHTNESS TEMPERATURES Joe Tenerelli SMOS Payload Calibration Meeting September 2012.
Advertisements

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 L2 Ocean Salinity Level 2 Ocean Salinity L20S Tool Box Architecture & Release 27 June 2014 ARGANS & SMOS L2OS ESL.
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,
PART 2: A QUICK COMPARISON OF V504 AND V620 GLOBAL MAPS Joe Tenerelli SMOS Calibration Meeting 18 26/05/2014.
SMOS – in situ comparisons J. Boutin*, N. Martin*, O. Hernandez*, N. Reul , G. Reverdin* *LOCEAN,  IFREMER.
F. Wentz, T. Meissner, J. Scott and K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November ,
OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements.
SMOS L1v620-L2v613 versus L1v505- L2v550 validation May 2011 Nicolas Martin, Jacqueline Boutin LOCEAN 26 May 2014.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L1 -> L2OS tools 12 February 2014 ARGANS & SMOS L2OS ESL.
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)
GLOBAL BIASES IN THE DWELL-LINE MEAN STOKES PARAMETERS FROM SMOS FOR NOVEMBER 2010 Joe Tenerelli 25 February 2011.
Aquarius Status Salinity Retrieval and Applications D. M. Le Vine NASA/GSFC, Greenbelt, MD E. P. Dinnat, G. Lagerloef, P. de Matthaeis, H. Kao, F.
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.
Preliminary results on Formation and variability of North Atlantic sea surface salinity maximum in a global GCM Tangdong Qu International Pacific Research.
IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010.
ElectroScience Lab Studies of Radio Frequency Interference in SMOS Observations IGARSS 2011 Joel T. Johnson and Mustafa Aksoy Department of Electrical.
1 Boutin et al., Avril 2015, SMOS-OCEAN TOSCA SMOS Salinity anomalies – Towards the correction of SMOS SSS systematic biases - J. Boutin 1, N. Martin 1,
1 Boutin et al., 2014 SMOS Salinity anomalies: new insights into SMOS capability at sensing SSS variability and into the improvements to be made in the.
DIRECT TROPOSPHERIC OZONE RETRIEVALS FROM SATELLITE ULTRAVIOLET RADIANCES Alexander D. Frolov, University of Maryland Robert D. Hudson, University of.
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.
1.STSE 2.Objectives of today 3.Data availability 4.Reprocessing 5.RFI 6.Conferences & user meetings Introduction – SMOS mission status.
1 Improved Sea Surface Temperature (SST) Analyses for Climate NOAA’s National Climatic Data Center Asheville, NC Thomas M. Smith Richard W. Reynolds Kenneth.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity status 4 February 2013 ARGANS.
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 L2 Ocean Salinity – PM#25 1/20 Level 2 Ocean Salinity May 2013 OTT post-processor.
SMOS Science Workshop, Arles, th Sept, 2011 IMPROVING SMOS SALINITY RETRIEVAL: SYSTEMATIC ERROR DIAGNOSTIC J. Gourrion, R. Sabia, M. Portabella,
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.
Spatially Complete Global Surface Albedos Derived from MODIS Data
Objectives of the Workshop, Results of SMOS+ Surface Ocean Salinity (SOS) Ellis Ash (SatOC) Christine Gommenginger, Chris Banks, Eleni Tzortzi (NOC) Jacqueline.
Calibration and Validation Studies for Aquarius Salinity Retrieval PI: Shannon Brown Co-Is: Shailen Desai and Anthony Scodary Jet Propulsion Laboratory,
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 REVIEW OF BIAS PATTERNS IN THE MIRAS BRIGHTNESS TEMPERATURES OVER THE OCEAN Joe Tenerelli SMOS Quality Working Group # Feb 2013 ESRIN.
Séverine Fournier, Nicolas Reul, Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER Joe Salisbury, Doug Vandemark University of New Hampshire,
Ocean Salinity validation of mission requirements review / improvements: Points of Reflexion ESL teams Mission Requirements: The so-called GODAE requirements:
Sea surface salinity from space: new tools for the ocean color community Joe Salisbury, Doug Vandemark, Chris Hunt, Janet Campbell, Dominic Wisser, Tim.
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.
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.
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,
SMOS-BEC – Barcelona (Spain) Revealing Geophysically-Consistent Spatial Structures in SMOS Surface Salinity Derived Maps Marcos Portabella, Estrella Olmedo,
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.
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.
Estimating SMOS error structure using triple collocation Delphine Leroux, CESBIO, France Yann Kerr, CESBIO, France Philippe Richaume, CESBIO, France 1.
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,
21-23/04/2015PM27 J-L Vergely, J. Boutin, N. Kolodziejczyk, N. Martin, S. Marchand SMOS RFI/Outlier filtering.
SMOS Science Meeting September 2011 Arles, FR Simulating Aquarius by Resampling SMOS Gary Lagerloef, Yann Kerr & Eric Anterrieu and Initial Results.
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 L2OS v611 status 12 February 2014 ARGANS & SMOS L2OS ESL.
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.
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.
Validating SMAP SSS with in situ measurements
Spatial Modes of Salinity and Temperature Comparison with PDO index
Argo Delayed-Mode Salinity Data
Aquarius SSS space/time biases with respect to Argo data
‘Aquarius’ Maps Ocean Salinity Fine-scale Structure
NOAA Objective Sea Surface Salinity Analysis P. Xie, Y. Xue, and A
Presentation transcript:

EXTENDING THE LAND SEA CONTAMINATION CHARACTERIZATION TO THE EXTENDED ALIAS- FREE FIELD OF VIEW Joe Tenerelli (CLS) and Nicolas Reul (IFREMER) SMOS Quality Working Group #15 ESA ESRIN 6-8 October 2014

REMINDER OF THE PROBLEM There is a significant bias in the retrieved surface salinity around all of the continents, and this bias seems to be stable over the entire mission. By significant I mean that the biases can exceed 2 pss, while the range of SSS over the global oceans is pss (away from river plumes and freshwater lenses). We will call this bias land-sea contamination, or LSC.

AN EXAMPLE OF LSC AROUND AUSTRALIA BASED UPON ARGO MEASUREMENTS FROM

AN EXAMPLE OF LSC AROUND SOUTH AMERICA BASED UPON ARGO MEASUREMENTS FROM

GLOBAL LSC IN TERMS OF FIRST STOKES PARAMETER ASCENDING PASSES FOR MAY 2011: FIRST STOKES PARAMETER BIAS The LSC is global:

ASCENDING PASSES FOR MAY 2011: RETIEVED SALINITY BIAS GLOBAL LSC IN TERMS OF RETRIEVED SALINITY The LSC is global:

ASCENDING PASSES FOR MAY 2011: RETIEVED SALINITY BIAS Note: In this presentation SMOS SSS is retrieved using the first Stokes parameter and a simple linear retrieval algorithm. GLOBAL LSC IN TERMS OF RETRIEVED SALINITY

LSC IS SCENE-DEPENDENT scene-dependent change as the distribution of land over the front half space changes The biases are also scene-dependent and therefore change as the distribution of land over the front half space changes:

THE LSC CORRECTION

KEY POINTS OF THE EMPIRICAL LSC CORRECTION latitude-longitude grid: 0.5x0.5 deg lat-lon grid Director cosine grid: 0.025x0.025 director cosine units rather than 0.1x0.1 director cosine units Two lookup tables are calculated: One for RFI-flagged snapshots and another for non-RFI-flagged (nominal) snapshots. 41,469 half-orbits are used to compute the mission average land contamination. Biases in all four Stokes parameters are computed. Final correction is weighted by a function that approaches zero as the fraction of land in the front half space approaches 0.2% and approaches one as the fraction approaches 1%. The correction also approaches zero as the ice fraction approached 0.02%.

DISCRETIZATION

PROCESSING STEPS (1) Successive snapshots (‘scene’) (Tx,Ty,Uxy,Vxy) on hex grid Flag scene for RFI: Tx > 500 K or Ty > 500 K or Uxy > 200 K Compute bias relative to forward model (ref. SSS is WOA-2009) for all four Stokes parameters in instrument polarization basis Grid biases onto ISEA-4H9 grid and identify (xi,eta) bins Accumulate data into daily maps on 0.5°x0.5° lat-lon grid; remove gridpoints with no measurements; introduce linear indexing for retained gridpoints Accumulate data into monthly maps; keep track of pointwise measurement counts Create separate monthly files for each variable Average the monthly Stokes parameter bias maps over the full period (Jan 2010-June 2014) RFI-flagged RFI-free

PROCESSING STEPS (2) Remove gridpoints with fewer than 10 passes entering into the average For each (xi,eta) cell, remove average bias over all (lat,lon) cells with no land or ice in the front half-space; removes impact of the choice of OTT Apply linear weighting function to biases which ramps down from 1 at FHS land fraction of 1% to 0 at land fraction of 0.2%. Apply similar weighting function that ramps to 0 as the ice fraction approaches 0.02%. Set correction to zero south of 60°S latitude Merge all Stokes parameter biases (single precision) into a single file for each pass direction and for RFI and non-RFI- flagged scenes. Each file is 2.45 GB in size with about 88 million gridpoints in each.

DISCRETIZATION

EXAMPLE BIAS OVER EAF-FOV

MEASUREMENT COUNT FOR THE AVERAGING non-RFI-flagged snapshots

EXAMPLE MAP OF THE FIRST STOKES BIAS non-RFI-flagged snapshots

MEASUREMENT COUNTS FOR THE AVERAGING RFI-flagged snapshots

EXAMPLE MAP OF THE FIRST STOKES BIAS RFI-flagged snapshots

EVALUATION OF THE CORRECTION 1.Global perspective for May Impact in areas with stable SSS: Australia and South America in May 2011 using ISAS as the reference 3.Comparison with ARGO data around Australia and South America over the period Impact in areas with strongly varying SSS: Panama and the Amazon plume regions

EVALUATION OF THE CORRECTION 1.Global perspective for May Impact in areas with stable SSS: Australia and South America in May 2011 using ISAS as the reference 3.Comparison with ARGO data around Australia and South America over the period Impact in areas with strongly varying SSS: Panama and the Amazon plume regions

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MONTHLY MAP FIRST STOKES PARAMETER DIVIDED BY TWO BEFORE CORRECTION

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MONTHLY MAP FIRST STOKES PARAMETER DIVIDED BY TWO AFTER CORRECTION

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MONTHLY MAP DESCENDING-ASCENDING BIAS BEFORE CORRECTION

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MONTHLY MAP DESCENDING-ASCENDING BIAS AFTER CORRECTION

EVALUATION OF THE CORRECTION 1.Global perspective for May Impact in areas with stable SSS: Australia and South America in May 2011 using ISAS as the reference 3.Comparison with ARGO data around Australia and South America over the period Impact in areas with strongly varying SSS: Panama and the Amazon plume regions

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS First consider two areas with stable surface salinity whose distribution is well-measured by ARGO floats.

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS First consider two areas with stable surface salinity whose distribution is well-measured by ARGO floats.

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS First consider two areas with stable surface salinity whose distribution is well-measured by ARGO floats.

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS

CORRECTION REDUCES VARIATION OF BIAS WITH LAND FRACTION OUTSIDE THE FUNDAMENTAL HEXAGON

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS CORRECTION REDUCES VARIATION OF BIAS WITH LAND FRACTION OUTSIDE THE FUNDAMENTAL HEXAGON

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS

BIAS APPEARS AS SOON AS LAND APPEARS OUTSIDE THE FUNDAMENTAL HEXAGON

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS BIAS APPROACHES 2.5 PSU AS THE LAND FRACTION APPROACHES 0.9.

INITIAL EVALUATION OF THE CORRECTION USING THE MONTH OF MAY 2011 AND THE ISAS MAPS

CORRECTION SEEMS TO REDUCE THE ABSOLUTE ERROR TO ABOUT KM FROM THE COAST. CORRECTION IS LESS EFFECTIVE WITHIN 150 KM OF THE COAST.

EVALUATION OF THE CORRECTION 1.Global perspective for May Impact in areas with stable SSS: Australia and South America in May 2011 using ISAS as the reference 3.Comparison with ARGO data around Australia and South America over the period Impact in areas with strongly varying SSS: Panama and the Amazon plume regions

ARGO COLLOCATIONS: AUSTRALIA

TIME PERIOD: ARGO-SMOS collocations are binned into 50 km wide bands (as a function of distance to coast). Biases are then computed in these bands.

ARGO COLLOCATIONS: AUSTRALIA UNCORRECTED SMOS SSS

ARGO COLLOCATIONS: AUSTRALIA CORRECTED SMOS SSS

ARGO COLLOCATIONS: AUSTRALIA

bias reduction of about 0.5 pss

ARGO COLLOCATIONS: AUSTRALIA Impact of the LSC correction drops rapidly within about 200 km of the coast

ARGO COLLOCATIONS: AUSTRALIA bias reduction of about 0.8 pss

A COMPARISON WITH SHIP TSG DATA WITHOUT LSC CORRECTION

A COMPARISON WITH SHIP TSG DATA WITH LSC CORRECTION

ARGO COLLOCATIONS: SOUTH AMERICA

bias reduction of about 1 pss

ARGO COLLOCATIONS: SOUTH AMERICA bias reduction of about 2 pss

EVALUATION OF THE CORRECTION 1.Global perspective for May Impact in areas with stable SSS: Australia and South America in May 2011 using ISAS as the reference 3.Comparison with ARGO data around Australia and South America over the period Impact in areas with strongly varying SSS: Panama and the Amazon plume regions

DYNAMIC ZONE: PANAMA Comparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA Comparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA bias reduction of nearly 2 pss

DYNAMIC ZONE: PANAMA Comparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA Comparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA bias reduction of about 1 pss

AMAZON PLUME: ANACONDA TSG Comparing Anaconda transects with 10-day SMOS SSS

AMAZON PLUME: ANACONDA TSG Comparing Anaconda transects with 10-day SMOS SSS

AMAZON PLUME: ANACONDA TSG Comparing Anaconda transects with 10-day SMOS SSS

AMAZON PLUME: ANACONDA TSG Comparing Anaconda transects with 10-day SMOS SSS Corrected SMOS SSS differs from the reference used to derive the LSC correction.

AMAZON PLUME: ANACONDA TSG

LSC correction seems to overcorrect the bias (when compared to the ship TSG) in two zones along the ship track. In fact, the corrected SSS exceeds the reference used to derive the LSC correction (black curve)! To be investigated further…

CONCLUSIONS Introduced the new land sea contamination lookup table, now defined over the extended alias-free field of view. Comparison with ISAS monthly SSS map for May 2011 around Australia and South America shows reduction in LSC. Comparisons with ARGO from also show reduction in the LSC. Reductions in LSC may also be seen in regions of strongly time varying SSS such as near Panama and in the Amazon plume region. However, results are mixed in these areas and cases have been found in which application of the correction seems to increase the biases relative to in-situ measurements. This must be investigated further.

REMAINING ISSUES Difficult to derive the correction in RFI-contaminated areas such as the west Pacific, north Atlantic and Pacific, and some areas around Africa and South America. Impossible to derive the correction where the ice distribution varies with time in the field of view. Correction lookup table remains contaminated in some areas, possible due to residual RFI that is not detected by the snapshot filter. Latitudinal drift is not corrected when deriving the correction, and this drift may impact the correction. Method of handling RFI requires testing and refinement. A forward model is required to derive the correction, and errors in this model will impact the correction. Reference for salinity for the forward model is WOA Need to examine use of better reference such as Aquarius SSS maps or ISAS maps. Analysis based upon v500 data. Analysis needs to be redone using the data from the latest reprocessing campaign. Analysis based upon v500 data. Analysis needs to be redone using the data from the latest reprocessing campaign.

AMAZON PLUME