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,

Slides:



Advertisements
Similar presentations
1 © ACRI-ST, all rights reserved – 2012 Isotropic RFI detection Jean-Luc Vergely (ACRI-ST) Claire Henocq (ACRI-ST) Philippe Waldteufel (LATMOS)
Advertisements

BIAS TRENDS IN THE 1-SLOPE (REPROCESSING) AND CALIBRATED L1 BRIGHTNESS TEMPERATURES Joe Tenerelli SMOS Payload Calibration Meeting September 2012.
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 – Reprocessing Level 2 Ocean Salinity Reprocessing 17 September 2008.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L20S Tool Box Architecture & Release 27 June 2014 ARGANS & SMOS L2OS ESL.
SMOS L2 Ocean Salinity Reprocessed constant calibration L1c OTT drift study 13 April 2011 ARGANS & L2OS ESL ascendingdescending.
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.
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.
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,
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.
GOME-2 polarisation data and products L.G. Tilstra (1,2), I. Aben (1), P. Stammes (2) (1) SRON; (2) KNMI GSAG #42, EUMETSAT,
OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L1 -> L2OS tools 12 February 2014 ARGANS & SMOS L2OS ESL.
Time & Frequency Products R. Peřestý, J. Kraus, SWRM 4 th Data Quality Workshop 2-5 December 2014 GFZ Potsdam Recent results on ACC Data Processing 1 SWARM.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity Using TEC estimated from Stokes 3 24 October 2012 ACRI-st, LOCEAN & ARGANS SMOS+polarimetry.
GLOBAL BIASES IN THE DWELL-LINE MEAN STOKES PARAMETERS FROM SMOS FOR NOVEMBER 2010 Joe Tenerelli 25 February 2011.
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.
ElectroScience Lab Studies of Radio Frequency Interference in SMOS Observations IGARSS 2011 Joel T. Johnson and Mustafa Aksoy Department of Electrical.
Maintaining and Improving the AMSR-E and WindSat Ocean Products Frank J. Wentz Remote Sensing Systems, Santa Rosa CA AMSR TIM Agenda 4-5 September 2013.
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.
An Update on A Few Issues Relevant to Ocean Salinity Retrieval for SMOS Joe Tenerelli SMOS Quality Working Group 11 July ESA ESRIN.
1.STSE 2.Objectives of today 3.Data availability 4.Reprocessing 5.RFI 6.Conferences & user meetings Introduction – SMOS mission status.
Microwindow Selection for the MIPAS Reduced Resolution Mode INTRODUCTION Microwindows are the small subsets of the complete MIPAS spectrum which are used.
Recent activities on utilization of microwave imager data in the JMA NWP system - Preparation for AMSR2 data assimilation - Masahiro Kazumori Japan Meteorological.
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 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.
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 REVIEW OF BIAS PATTERNS IN THE MIRAS BRIGHTNESS TEMPERATURES OVER THE OCEAN Joe Tenerelli SMOS Quality Working Group # Feb 2013 ESRIN.
SMOS L2 Ocean Salinity Level 2 Ocean Salinity L2OS planning 2 July 2014 ARGANS & SMOS L2OS ESL 1.
SPCM-9, Esac, May 3 rd, 2012 MODEL-INDEPENDENT ESTIMATION OF SYSTEMATIC ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES J. Gourrion, S. Guimbard, R. Sabia,
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,
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.
IOCP High Level Plan, 8 September In-Orbit Commissioning Phase: High Level Planning.
SMOS-BEC – Barcelona (Spain) LO calibration frequency impact Part II C. Gabarró, J. Martínez, V. González, A. Turiel & BEC team SMOS Barcelona Expert Centre.
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 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.
MODIS Preprocessing (before L1B) Changes over the Last Year (and looking forward) Chris Moeller and others CIMSS; Univ. Wisconsin July 13, 2004 Thanks.
TEC introduced high ratios of std(Tbmeas-Tbmodel)/ radio_accuracy X. Yin, J. Boutin LOCEAN.
21-23/04/2015PM27 J-L Vergely, J. Boutin, N. Kolodziejczyk, N. Martin, S. Marchand SMOS RFI/Outlier filtering.
Orbit comparison for Jason-2 mission between GFZ and CNES (POE-D) The GFZ orbit is referred to as GFZ in the following study The CNES orbit is referred.
SMOS Science Meeting September 2011 Arles, FR Simulating Aquarius by Resampling SMOS Gary Lagerloef, Yann Kerr & Eric Anterrieu and Initial Results.
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.
1 Summary of CFS ENSO Forecast September 2010 update Mingyue Chen, Wanqiu Wang and Arun Kumar Climate Prediction Center 1.Latest forecast of Nino3.4 index.
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.
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.
1 Summary of CFS ENSO Forecast December 2010 update Mingyue Chen, Wanqiu Wang and Arun Kumar Climate Prediction Center 1.Latest forecast of Nino3.4 index.
1 Summary of CFS ENSO Forecast August 2010 update Mingyue Chen, Wanqiu Wang and Arun Kumar Climate Prediction Center 1.Latest forecast of Nino3.4 index.
The Population of Near-Earth Asteroids and Current Survey Completion Alan W. Harris MoreData! : The Golden Age of Solar System Exploration Rome,
JMA GPRC report Arata OKUYAMA Meteorological Satellite Center,
Blending multiple-source wind data including SMOS, SMAP and AMSR-2 new products Joe TENERELLI, Fabrice COLLARD OceanDataLab, Brest, France.
Spatial Modes of Salinity and Temperature Comparison with PDO index
On-Orbit Performance and Calibration of the HMI Instrument J
Observing Climate Variability and Change
Aquarius SSS space/time biases with respect to Argo data
‘Aquarius’ Maps Ocean Salinity Fine-scale Structure
Constructing Climate Graphs
Midnight calibration errors on MTSAT-2
Presentation transcript:

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, 2011

July Aug Sep Oct Nov Dec Descending passesAscending passes. Latitudinal Drift Apparent in L3 SSS desc from Oct to Dec Differing behaviour asc/desc Very Difficult to merge both passes !

Figure 2: : 10-day running mean window average evolution of the global mean differences between SMOS level 2 and in situ SSS from July to January All Data (Black), Ascending passes (blue) and Descending passes (red) Processor Version changes Asc SSS saltier than Desc SSS by ~0.7 psu Since 4th Aug Stable temporal variance of the error Fluctuating temporal mean error: ~ psu

Descending passesAscending passes. Very Significant Latitudinal Bias In desc Passes after mid-oct: Sun ?

SUN GEOMETRY REVIEW The following figures show sun angle from boresight as a function of time of year and latitude for descending and ascending passes.

REVIEW OF BIAS TRENDS OVER THE OCEAN Long term bias trends for original DPGS Level 1B data, referenced to average of early June ascending and descending passes. For the dashed curves no correction for the June-July jump has been made, while for the solid curves offsets have been applied in early June and early August, where the offsets have been based on consecutive passes (of a given direction) over the Pacific.

Long term bias trends for original DPGS Level 1B data including only the corrected curves. REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for original DPGS Level 1B data including only the corrected curves. Tb(desc) > Tb(asc) Direct sun correction + thermal loss variations + scattered galactic noise modeling error: Tb(desc) > Tb(asc) Tb(desc) < Tb(asc) Direct sun correction + thermal loss variations: Tb(desc) < Tb(asc) crossover of desc-asc discrepancy early May and early-mid August REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for Commissioning reprocessing Level 1B data, referenced to average of early June ascending and descending passes. The FTR and NIR calibration parameters remains fixed for the entire period. FIXED NIR CAL FIXED FTR DIRECTSUN CORRECTION WITH V(0,0) and BHS BUGS NO THERMAL LOSS MODEL REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for Commissioning reprocessing Level 1A data reprocessed using JRECON image reconstruction with no direct sun correction. Aside from the area element discrepancy which has little impact over open ocean, main difference between DPGS and JRECON solutions is lack of direct sun correction in JRECON solutions. FIXED NIR CAL FIXED FTR NO DIRECT SUN CORRECTION NO THERMAL LOSS MODEL REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for Commissioning reprocessing Level 1A data reprocessed using JRECON image reconstruction with no direct sun correction. Aside from the area element discrepancy which has little impact over open ocean, main difference between DPGS and JRECON solutions is lack of direct sun correction in JRECON solutions. Tb(desc) > Tb(asc) Problem here is reduced to impact of thermal loss variations + scattered galactic noise modeling error: Tb(desc) > Tb(asc) Tb(desc) < Tb(asc) Direct sun correction problem is gone leaving only the thermal loss variation impact : Tb(desc) < Tb(asc) Timing of crossovers of desc-asc discrepancy change only slightly FIXED NIR CAL FIXED FTR NO DIRECT SUN CORRECTION NO THERMAL LOSS MODEL REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for Commissioning reprocessing Level 1B data, referenced to average of early June ascending and descending passes. The FTR and NIR calibration parameters remains fixed for the entire period. Tb(desc) > Tb(asc) Problem here is reduced to impact of thermal loss variations + scattered galactic noise modeling error: Tb(desc) > Tb(asc) Tb(desc) < Tb(asc) Direct sun correction problem is gone leaving only the thermal loss variation impact : Tb(desc) < Tb(asc) Timing of crossovers of desc-asc discrepancy change only slightly FIXED NIR CAL FIXED FTR DIRECTSUN CORRECTION WITH V(0,0 and BHS BUGS NO THERMAL LOSS MODEL REVIEW OF BIAS TRENDS OVER THE OCEAN

Long term bias trends for Commissioning reprocessing Level 1A data reprocessed using JRECON image reconstruction with no direct sun correction. Aside from the area element discrepancy which has little impact over open ocean, main difference between DPGS and JRECON solutions is lack of direct sun correction in JRECON solutions. Tb(desc) > Tb(asc) Problem here is reduced to impact of thermal loss variations + scattered galactic noise modeling error: Tb(desc) > Tb(asc) Tb(desc) < Tb(asc) Direct sun correction problem is gone leaving only the thermal loss variation impact : Tb(desc) < Tb(asc) Timing of crossovers of desc-asc discrepancy change only slightly FIXED NIR CAL NO FTT NO DIRECT SUN CORRECTION NO THERMAL LOSS MODEL REVIEW OF BIAS TRENDS OVER THE OCEAN

Next we considered a reprocessing in which NIR parameters are updated in an optimal way using the periodic cold sky calibrations. Here we have not used the FTT and so the solutions should be very close to those obtained with fixed FTR. REVIEW OF BIAS TRENDS OVER THE OCEAN Tb(desc) > Tb(asc) Problem here is reduced to impact of thermal loss variations + scattered galactic noise modeling error: Tb(desc) > Tb(asc) Tb(desc) < Tb(asc) Direct sun correction problem is gone leaving only the thermal loss variation impact : Tb(desc) < Tb(asc) Timing of crossovers of desc-asc discrepancy change only slightly OPTIMAL NIR CAL NO FTT NO DIRECT SUN CORRECTION NO THERMAL LOSS MODEL

Next we considered a reprocessing in which NIR parameters are updated in an optimal way using the periodic cold sky calibrations. Here we have not used the FTT and so the solutions should be very close to those obtained with fixed FTR. REVIEW OF BIAS TRENDS OVER THE OCEAN Tb(desc) > Tb(asc) Dsc-asc discrepancies in Sep-Oct are much reduced and the remaining discrepancy is likely related to scattered galactic noise modeling error: Tb(desc) > Tb(asc) No direct sun correction and new loss model resolve discrepany almost completely in May-August Timing of crossovers of desc-asc discrepancy change only slightly OPTIMAL NIR CAL NO FTT NO DIRECT SUN CORRECTION COMPLETE NEW THERMAL LOSS MODEL

Next we considered a reprocessing in which NIR parameters are updated in an optimal way using the periodic cold sky calibrations. REVIEW OF BIAS TRENDS OVER THE OCEAN Updating the FTR changes significantly the bias trends up through early May. FTR updates are marked by black vertical bars. OPTIMAL NIR CAL NO FTT NO DIRECT SUN CORRECTION COMPLETE NEW THERMAL LOSS MODEL

Next we considered a reprocessing in which NIR parameters are updated in an optimal way using the periodic cold sky calibrations. Here we have not used the FTT and so the solutions should be very close to those obtained with fixed FTR. REVIEW OF BIAS TRENDS OVER THE OCEAN Updating the FTR changes significantly the bias trends up through early May. FTR updates are marked by black vertical bars. OPTIMAL NIR CAL FTT/UPDATED FTR NO DIRECT SUN CORRECTION COMPLETE NEW THERMAL LOSS MODEL

Here we replot the bias curves for the solutions without the new loss model but without offsetting the curves in any way in order to provide a feel for absolute biases and EAF-AF bias discrepancies. REVIEW OF BIAS TRENDS OVER THE OCEAN OPTIMAL NIR CAL FTT/UPDATED FTR NO DIRECT SUN CORRECTION COMPLETE NEW THERMAL LOSS MODEL

Here we replot the bias curves for the solutions with the new loss model and without offsetting the curves in any way in order to provide a feel for absolute biases and EAF-AF bias discrepancies. REVIEW OF BIAS TRENDS OVER THE OCEAN OPTIMAL NIR CAL FTT/UPDATED FTR NO DIRECT SUN CORRECTION COMPLETE NEW THERMAL LOSS MODEL

Descending minus ascending L1-JRECON reveals a complex pattern of biases and bias trends associated with the buggy direct sun correction. REVIEW OF BIAS TRENDS OVER THE OCEAN

UPDATE ON DIRECT SUN CORRECTION The impact of the V(0,0) and back half space solid angle bugs appears clearly in the latitude-time bias plots. Here we show for the Commissioning Reprocessing Pacific descending passes a hovmoller plot of the difference between AF-FOV bias for solutions obtained using JRECON with no direct sun correction and those obtained from L1OP L1B files, which include the direct sun correction with BOTH the V(0,0) and back half space bugs. The sun passes between the front and back of the array along the solid black curves. Clearly evident is the latitudinal bias introduced by the V(0,0) bug (when the sun is in the front half space) between October and March and the negative bias (with strange jumps) introduced by the solid angle bug when the sun is in the back half space. This plot shows how these two problems with the direct sun correction have introduced significant biases on both short and long time scales, obscuring the true impact of the thermal loss model. Sun in front: V(0,0) bug Sun in back: Solid angle bug Sun in back: Solid angle bug Sun in back: Solid angle bug Sun in front: V(0,0) bug

UPDATE ON DIRECT SUN CORRECTION The impact of the V(0,0) and back half space solid angle bugs appears clearly in the latitude-time bias plots. Here we show for the Commissioning Reprocessing Pacific descending passes a hovmoller plot of the difference between AF-FOV bias for solutions obtained using JRECON with no direct sun correction and those obtained from L1OP L1B files, which include the direct sun correction with BOTH the V(0,0) and back half space bugs. The sun passes between the front and back of the array along the solid black curves. Clearly evident is the latitudinal bias introduced by the V(0,0) bug (when the sun is in the front half space) between October and March and the negative bias (with strange jumps) introduced by the solid angle bug when the sun is in the back half space. This plot shows how these two problems with the direct sun correction have introduced significant biases on both short and long time scales, obscuring the true impact of the thermal loss model. Sun in front: V(0,0) bug Sun in back: Solid angle bug Sun in back: Solid angle bug Sun in front: V(0,0) bug

UPDATE ON DIRECT SUN CORRECTION Descending minus ascending L1-JRECON reveals a complex pattern of biases and bias trends associated with the buggy direct sun correction.

UPDATE ON DIRECT SUN CORRECTION In the figure below we show the difference in AF-FOV (Tx+Ty)/2 bias between five possible solutions and a reference solution for the Nov 9 descending pass. The reference solution is Roger’s latest run using the new loss model with no direct sun correction. The blue curve shows the solution difference obtained using the direct sun correction with the V(0,0) fix but with the solid angle bug when the sun is in the back half-space. Bias differences are close to zero north of about 50 degS, but note the jump in bias when the sun passes into the BHS around 50 degS. The red curve shows the difference obtained using L1PP with the latest corrected direct sun correction (with both the V(0,0) and back half-space bugs fixed). The bias jump is not present in the corrected solution. For reference I also include difference curves for the Original (No) Loss Model solution with no direct sun correction (magenta curve), the Commissioning reprocessing solutions obtained from L1OP with the bad (bug in back half-space and missing V(0,0) correction) direct sun correction (green curve) and from JRECON with no direct sun correction (cyan curve).

UPDATE ON DIRECT SUN CORRECTION Here we show the same set of curves but for the Dec 20, 2010 descending Pacific pass. From these two figures we see that the back half-space problem appearing in the blue curves disappears in the latest solutions shown by the red curves. The latest corrected direct sun correction, including the V(0,0) correction, has little impact on the differences (relative to no sun correction) when the sun is in the front half-space (north of 40 degS in the figure below and north of around 50 degS in the previous slide). Also note the progressive reduction of differences as we move from Reprocessing L1OP L1B to Reprocessing JRECON (with no dir sun correction) and finally to the latest solution with the new thermal loss model.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION The direct sun correction does slightly improve solutions in the AF and EAF FOV in terms of standard deviation of bias between SMOS and the model. Below we show one example of this reduction for the Dec 20 descending pass. Here we evaluate the median and standard deviation of the difference between SMOS and model brightness temperatures (actually we show only Txx for the standard deviation) for both the solutions with no sun correction (left panels) and solutions with the latest sun correction (right panels), using snapshots from 10 degN to 30 degN. The standard deviations in the lower panels do show some reduction with the sun correction in the AF and EAF FOV and this is consistent along the orbit. Improvement in bias is less evident.

UPDATE ON DIRECT SUN CORRECTION with the back half-space bugSome residual asc-desc discrepancies remain even with the new loss model Finally, we have just recently discovered that the back half space direct sun correction may influence the long term drift bias curves noticeably. To see this consider first the bias curves for the latest processing of Roger using the new loss model and direct sun correction with the back half-space bug. Some residual asc-desc discrepancies remain even with the new loss model especially in mid-July through September.

UPDATE ON DIRECT SUN CORRECTION But after switching to the latest direct sun correction the residual asc-desc discrepancy is reduced further in mid-July through September:

About the Land contamination SSS inverted from reprocessed L1A with L1B from JRECON With scale factor corrected on V(0,0)

Before After Clearly Reduced contamination but there are still sign-changed residual signatures