extending the land sea contamination characterization to the extended alias-free field of view

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 ESRIN6-8 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 30-40 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 2010-2013

AN EXAMPLE OF LSC AROUND SOUTH AMERICA

BASED UPON ARGO MEASUREMENTS FROM 2010-2013

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 SALINITYThe 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-DEPENDENTThe biases are also scene-dependent and therefore change as the distribution of land over the front half space changes:

THE LSC CORRECTION

Given the preceding, we suppose that the ‘land contamination’ bias in polarization p may be expressed as

where is the pass direction, are the geographic longitude and latitude and are the usual director cosine coordinates.

As a first test we developed a coarse lookup table over just the alias-free field of view (AF-FOV). Here we extend the results to the extended alias-free field of view (EAF-FOV).

∆Tp(D,λg,θg,ξ,η),

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 orTy > 500 K orUxy > 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 AVERAGINGnon-RFI-flagged snapshots

EXAMPLE MAP OF THE FIRST STOKES BIASnon-RFI-flagged snapshots

MEASUREMENT COUNTS FOR THE AVERAGINGRFI-flagged snapshots

EXAMPLE MAP OF THE FIRST STOKES BIASRFI-flagged snapshots

EVALUATION OF THE CORRECTION

1. Global perspective for May 2011

2. 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 2010-2013

4. 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


FIRST STOKES PARAMETER DIVIDED BY TWO AFTER CORRECTION


DESCENDING-ASCENDING BIAS BEFORE CORRECTION


DESCENDING-ASCENDING BIAS AFTER CORRECTION

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.


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


BIAS APPEARS AS SOON AS LAND APPEARS OUTSIDE THE FUNDAMENTAL HEXAGON


BIAS APPROACHES 2.5 PSU AS THE LAND FRACTION APPROACHES 0.9.


CORRECTION SEEMS TO REDUCE THE ABSOLUTE ERROR TO ABOUT 150-200 KM FROM THE COAST. CORRECTION IS LESS EFFECTIVE WITHIN 150 KM OF THE COAST.

ARGO COLLOCATIONS: AUSTRALIA


TIME PERIOD: 2010-2013

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


UNCORRECTED SMOS SSS


CORRECTED SMOS SSS


bias reduction of about 0.5 pss


Impact of the LSC correction drops rapidly within about

200 km of the coast


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

DYNAMIC ZONE: PANAMAComparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA

bias reduction of nearly 2 pss

DYNAMIC ZONE: PANAMAComparing Matisse transect with SMOS SSS

DYNAMIC ZONE: PANAMA


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

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

Corrected SMOS SSS differs from the reference used to derive the LSC correction.

AMAZON PLUME: ANACONDA TSG

AMAZON PLUME: ANACONDA TSGLSC 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 2010-2013 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-2009. 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.

AMAZON PLUME

extending the land sea contamination characterization to the extended alias-free field of view

Documents

fraction of land

example of lsc

stokes parameter biasthe

fhs land fraction

ice fraction approaches

bias landsea contamination

distribution of land

lsc correctionkey points