UPDATE ON BIAS TRENDS, DIRECT SUN CORRECTION, AND ROUGHNESS CORRECTION Joe Tenerelli May 10, 2011

UPDATE ON DIRECT SUN CORRECTION We are currently in the process of reevaluating the direct sun correct with Roger Oliva and DEIMOS. It appears that inclusion of the V(0,0) component of the correction improves the correction significantly.

AN UPDATE ON BIAS TRENDS

SUMMARY In the following slides we take a brief look at SMOS AF and EAF FOV bias trends over 2010 for six possible solution strategies: 1.Commissioning reprocessing fixed NIR calibration solutions with fixed FTR and with direct sun correction; 2.Commissioning reprocessing fixed NIR calibration solutions with no FTT and without direct sun correction; 3.Reprocessing with optimal NIR calibration and FTT with variable FTR; 4.Reprocessing with optimal NIR calibration, no FTT (similar to fixed FTR) and no direct sun correction; 5.Reprocessing with the new loss model, optimal NIR calibration, no direct sun correction and FTT with variable FTR; 6.Reprocessing with the new loss model, optimal NIR calibration, no direct sun correction and no FTT.

SUMMARY The initial results indicate that: 1.Although the (fixed NIR cal and fixed FTR) commissioning reprocessing (with or without direct sun correction) AF biases seem rather stable, the EAF biases increase markedly in April 2010 (and more for asc than desc passes). 2.Introducing the optimal NIR cal parameters stabilizes the EAF biases if no FTT is used, but the AF biases become less stable, dropping significantly. 3.If FTT is introduced with variable FTR, both AF and EAF biases increase markedly from March to May as the FTR is changed. 4.With the new loss model consistency between asc and desc biases seems to increase but a jump in both AF and EAF biases is introduced in early June. 5.For all types of solutions the bias spatial pattern evolves significantly over time, but use of variable FTR does not resolve this problem; instead, abruptly changing the FTR introduces jumps in both AF and EAF bias trends.

STRANGE RIPPLE PROBLEM April 8 ascending pass Previously we observed that for several of the 38 test orbits strange ripples appeared in the OTTs relative to a reference OTT in January. Below in the left panel is an example of this problem for an ascending pass on April 8, This turned out to be related to a problem with the configuration of the L1PP (CRSD input file). The right figure below shows a new version of the same half-orbit processed with the correct configuration. In the new version the large-amplitude ripples are gone.

COMMISSIONING REPROCESSING BIAS TRENDS As a basis for looking at the impact of calibration changes let us begin by looking at the AF and EAF bias evolutions for Tx and Ty for the commissioning reprocessing data. Ascending pass AF bias is quite stable while EAF bias jumps up by 2 K in Tx and 1.5 K in Ty in April All curves referenced to bias in early March ascending pass

COMMISSIONING REPROCESSING BIAS TRENDS For descending passes the EAF jump is equal in Tx and Ty and smaller, about 1K, but the bias continues to rise slowly over the following months, equally in Tx and Ty for EAF until early September, when Ty levels off while Tx continues to rise (probably galactic noise modeling error). All curves referenced to bias in early March ascending pass

COMMISSIONING REPROCESSING BIAS TRENDS Here is show AF and EAF reprocessing bias trends in (Tx+Ty)/2 for the 38 passes used for testing the new loss model. Again, EAF bias jumps up while AF bias remains more stable over time. These curves are based on L1B produced by L1 with constant FTR and direct sun removal on. All curves referenced to average of asc and desc bias in mid-January.

FIXED NIR CALIBRATION, NO FTT Here are similar curves but for JRECON. Even without the new loss model there appears to be more consistency between asc and desc biases than with the L1 solutions. This may be related to the direct sun correction which is not implemented in JRECON. Rise in EAF bias here All curves referenced to average of asc and desc bias in mid-January.

OPTIMAL NIR CALIBRATION, NO FTT If we now keep the old loss model and but introduce optimal NIR calibration then we obtain the following bias trends with JRECON. Here there is no FTR so these curves should be close to what we would obtain with L1 with constant FTR and no sun correction. We now have a drop of AF bias from March to June, while the EAF bias is more stable than with fixed NIR calibration during this same time period. Opposite bias drift without FTT All curves referenced to average of asc and desc bias in mid-January.

If we now switch to the L1 results with varying FTR we obtain the following bias trends. The trends differ from those obtained with JRECON mainly when the FTR in L1 changes. Here the new loss model is not used and there is some notable inconsistency between ascending and descending pass biases. OPTIMAL NIR CALIBRATION, VARYING FTR All curves referenced to average of asc and desc bias in mid-January.

NEW LOSS MODEL, VARYING FTR If we now introduce the new loss model the discrepancy between asc and desc pass biases tends to (but not for all passes) decrease. But now a large jump is introduced in early June that was not present in the optimal cal old loss model solutions. Note the FTR does not change in early June so this should not be the origin of this jump. Jump in bias in early June with new loss model, not related to FTR change… All curves referenced to average of asc and desc bias in mid-January.

NEW LOSS MODEL, NO FTT Indeed even JRECON solutions exhibit this bias jump… Jump even in JRECON with no FTT… All curves referenced to average of asc and desc bias in mid-January.

OLD LOSS MODEL, NO FTT But the old loss model solutions do not exhibit this jump… No jump with old loss model. All curves referenced to average of asc and desc bias in mid-January.

DIFFERENT TYPES OF PROCESSING Old loss model, optimal NIR cal, varying FTR Old loss model, optimal NIR cal, no FTT Old loss model, fixed NIR cal (repro) New loss model, no FTT Impact of varying FTR Impact of optimal NIR cal Impact of new loss model

DIFFERENT TYPES OF PROCESSING Old loss model, optimal NIR cal, varying FTR Old loss model, optimal NIR cal, no FTT New loss model, with varying FTR New loss model, no FTT Which is best??? Impact of varying FTR Impact of new loss model

OTT EVOLUTION WITH VARYING FTR March 20 ascending pass April 8 ascending pass June 3 ascending pass If we look at bias in (Tx+Ty)/2 over the field of view relative to the bias in January we get a feel for the evolution of the spatial pattern of the bias. Here we see this evolution for the L1 solutions with optimal NIR cal, the new loss model and FTT with variable FTR. Clearly the bias pattern is evolving significantly over time and part of this is coming from the variable FTR (see the next slide). Note also the strange bias patterns in the April 8 plot. These seem to come and go and do not appear in the March 20 and June 3 plots.

OTT EVOLUTION WITH NO FTT March 20 ascending pass April 8 ascending pass June 3 ascending pass Here we have the same plots but for the no FTT solutions. The strange patterns on April 8 remain, but now the bias pattern evolution is very different, and the main difference between these solutions and those on the previous slide is the use of the FTT with variable FTR.

BIAS TRENDS WITH LATITUDE: IMPACT OF LOSS MODEL Here we compare the variation with latitude of the AF-FOV bias between SMOS and the forward model for pairs of nearby ascending and descending half-orbits. All biases are relative to the latitudinal average (between 50 degS and the equator) of the average of the AF biases for one ascending and one descending pass on March 20, Ascending pass biases are blue while descending pass biases are red.

BIAS TRENDS WITH LATITUDE: IMPACT OF LOSS MODEL In June both old and new loss models produce little asc-desc discrepancy…

BIAS TRENDS WITH LATITUDE: IMPACT OF LOSS MODEL Here we compare the variation with latitude of the AF-FOV bias between SMOS and the forward model for pairs of nearby ascending and descending half-orbits. All biases are relative to the latitudinal average (between 50 degS and the equator) of the average of the AF biases for one ascending and one descending pass on March 20, Ascending pass biases are blue while descending pass biases are red. Here we show bias for two passes on Nov In this case the old loss model (left) shows slightly higher discrepancy between asc and desc passes, especially far from the equator, than the new loss model (right). You can also see the substantial drift in the overall biases since March 20 (the reference), with and without FTT (the curves have drifted away from 0 over time).

THE ROUGHNESS PROBLEM SSS retrievals based upon the first Stokes parameter with the surface wind forced to that of ECMWF yields noticeable streaks, especially at high latitudes.

THE ROUGHNESS PROBLEM SSS retrievals based upon the first Stokes parameter with the surface wind forced to that of ECMWF yields noticeable streaks, especially at high latitudes.

THE ROUGHNESS PROBLEM It is interesting to see if we can find combinations of the linear polarizations Tv and Th that are not sensitive to surface wind speed changes but remain sensitive to SSS changes. For example, at 56 deg incidence angle the empirically derived dependence of emission Tv on surface wind speed is nearly half that of emission Th. This suggests that the linear combination Tv – 0.5 Th might be insensitive to wind speed changes, at least approximately.

THE ROUGHNESS PROBLEM At large incidence angles Tv and Th also have significantly different dependencies on SSS. For example, at 56 deg incidence angle

THE ROUGHNESS PROBLEM

The phase calibration method involves assuming non-random phase shift error added to random phase shift between ports: This leads to a rotation relationship between desired and actual Stokes 3 and 4: and so or POLARIZATION MIXING IN THE COSMOS CAMPAIGN

Here is the resulting phase shift error as function of aircraft roll for wags: POLARIZATION MIXING IN THE COSMOS CAMPAIGN

Impact on Third Stokes parameter: POLARIZATION MIXING IN THE COSMOS CAMPAIGN

THE ROUGHNESS PROBLEM

The following is a map of SSS retrieved using the linear combination Tv-0.6 Th, where Tv and Th are dwell-line integrated surface basis linear brightness temperatures. All descending passes for August 2010 were used. An overall bias (whose origin requires study) of nearly 3 psu has been removed to produce this map. Also note the large land contamination which is probably related to land contamination in the third Stokes parameter. Finally, low SSS bias remains at high latitudes, which is probably related to the particular choice of linear combination of Th and Tv., which should be valid at only low to moderate wind speeds (this also requires more work).