AN INITIAL LOOK AT THE IMPACT OF THE NEW ANTENNA LOSS MODEL Joe Tenerelli SMOS QUALITY WORKING GROUP #4 7-9 March 2011

OVERVIEW The Level 1 Team has developed a new antenna loss model that should help to correct for the impact of variations of the antenna physical temperatures on the reconstructed brightness temperatures. In the following slides we will: 1.Review the bias problems in the SMOS brightness temperatures over the ocean; 2.Show that the direct sun correction algorithm may induce significant latitudinal variations in the bias between model and data over the open ocean; 3.Show that the direct sun correction may not behave as expected when the sun is in the back half-space of the array (Jerome’s presentation). 4.Look at the impact of the new loss model on a small set of Pacific Ocean orbits reprocessed by Roger Oliva (drift with latitude and temporal evolution). Caveat: Given potential impact of the direct sun correction on the bias variations, we must use caution in interpreting the results.

Problem 1 Problem 1: The differences between SMOS reconstructed brightness temperatures produced by DPGS and those predicted by our ‘best’ forward scene model drift with time. Certainly, a portion of this drift may be related to problems in the model (sun glint, galactic noise reflection). Another portion may originate with the instrument and Level 1 processing. 10-DAY MEAN SSS BIAS: DESCENDING PASSES

Problem 1 Problem 1: The differences between SMOS reconstructed brightness temperatures produced by DPGS and those predicted by our ‘best’ forward scene model drift with time. Certainly, a portion of this drift may be related to problems in the model (sun glint, galactic noise reflection). Another portion may originate with the instrument and Level 1 processing. 10-DAY MEAN SSS BIAS: DESCENDING PASSES +0.5 K (Tx+Ty)/2 -1 psu

Sensitivity of the first Stokes parameter to a 1 psu increase in SSS: Best case (warm water): +.5 K (Tx+Ty)/2 -1 psu Roughly 1 K/psu in (Tx+Ty) in warm water Roughly 0.5 K/psu in (Tx+Ty) in cold water

10-DAY MEAN SSS BIAS: ASCENDING MINUS DESCENDING PASSES MID-MAYMID-AUGUST END AUGUST MID-NOVEMBER Differences in SSS retrieved from descending and ascending passes can reach more than 1 psu and change (even in sign) with time.

HOVMOLLER PLOTS

BIAS TRENDS OVERVIEW Several months ago it was noticed that, in terms of descending-ascending pass differences, there is a qualitative correlation between Tp7 and SMOS- model bias variations as a function of latitude and time: AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

BIAS TRENDS We have also seen discontinuities in the bias trends that seem to correlate with the sun passing between the front and back half-spaces. More on this later… NOVAUGMARJUNSEPOCTAPRJULMAYDEC

BIAS TRENDS The discontinuities in the bias as well as the correlation between Tp7 and the bias trends remain in the reprocessed data: AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

BIAS TRENDS However, the correspondence between Tp7 and the bias is less clear when we apply the simplified reconstruction, with no direct sun correction, to the reprocessed Level 1A files: AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT TP7 VARIATIONS AND SUN GEOMETRY

AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

TP7 VARIATIONS AND SUN GEOMETRY AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

LATITUDINAL BIAS DRIFT SSS bias decrease with latitude for descending passes is associated with (Tx+Ty)/2 increase by about 1 K (red curve). We have also seen bias drifts with latitude, especially in November 2010

BIAS TRENDS IN TERMS OF SSS BIAS For the November descending passes, the DPGS SSS bias drift with latitude is consistent that the drift in AF-FOV bias in (Tx+Ty)/2: DPGS DESC-ASC JRECON DESC-ASC DPGS (Tx+Ty)/2 increase With latitude

BIAS TRENDS DPGS – JRECON DESC-ASC FOR NOVEMBER 2010: POSSIBLE IMPACT OF DIRECT SUN CORRECTION

IMPACT OF NEW LOSS MODEL Roger Oliva has reprocessed several orbits using the new antenna loss model implemented by DEIMOS. Results suggest that latitudinal drift in (Tx+Ty)/2 is reduced using the new loss model with the L1PP image reconstructions. Nevertheless, the drift is reduced further when we switch to the simplified reconstruction with no direct sun correction: Original loss modelNew loss modelExtension to LICEFs Fixed-Cal Reprocessing solutions (includes NIR AB) November 9 Pacific Descending (red) vs Ascending (blue) Passes

IMPACT OF NEW LOSS MODEL Original loss modelNew loss modelExtension to LICEFs Fixed-Cal Reprocessing solutions (includes NIR AB) November 10 Pacific Descending (red) vs Ascending (blue) Passes A similar result on November 10:

IMPACT OF NEW LOSS MODEL Original loss modelNew loss modelExtension to LICEFs Fixed-Cal Reprocessing solutions (includes NIR AB) June 13 and 14 Pacific Descending (red) vs Ascending (blue) Passes In June, when the direct sun correction has less impact than in November, drift with latitude is not a large problem in any solution. Actually, it seems the direct sun correction may introduce an offset that is largely independent of latitude (Needs to be confirmed using L1PP). In any case, the new loss model does improve agreement between ascending and descending passes.

BIAS TRENDS OVER TIME L1PP Level 1BJRECON With the old loss model, differences between descending and ascending pass SMOS-model biases are significant and drift over time, as we have observed for a while:

BIAS TRENDS OVER TIME L1PP Level 1BJRECON With the fixed calibration used to obtain the reprocessing solutions, ascending/descending differences remain significant but the drift over time of these differences seems to be reduced:

BIAS TRENDS OVER TIME L1PP Level 1BJRECON With the new antenna loss model (NAM, here applied only to the NIRs), differences between descending and ascending pass solutions are much reduced, but the overall drift over time reaches about 0.8 K:

BIAS TRENDS OVER TIME L1PP Level 1BJRECON Extension of the loss model to the LICEFS makes only a small impact in the bias trends over time:

CONCLUSIONS 1.Relative to our best available ocean scene model, significant biases exist in all SMOS-derived brightness temperatures over the alias-free field of view. The biases seem to have an impact on retrieved SSS that is roughly independent of longitude (see the SSS bias maps). 2.These biases drift with latitude (short time scale) and slowly over time (weekly to monthly time scales). 3.The latitudinal bias drift appears to involve both bias in the calibrated visibilities and bias introduced by the direct sun correction algorithm (see also Jerome Gourrion’s presentation). 4.The new antenna loss model reduces discrepancies between descending and ascending pass biases, but also increases the bias trend with time.

EXTRA SLIDES

LAND CONTAMINATION AND BIAS Maps of bias between SSS derived using DPGS L1B data and SSS derived using L1A and the JRECON breadboard are shown below. The strong land contamination halos in the DPGS solutions are not as evident in the JRECON solutions. For the descending passes, the linear bias gradient with latitude in the DPGS solutions (lower left) is not seen in the JRECON solutions (lower right). For all maps the sets of fixed OTTs shown earlier were used for both DPGS and JRECON solutions. DPGS ASC DPGS DESC JRECON ASC JRECON DESC

THIRD AND FOURTH STOKES

AF-FOV MEAN TEC

AF-FOV MEAN CELESTIAL SKY NOISE

April 2 Sep 20 June 25 Combining three descending passes over the last six months…

The difference between the two preceding maps gives an indication of how the model is in error. Here we have taken just the SMOS sky map produced from data corresponding to ECMWF 10-m wind between 3 and 6 m/s and we subtracted the 3 m/s model solution. The difference map shows how the model underestimates the data in the vicinity of the galactic equator by over 1 K. Next we will compare the data and model in more detail along the cross section shown by the magenta line segment.SMOS-MODEL

These curves are averages over celestial dwell lines and thus do not correspond to a specific viewing geometry. This is a cross section through the dwell lines near the galactic equator along the magenta line segment in the previous slide. The solid black curve is the ideal flat surface solution, the dashed black curve is the 3 m/s solution, and the colored curves are the actual SMOS data in various wind speed ranges with all geophysical sources except galactic noise removed. These curves are averages over celestial dwell lines and thus do not correspond to a specific viewing geometry. actual SMOS residual is NOT A SIMPLE LINEAR COMBINATION of the flat and 3 m/s model solutions. It is obvious from this plot that the actual SMOS data have peaks that are between the flat surface solution and the 3 m/s solution. However, away from the galactic plane the flat and rough surface solutions are very similar so that the actual SMOS residual is NOT A SIMPLE LINEAR COMBINATION of the flat and 3 m/s model solutions. Colored SMOS residual sky noise curves do not lie between the flat and rough surface model solutions here. Colored SMOS residual sky noise lies between the flat and rough surface model solutions (black curves) here.

TP7 VARIATIONS AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

TP7 VARIATIONS: SUN GEOMETRY AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

TP7 VARIATIONS: SUN GEOMETRY AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

TP7 VARIATIONS: SUN GEOMETRY AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT

TP7 VARIATIONS: SUN GEOMETRY AUGMARJUNNOVSEPOCTAPRJULMAYDECMARJUNNOVSEPAPRJULMAYDECAUGOCT