Solución de Dimitriadis

One of the steps in the algorithm used to derive SSS from the brightness temperatures acquired by SMOS that has a considerable impact on the quality of the retrieved salinities is the application of quality filters. Quality filters are used to discard those retrievals that do not meet certain prefixed standard of quality because: the brightness temperatures are contaminated by a specific or inferred perturbation; the existence of a problem during the retrieval algorithm; or the detection of any kind of inconsistency with the geophysical model. In this section we study the question of the impact of the filtering strategy on the quality of the resulting SMOS SSS maps.

In this section a filtering strategy alternative to that of the standard Level 2 processor is going to be applied and compared with the original set of filters producing the official SMOS-BEC SSS products. The filtering procedure tested here is analogous to the one used to produce SMOS-BEC SSS products as described in section3.2.1, but using a more restrictive swath of 300 km to get rid of lower quality retrievals far from the center of the track of the satellite swath. In addition, when L2 SSS measurements are close to coastal areas (distance closer than 500 km) we use a reduced swath of 100 km to diminish the impact of land-sea contamination. In addition, retrievals too far from the climatological values are discarded. This alternative filtering aims at improving the accuracy and reducing the bias present in SMOS L2 measurements, although at the cost of reducing the amount of L2 SSS values used to generate our L3 and OI maps. There is thus a trade-off between having a statistical set large enough and having values of enough quality to provide an accurate estimate of SSS. To filter outliers, all L2 SSS differing more than 2 with respect to WOA09 SSS are discarded.

The results of applying the weighted average algorithm to this filtered L2 SSS are shown in Figure 3.20 for 9-day and for monthly L3 maps. Compared with the results presented in section3.2.1, the new 9-day and monthly L3 maps loose global coverage specially close to land, although the global SSS structure is correctly represented, and the results are consistent with the previous filtering procedure. The number of grid points per cell used to create the weighted averaged maps of 9 days and one month has also been reduced, as shown in Figure3.21. In both cases the reduction of number of L2 measurements is most noticeable close to the coast.

As commented above, we face a trade-off between the coverage of SSS maps and the quality of the L2 SSS used to generate the maps. If a more restrictive filtering is applied to L2 SSS, the resulting coverage would be dramatically decreased, which would not be justified if the error on the resulting SSS maps does not greatly decrease accompanying. With the tested filtering procedure here, 8 L2 measurements are used on average to produce each grid point of the L3 SSS 9-day maps, so a relative error of 27% of L2 error is expected; an average of 22 L2 measurements is used to produce each grid point of the L3 SSS monthly maps, so a relative error of 16% is expected.

To study the error reduction associated with this new filtering, the standard deviation of L2 measurements inside each cell of 0.25^◦×0.25^◦, is presented in Figure3.22for 9-day and monthly

maps. In this case, as compared with results presented in section 3.2.1, the spread between measurements inside each cell close to the coast areas and in high latitudes is being reduced.

To quantitatively validate the proposed filtering procedure, we compare a year of data against Argo floats measurements, as presented in section3.4. Tables3.7and 3.8synthesize the results of differences between 9-day L3 and OI SSS vs collocated measurements from Argo buoys by latitudinal bands (global, 60S-60N, 30S-30N, and Zone 122), distance to coast, depth of the Argo uppermost measurement and SST differences between Argo and ECMWF to quantitatively assess SSS error maps.

Figure 3.20: Map of surface salinity obtained with the alternative strategy applying a weighted average on SMOS data for first nine days (top) and month of June 2012 (bottom).

In the case of 9-day products using the alternative filtering scheme on L2, that includes a bias correction with respect to climatology, the number of total matchups is reduced to seventy

Figure 3.21: Number of L2 measurements used for 9-day (top) and monthly products (bottom).

Latitude Global 60S-60N 30S-30N Zone 122

Maximum depth >10 m >10 m >10 m

Coast distance 1000 km 1000 km 1000 km

ECMWF-Argo SST <0.3 ºC <0.3 ºC <0.3 ºC

n 176033 174008 169094 116147 76711 92565 90742 64966 49705 6473 6355 5515 4575

∆S -0.01 -0.01 -0.01 0.03 0.02 -0.05 -0.05 -0.02 -0.03 0.00 0.00 0.02 0.01 L3

σ_<∆S> 0.71 0.70 0.70 0.64 0.54 0.49 0.49 0.44 0.43 0.38 0.39 0.37 0.36

Table 3.7: Statistics of the comparison of 9-day L3 (alternative filtering of L2) vs Argo SSS measurements for the year 2012.

thousand (two-hundred thousand for original 9-day SSS maps). As shown in the tables, at global scale the standard deviation of SMOS minus Argo differences is reduced when using the alternative filtering. The standard deviation is 0.71 for L3 and 0.48 for the OI (originally they

Figure 3.22: Standard deviation of L2 measurements inside each cel l of 0.25^◦×0.25^◦for 9-day (top) and monthly products (bottom).

Latitude Global 60S-60N 30S-30N Zone 122

Maximum depth >10 m >10 m >10 m

Coast distance 1000 km 1000 km 1000 km

ECMWF-Argo SST <0.3 ºC <0.3 ºC <0.3 ºC

n 235446 231658 223773 133312 88382 125347 122672 76151 58360 7802 7683 6370 5196

∆S -0.04 -0.04 -0.04 0.01 -0.00 -0.07 -0.07 -0.04 -0.04 -0.01 -0.01 0.01 0.01

OI

σ_<∆S> 0.48 0.47 0.47 0.44 0.34 0.36 0.36 0.30 0.29 0.25 0.25 0.24 0.23

Table 3.8: Statistics of the comparison of 9-day OI (alternative filtering of L2) vs Argo SSS measurements for the year 2012.

were 0.75 and 0.48), with biases of -0.01 for L3 and -0.04 for OI (originally they where -0.10 and -0.10).

As with the original validation, the comparison with matchups restricted by latitude, located farther than 1000 km from major coasts and restricted to verify the Argo consistency criteria on SST as above, leads to reduced standard deviations and bias. Using these matchups, the 9-day L3 SSS produces a salinity estimate with bias of 0.02, -0.03 and 0.01 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively, and standard deviations of 0.54, 0.43 and 0.36 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively. In the case of 9-day OI SSS, the salinity estimate has also a slight negative bias of 0.00, -0.04 and -0.01 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively, and standard deviations of 0.34, 0.29 and 0.23 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively. These results represent an improvement with respect to original filtering criteria, based on the decrease of bias error, although similar standard deviation are found.

Tables3.9and3.10synthesize the results of the differences with collocated buoys for monthly L3 and OI SSS, classified by latitudinal bands (global, 60S-60N, 30S-30N, and Zone 122), distance to coast, depth of the Argo uppermost measurement and SST differences between Argo and ECMWF to quantitatively assess the SSS error maps.

Latitude Global 60S-60N 30S-30N Zone 122

Maximum depth >10 m >10 m >10 m

Coast distance 1000 km 1000 km 1000 km

ECMWF-Argo SST <0.3 ºC <0.3 ºC <0.3 ºC

n 67573 66577 64470 40368 26628 35357 34615 22773 17397 2354 2316 1945 1588

∆S -0.06 -0.06 -0.05 0.00 -0.01 -0.08 -0.08 -0.05 -0.05 -0.03 -0.03 -0.01 -0.01 L3

σ_<∆S> 0.60 0.59 0.58 0.48 0.39 0.41 0.41 0.32 0.32 0.26 0.27 0.25 0.24

Table 3.9: Statistics of the comparison of monthly L3 (alternative filtering of L2) vs Argo SSS measurements for the year 2012.

Latitude Global 60S-60N 30S-30N Zone 122

Maximum depth >10 m >10 m >10 m

Coast distance 1000 km 1000 km 1000 km

ECMWF-Argo SST <0.3 ºC <0.3 ºC <0.3 ºC

n 75602 74121 71468 41684 27546 40124 39236 23845 18201 2423 2385 1973 1609

∆S -0.08 -0.07 -0.06 -0.01 -0.02 -0.08 -0.08 -0.05 -0.05 -0.03 -0.03 -0.00 -0.00 OI

σ_<∆S> 0.43 0.42 0.41 0.37 0.26 0.32 0.31 0.24 0.23 0.18 0.18 0.17 0.16

Table 3.10: Statistics of the comparison of monthly OI (alternative filtering of L2) vs Argo SSS measurements for the year 2012.

In the case of monthly products using alternative filtering of L2, the number of total matchups reduce to the order of seventy thousand (seventy-five thousand for original monthly SSS maps).

At global scale the standard deviation of the SMOS minus Argo differences is reduced when using the alternative filtering. The standard deviation are 0.60 for L3 and 0.43 for the OI (originally they were 0.66 and 0.45), and the biases are -0.06 for L3 and -0.08 for OI (originally they were -0.14 and -0.11).

When the matchups are restricted by latitude, located farther than 1000 km from major coasts and restricted to verify the consistency Argo SST criteria as above, reduced standard deviations and bias are obtained. With such a selection of matchups, monthly L3 SSS products yields a slightly negative biases of -0.01, -0.05 and -0.01 at the 60^◦, 30^◦ latitudinal bands and

Zone 122 respectively and standard deviations of 0.39, 0.32 and 0.24 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively. For the case of monthly OI SSS, the salinity estimate has also slight negative biases of -0.02, -0.05 and -0.00 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively, and standard deviations of 0.26, 0.23 and 0.15 at the 60^◦, 30^◦ latitudinal bands and Zone 122 respectively. Also for monthly products, the alternative filtering results in an improvement of the mean bias compared to the results from filtering criteria originally used in L3 and OI SSS maps.

When comparing with monthly SSS maps not coming from remote sensing (Table3.11), as done in previous section, the results are also improved (compare to Table3.5). For the case of OI (not shown), this comparison is not much affected by the filtering procedure.

Latitude Global 60S-60N 30S-30N Zone 122

Climatology WOA09 WOA13 ISAS WOA09 WOA13 ISAS WOA09 WOA13 ISAS WOA09 WOA13 ISAS

∆S -0.01 -0.02 -0.01 0.00 -0.01 -0.01 -0.06 -0.07 -0.08 -0.00 -0.01 -0.01

L3

σ_<∆S> 0.85 0.83 0.85 0.65 0.65 0.64 0.50 0.48 0.47 0.31 0.30 0.27

Table 3.11: Statistics of the comparison between monthly L3 (alternative filtering of L2) vs climatological SSS for the year 2012.