At this northern midlatitude site, OMI overpasses occur from 0.5 h before to 2.5 h after solar noon. The GOME-2 overpasses take place in the morning from 3 to 0.5 h before solar noon. The VDA site, located in a topographically flat region, is characterized by rather high total ozone columns (on average in the 250–450 DU range) and by the presence of absorbing aerosols of pollutant origin. The surface albedo at 360 nm, provided in the OMUVB database, exhibits a weak seasonality in the 0.03–0.07 range.

For both satellite instruments, the distance between the ground station and the CTP/grid cell centre point is first chosen smaller than or equal to 100 km.

Comparison results for AS conditions are shown in Fig. 1 for both satellite instruments: the upper panels present OMI v1.3 and the lower panels GOME-2. Histograms of the percent relative differences between SB and GB UVI data are located to the left and correlation diagrams are located to the right. Crosses circled in blue (for OMI) or turquoise (GOME-2) correspond to a COD of less than or equal to 1. Notice that the GOME-2 data set is smaller than the OMI data set because there is only one value per day and no value when SZA at noon is larger than 70∘. The data show a medium dispersion around relative difference means (SD nearly 40 %, means nearly 21 %), the correlation between SB and GB UVI is strong (correlation coefficients r∼0.95) and the regression lines have a slope larger than unity (1.08±0.01 for OMI, 1.12±0.01 for GOME-2) with a small intercept. Satellite-derived UVI is larger than GB UVI (positive relative difference) in 78 % of cases for OMI and in 73 % for GOME-2. When the COD is smaller than or equal to 1 (circled crosses) the UVI relative difference is almost always positive for OMI (Fig. 1b), but is less so for GOME-2 (Fig. 1d). Satellite-derived UVI smaller than GB UVI (negative relative difference) can occur when the COD is large, as seen in Fig. 3a and c where the UVI relative difference is plotted vs. the COD retrieved from satellite instruments. These negative UVI relative differences for large COD values are observed for both low and high UVI (UVI ≥ 3: blue circles for OMI, green circles for GOME-2), especially for OMI. Negative UVI differences for small COD values (COD ≤ 1) are sometimes observed for GOME-2, which can be related to the SZA at the time of satellite overpass. Indeed, as seen in Fig. 4a and c, a filter set up on SZA at overpass shows that SZA > 60∘ corresponds to UVI < 3 and to many negative relative differences for both OMI and GOME-2 (blue and green circles). Approximate values of the median relative biases are 12.5 % for OMI and 12.1 % for GOME-2. The 90th percentiles (p90) indicate that 10 % of the cases correspond to relative differences larger than about 71 and 64 % for OMI and GOME-2, respectively (Fig. 1 caption). This 10 % of cases for which the SB UVI overestimate the UVI at the surface are identified as red and violet crosses in Fig. 3a and c, i.e. correspond often to UVI lower than 3 and to a large COD. The 10th percentiles (p10) indicate that 10 % of the cases correspond to OMI and GOME-2 underestimations of more than about 15 % (Fig. 1 caption), associated also with UVI < 3 and with a large COD (Fig. 3a and c).

Figure 2 shows the results obtained for CS conditions. The dispersion around relative difference means is weak (SD < 10 %, means < 8 %), the correlations between SB and GB UVI are very strong (r∼1), and the slopes of the regression lines are slightly larger than for AS conditions (1.10±0.01 for OMI, 1.14±0.02 for GOME-2) with small intercepts. Satellite-derived UVI is still generally larger than GB UVI (∼ 92 % of cases for OMI, 80 % for GOME-2) and this corresponds almost always to COD ≤ 1 (circled crosses), as seen also in Fig. 3b and d. These low COD values indicate that the satellite algorithms provide a good estimate of the actual cloudiness. As with GOME-2 for AS conditions, for UVI values smaller than about 3, GOME-2 values are generally smaller than GB values (Fig. 3d), and these cases correspond to SZA > 60∘ (Fig. 4d). Only two such cases are observed for OMI (Figs. 3b and 4b). Both satellite sensors demonstrate a positive median relative bias (SB UVI > GB UVI) of about 8.5 %. The number of CS cases is not very large for GOME-2 comparison (37), but the results are statistically robust. A total of 10 % of the cases correspond to an SB overestimation of more than 16 and 13 % for OMI and GOME-2, respectively (p90, Fig. 2 caption). p10 values indicate that 10 % of the cases correspond to an OMI underestimation (p10=0 %, i.e. 10 % of the cases show a negative relative difference) and to more than 10 % of GOME-2 underestimations. For GOME-2, most of these cases correspond to UVI < 3 (violet crosses in Fig. 3d).

The statistics of the results are reported in Table 1 for AS conditions and in Table 2 for CS conditions. The median bias is positive and small for both instruments: 0.21 for OMI and 0.33 for GOME-2 for AS conditions, 0.32 for OMI and 0.39 for GOME-2 for CS conditions.

A seasonal effect on differences is observed for both instruments with smaller values in winter which correspond to small UVI. UVI relative differences for OMI show no seasonal effect (the large UVI differences being divided by high UVI). On the other hand, GOME-2 UVI relative differences exhibit seasonal variations, which is due to negative values related to a small UVI and large SZA occurring mostly in winter rather than in other seasons (not shown). Surface albedo seasonality seems too weak to explain this behaviour.

These performances of the two satellite instruments should not be compared because the temporal coverage is not the same. Another study conducting a comparison of the performances is carried out further.

The overpass of both satellite instruments occurs sometimes quite far from noon. Surprisingly, no correlation between the UVI relative difference and the time difference between overpass and noon is observed, neither for AS nor for CS conditions (not shown).

For CS conditions, radiative transfer (RT) computations are carried out to understand the positive biases observed. We use DISORT (DIScrete Ordinates Radiative Transfer) code for SZA < 70∘ (Stamnes et al., 1988; Van Weele et al., 2000). We use SZA, the extraterrestrial solar spectrum from Dobber et al. (2008), and Paur and Bass (1985) ozone absorption cross sections. For the other input parameters, we take the total ozone column (TOC) from OMI (OMTO3), aerosol data from the sun photometers of the AERONET/PHOTONS network (Holben et al., 1998) (we use daily or monthly means of the aerosol optical depth (AOD) at 440 nm, the Ångström exponent (between 440 and 870 nm) to derive the spectral AOD, the single scattering albedo (SSA) at 440 nm) and the surface albedo from Feister and Grewe (1995). The other input parameters used in the RT model are the midlatitude ozone, temperature and pressure profiles. Note that the sun photometers are operating next to the spectroradiometers.

We have compared the simulated UVI to both OMI and GB UVI for several cloud-free cases. The histograms of the percent relative difference between the computed UVI and the measured one are reported in Fig. 5a for GB UVI and Fig. 5b for OMI. GB UVI measurements are 1.7 % lower and OMI UVI are 4.7 % higher than the simulated UVI, each value being within GB and OMI measurement uncertainty, respectively. Since the TOC is the same for both modelling and OMI, this overestimation of OMI UVI might be mainly related to aerosol parameters and surface albedo, though this parameter value is small. Of course part of the bias might come from differences between the two RT models used and also between the other input parameters. Kazadzis et al. (2009b) concluded also with an overestimation due to aerosol variability (in time and space). Of course, we have to keep in mind that modelling computations are affected by uncertainties.

For this previous modelling, we have chosen OMTO3 but other TOC data could be used, such as the TOC derived from the GB spectra following the method described in Houët and Brogniez (2004), relying on a differential absorption technique (Stamnes et al., 1988). The accuracy of this product is about 3 %. We find that this TOC is often larger than OMTO3, which is in agreement with Antón and Loyola (2011) findings for cloud-free conditions (OMTO3 smaller than GB TOC by 2–3 % on average). Figure 5c shows the UVI relative difference between the computed and the GB UVI vs. the TOC relative difference. The computed UVI is often larger than the GB UVI measurements for a negative TOC relative difference, which could explain the positive 1.7 % bias. Note that the denominator of the relative differences (UVI or TOC) is the mean, contrarily to the SB–GB comparisons because, in this study, neither piece of data is considered as a reference.

Another TOC product from OMI (OMDOAO3) exists, which is sometimes quite different from OMTO3 (either larger or smaller) leading to a different modelled UVI and thus to a quite different relative difference. For example, a 7.6 % relative difference between GB UVI (4.8) and modelled UVI using OMTO3 (290 DU) becomes 4.8 % while using OMDOAO3 (297 DU).

TOC from GOME-2 is also sometimes different from OMTO3 and often smaller than spectroradiometer TOC.

Underestimation of OMTO3 and of GOME-2 TOC for cloud-free and cloudy cases, as is found also by Antón and Loyola (2011), can explain part of the observed biases between SB and GB UVI. Aerosol climatology from Kinne (2007) and Kinne et al. (2013) might also contribute to the biases. Indeed, these aerosol climatologies rely on AERONET data that show an interannual variability, and the gridding is 1∘ × 1∘ in latitude–longitude. Cloud cover variability within the satellite pixel (Kazadzis et al., 2009b) is expected to contribute to the biases as well as the surface albedo climatology from Tanskanen (2004).

The impact of the distance between the ground station and the CTP/grid cell centre point appears to be negligible. Tables 3 and 4 report results for distances smaller than or equal to 10 km. For both OMI and GOME-2, the number of UVI pairs (SB–GB) is much smaller than when 100 km distance is considered. For AS conditions, the correlation between SB UVI and GB UVI data is hardly stronger for both satellite instruments (correlation coefficient increased by 0.01). Regression line slopes are closer to 1 than for the 100 km case (1.06±0.02 for OMI, 1.10±0.03 for GOME-2). However, the relatively large uncertainties limit the significance of these differences. The values of the statistics parameters indicate an agreement close to that obtained for 100 km (median UVI relative bias for OMI of 10.3 % instead of 12.5 %; for GOME-2, 14.3 % instead of 12.1 %). Based on p90 and p10 values, the filter on the distance has mainly removed cases of large SB UVI overestimation, not those of SB UVI underestimation (p90 < 53 %, much lower than for 100 km distance, and p10 close to the 100 km distances).

For CS conditions, correlation between OMI UVI and GB UVI is the same as for 100 km; the slope is almost unchanged 1.09±0.01; the statistics parameters indicate better agreement (median relative bias 6.6 % instead of 8.4 %), though these results are statistically less robust than for 100 km because there are only 14 UVI pairs. As for AS conditions, few cases with large SB overestimation have been removed (p90 about 10 %, p10 about -3 %). No study can be conducted for GOME-2 (only 2 pairs). Thus, a satellite validation performed with shorter distances between the satellite CTP/grid cell centre point and the GB instrument does not change the results significantly.

Finally, for AS conditions about 56 % of OMI and GOME-2 UVI data agree with GB data in the interval [-20, 20 %] (30 % agree in the interval [-10, 10 %]). For CS conditions, 100 % of OMI UVI data and 95 % of GOME-2 UVI data agree with GB data in the interval [-20, 20 %] (60 % agree in the interval [-10, 10 %]).

As mentioned above, an additional study compares the performances of the two instruments on common dates. Tables 5 and 6 report the results. For AS conditions, the correlation between SB and GB UVI is strong (r∼0.95) for both satellite instruments; the slopes of the regression lines are significantly closer to 1 for OMI 1.05±0.01 than for GOME-2 1.12±0.02, with a negative intercept for GOME-2. For CS conditions, SB and GB UVI measurements are very strongly correlated (r∼0.99) for both satellite instruments, the slopes of the regression lines are larger than for AS conditions with a smaller difference between OMI 1.10±0.01 and GOME-2 1.14±0.02. The median biases and median relative biases are very close for both instruments for both AS and CS conditions. OMI and GOME-2 overestimations are the same for AS conditions (p90 about 65 %), but the underestimation is smaller for OMI (p10 about -11 % for OMI, -17 % for GOME-2). For CS conditions, OMI essentially overestimates UVI (both p90 and p10 positive, only 7 % of the cases show a negative relative difference). GOME-2 is unchanged compared to the all-cases study (with underestimation corresponding to a UVI < 3).

The seasonal variability of differences between SB and GB UVI is greater for GOME-2 with frequently larger values than for OMI outside the winter period. UVI relative differences show no seasonal variability for OMI, but they do for GOME-2 because, as mentioned in the previous study, (i) the UVI differences for GOME-2 are larger than for OMI outside the winter season, leading to larger relative differences for GOME-2 than for OMI, and (ii) there are more negative relative differences for GOME-2 than for OMI, mainly in winter.

Tables 1 and 2 also report the results of the comparison of OMI v1.2 data with GB data. The median UVI relative bias is about 21 % for AS conditions, overestimation is strong and underestimation is weak (p90 about 85 %, p10 about -8 %). For CS conditions, the median UVI relative bias is about 18 %, overestimation is large and there is almost no underestimation (p90 about 23 %, p10 about 10 %; only 3 % of the cases show a negative relative difference).

Median UVI biases are about 0.4 for AS conditions and about 0.8 for CS conditions. In addition, the slopes of the regression lines are 1.17±0.01 for AS conditions and 1.20±0.01 for CS conditions. Apart from the strong correlation between SB and GB UVI (nearly the same as for v1.3), all these statistics parameters are significantly different from those produced by v1.3 and much worse. As expected, v1.3 data are more accurate than v1.2 data. Indeed, as observed in Fig. 6 (red dashed lines), the AOD is quite large (Fig. 6a) and the SSA is significantly smaller than unity (Fig. 6b), leading to a CF applied to v1.2 data to account for absorbing aerosols (see Sect. 2.2.1) much smaller than unity (Fig. 6c). As mentioned previously, UVI relative differences in v1.3 do not exhibit seasonal variability, whereas a weak seasonal variability is observed in v1.2 (not shown), possibly related to CF seasonality. The current validation study at VDA demonstrates that v1.3 offline correction for absorbing aerosol is very efficient, even if there remains a positive bias.

At this northern midlatitude site, OMI overpasses occur from 0.25 h before to 2.75 h after solar noon and GOME-2 overpasses take place in the morning (ranging from 3.25 to 1 h before solar noon). The OHP site, located in a mountainous region, is characterized by rather high total ozone columns (on average in the 250–420 DU range) and sometimes by the presence of absorbing aerosols. Surface albedo has a weak seasonal variability in the 0.02–0.05 range.

The first validation is conducted for distance between the GB station and the CTP/grid cell centre point ≤ 100 km.

Results for AS conditions are shown in Fig. 7. Similar to VDA, the GOME-2 data set is limited because only one value per day is available. The data show medium dispersion around relative difference means (SD nearly 50 %, means nearly 21 %), GB and SB UVI are strongly correlated (r∼0.96) and regression lines have a slope larger than unity (1.04±0.01 for OMI, 1.05±0.01 for GOME-2) with a small intercept. Similar to VDA, satellite-derived UVI is generally larger than GB UVI (82 % of positive relative difference for OMI, 72 % for GOME-2). When the COD is smaller than 1, the relative difference is almost always positive for OMI (Fig. 7b), but is less so for GOME-2 (Fig. 7d). Negative differences for large COD values are observed both for low and high UVI, especially for OMI data. Again, negative differences for small COD values generally correspond to low UVI (Fig. 9a and c) and large SZA, especially for GOME-2 (Fig. 10a and c). Median values of the relative biases are about 9.3 % for OMI and 8.4 % for GOME-2. A total of 10 % of the cases correspond to an SB overestimation larger than about 60 % (p90) for both OMI and GOME-2 (Fig. 7 caption), cases identified as red and violet crosses in Fig. 9a and c, corresponding often to UVI < 3 and to a large COD, as observed at VDA. A total of 10 % of the cases correspond to an SB underestimation of more than 8 % for OMI and 12 % for GOME-2 (p10), associated also with UVI < 3 and with a large COD (Fig. 9c).

Figure 8 shows the results obtained for CS conditions. The dispersion around relative difference means is small (SD < 10 %, means < 6 %), SB and GB UVI are strongly correlated (r=0.99) and the slopes of the regression lines are still larger than unity (1.05±0.01 for OMI, 1.09±0.01 for GOME-2). Note that the point well below the regression line in Fig. 8b (red circle) corresponds to an OMI CTP at 98 km distance from the GB site, 1700 m a.s.l. and to a large COD (7.75). Satellite-derived UVI is still often larger than GB UVI (∼ 85 % of cases for OMI, 68 % for GOME-2). The COD is generally small (Fig. 9b and d), indicating that the satellite algorithms perform a good estimate of the actual cloudiness. As was the case for all sky conditions, GOME-2 UVI is generally smaller than GB UVI for UVI values smaller than about 3 (green circles); these cases correspond to SZA > 60∘ (Fig. 10d). Few such cases are observed for OMI; they correspond to a UVI close to 1 (Fig. 10b). The positive median relative bias is 5.8 % for OMI and 4.1 % for GOME-2. SB overestimation is larger than 12 % for both OMI and GOME-2 in 10 % of the cases (p90, Fig. 8 caption). p10 values indicate that 10 % of the cases correspond to an OMI underestimation and more than 12 % to a GOME-2 underestimation. As previously, for GOME-2 these cases correspond to UVI < 3 (violet crosses in Fig. 9d).

The statistics of the results are reported in Tables 1 and 2. The median bias is positive and small for both satellite instruments: 0.32 for OMI and 0.41 for GOME-2 for AS conditions, and about 0.25 for both OMI and GOME-2 for CS conditions.

As for VDA, seasonal variability is observed with differences for both satellite instruments with smaller values in winter. OMI relative differences show no seasonal variability, but GOME-2 relative differences exhibit seasonal variations not explained by the observed weak surface albedo seasonality.

Both satellite overpass times can be quite different from noon, however, no correlation between the relative difference and the time difference is observed (not shown).

As for VDA, we have performed RT calculations also with midlatitude ozone, temperature and pressure profiles. Figure 5d and e show the histograms of the percent relative difference between the computed UVI and the measured one for cloud-free conditions. GB and OMI UVI are 5.4 and 2.2 % smaller than the simulated UVI, respectively. This small underestimation of OMI UVI is well within OMI measurement uncertainty and is caused, as at VDA, by differences between the input parameters (aerosol parameters, surface albedo, etc.) and between the two RT models used. Though rather large, the underestimation of GB UVI is still consistent with GB measurement uncertainty. This bias is explained considering the TOC value. Indeed, as at VDA, the TOC derived from the GB spectra is often larger than OMTO3. Figure 5f shows the UVI relative difference between the computed and the GB UVI vs. the TOC relative difference. The modelled UVI is larger than the GB UVI for a negative TOC relative difference, which is consistent with the 5.4 % bias.

Similar to VDA, TOC from GOME-2 is also sometimes different from OMTO3, and often smaller than spectroradiometer TOC (in agreement with Antón and Loyola (2011). Thus, part of the observed positive biases between SB and GB UVI for cloud-free and cloudy conditions can be explained by OMTO3 and GOME-2 TOC underestimation. At this site also, cloud cover variability within the satellite pixel (Kazadzis et al., 2009b), aerosol climatology (Kinne et al., 2013) and surface albedo climatology (Tanskanen, 2004) might explain part of the biases.

The results for distances between the ground station and the CTP/grid cell centre point ≤ 10 km are reported in Tables 3 and 4. The number of events is much smaller than for the 100 km distance. For AS conditions, the slope of the regression line for OMI data is nearly unchanged compared to the 100 km distance case (1.05±0.02), and the correlation coefficient and the parameters (median bias and median relative bias) are nearly unchanged as well. For GOME-2, though the slope of the regression line is closer to 1 compared to 100 km distance and the correlation coefficient is nearly unchanged, the statistics parameters are slightly worse. p90 is much smaller for OMI (45 %), and unchanged for GOME-2 compared to that for 100 km distance, and p10 is close to the 100 km distances. That means that the filter on the distance has mainly removed cases of large OMI UVI overestimations.

For CS conditions, for both OMI and GOME-2, the regression slopes are not significantly different from those for 100 km distance, and the statistics of the results are very similar, with the exception of the GOME-2 p10 percentile (-19 %) meaning that the underestimation is stronger, though GOME-2 results are statistically less robust than for 100 km because there are only 19 UVI pairs. Thus, the distance between the satellite CTP/grid cell centre point and the GB instrument does not significantly affect the results of the satellite sensor validation.

Since the region is mountainous, the effect of altitude may be evident in the data. The influence of altitude can only be studied with OMI data for which the terrain height is available in the OMUVB files. Tables 3 and 4 report the results accounting for CTP whose altitude is within ±250 m from the ground site altitude, with this value being chosen as leading only to a ±2–3 % shift in erythemally weighted UV (McKenzie et al., 2001). Whether for AS or for CS conditions, the statistics parameters (as well as the regression line slope and correlation coefficient) are very close to those obtained without a filter on altitude. So, the altitude selection does not improve the comparisons between SB and GB data. This is likely due to OMI-estimated cloudiness, OMI spatial resolution and also the fact that CTPs with lower and higher altitude compared to the actual altitude of the site give opposite effects on UVI. Indeed, all these factors play a role in the validations carried out with or without an altitude selection.

Finally, for AS conditions, about 70 % of OMI UVI data and about 67 % of GOME-2 data agree with GB data in the interval [-20, 20 %] (44 % agree in [-10, 10 %]). For CS conditions, about 97 % of both OMI and GOME-2 UVI data agree with GB data in the interval [-20, 20 %] (72 % agree in [-10, 10 %]).

The statistical comparisons restricted to the same dates for both OMI and GOME-2 are reported in Tables 5 and 6. For AS conditions, the correlation between SB and GB UVI data is strong (r∼0.96) for both satellite instruments, and the slopes of the regression lines are larger than 1, with no significant difference. For CS conditions, the correlation is very strong (r∼0.99), the regression slope for OMI is slightly closer to 1 than for GOME-2. For AS conditions, median relative biases between GB and SB UVI data are very close for OMI and GOME-2, but median absolute bias for GOME-2 is larger than for OMI. For CS conditions, the median relative bias for GOME-2 is smaller than for OMI but median absolute bias for GOME-2 is larger than for OMI. This different behaviour between median relative bias and median bias for OMI and for GOME-2 is due to the seasonality of the relative differences and differences for GOME-2 – GB UVI pairs. Indeed, the seasonal variability of differences between SB and GB UVI is greater for GOME-2 than for OMI, and, as observed for VDA, OMI relative differences between SB and GB UVI show no seasonal variability, but GOME-2 relative differences do. OMI and GOME-2 overestimations are very close for AS conditions (p90 about 55 %), and the underestimation is smaller for OMI (p10 about -8 % for OMI, -12 % for GOME-2). For CS conditions, OMI mainly overestimates UVI (p10=-0.5 %), and the GOME-2 underestimation is much larger (p10 about -11 %).

The statistics of the results of the comparison for OMI v1.2 are also reported in Tables 1 and 2. The median relative bias is about 20 % for AS conditions; v1.2 strongly overestimates and weakly underestimates UVI (p90 about 73 %, p10 about 2 %). For CS conditions, the median UVI relative bias is about 17 %, overestimation is large and there is almost no underestimation (p90 about 23 %, p10 about 9 %; 3 % of the cases show a negative relative difference).

Median biases are about 0.8 for AS conditions and about 0.9 for CS conditions. The slopes of the regression lines are 1.14±0.01 for AS conditions and 1.18±0.01 for CS conditions. Thus, all these statistics parameters are significantly larger than those produced by v1.3, indicating that v1.3 product is more reliable than v1.2. This result can be understood by looking at Fig. 6 (black dashed lines). AOD is quite large (Fig. 6a) and SSA is significantly smaller than unity (Fig. 6b), leading to a CF applied to v1.2 UVI that is much lower than unity (Fig. 6c). The current validation study at OHP shows that the correction for absorbing aerosol performed in v1.3 is very efficient, though imperfect.

In the tropical region, OMI overpasses occur in the afternoon from 0.75 to 3.5 h after solar noon and GOME-2 in the morning from 4.25 to 2.25 h before solar noon. As mentioned previously, SDR is characterized by rather low total ozone column (on average in the 240–300 DU range), by the proximity to the ocean, by a complex topography and by a frequent occurrence of clouds forming at around midday. Cloud variability between overpass time and noon is thus high, cloud sub-pixel variation is also high, and therefore, cloudiness estimation is the most important factor of uncertainty in deriving UVI from space measurements. This site may be not representative of satellite pixels because a large part of the area contributing to the satellite measurement is over the ocean where the cloud cover is likely different from that over the mountainous island. As at the other sites, surface albedo has a weak seasonality in the 0.04–0.08 range.

The first validation is conducted for distance between the GB station and the CTP/grid cell centre point ≤ 100 km.

Results for AS conditions are shown in Fig. 11. Similar to other sites, the GOME-2 data set is limited because only one value per day is available. The data show large dispersion around relative difference means (SD nearly 57 %, mean nearly 29 % for OMI and SD nearly 67 %; mean nearly 35 % for GOME-2.). These dispersions and means are larger than at the two other sites. GB and SB UVI are correlated less strongly than at other sites (r∼0.74 for OMI, r∼0.71 for GOME-2), though the correlation is significant since the probability of getting these r values by chance is lower than 0.05 %. The slopes of the regression lines are much smaller than unity (0.91±0.02 for OMI, 0.78±0.03 for GOME-2). Satellite-derived UVI is generally larger than GB UVI (∼ 80 % of positive relative difference for both OMI and GOME-2). When the COD is smaller than 1, the relative difference is almost always positive for both instruments (Fig. 11b and d). Satellite-derived UVI smaller than GB UVI can occur when the COD is large, as seen in Fig. 13a and c. No link with SZA at overpass is observed; indeed, for OMI observations, SZA is almost always smaller than 60∘, and for GOME-2 several cases with SZA at overpass > 60∘ occur but the relative differences are not more negative than for other cases (not shown).

Median values of the relative biases are about 10 % for both OMI and GOME-2. The p90 values indicate that 10 % of the cases correspond to an SB overestimation larger than about 86 % for OMI and 100 % for GOME-2 (Fig. 11 caption); these values are much larger than those observed at VDA and OHP. A total of 10 % (p10) of the cases correspond to an SB underestimation of more than about 9 % for OMI and 6 % for GOME-2.

Figure 12 shows the results obtained for CS conditions. The dispersion around relative difference means is much lower than for AS conditions (SD < 9 %, means < 4 %), the correlations between SB and GB UVI are high, though weaker than at the two other sites (r=0.96) and the slopes of the regression lines are close to unity (1.03±0.02 for OMI, 0.98±0.02 for GOME-2). Several SB–GB UVI pairs show negative relative differences, which correspond to COD > 1. As seen in Fig. 13b and d, the COD can still be large, indicating the difficulty for both satellite algorithms to estimate the actual cloudiness (i.e. no clouds here), though the possibility of a bad selection of CS cases at the GB site cannot be excluded. We have checked that the few points far from the regression line in Fig. 12b and d correspond to a large COD at overpass (for OMI) or at noon (for GOME-2). Satellite-derived UVI is larger than GB UVI in nearly 70 % of cases for both instruments. The positive relative bias is nearly 4 % for both OMI and GOME-2. A total of 10 % of the cases (p90) correspond to an SB overestimation of more than about 14 % for OMI and 11 % GOME-2 (Fig. 12 caption). The p10 percentiles indicate that only 10 % of the cases correspond to an OMI and GOME-2 underestimation larger than about 8 %.

All the statistics of the results are reported in Tables 1 and 2. The median bias is positive: about 0.8 for AS conditions and 0.4 for CS conditions for OMI, about 0.9 for AS conditions and 0.3 for CS conditions for GOME-2. These values are larger than at the two other sites for AS conditions because of the higher UVI levels.

A seasonal variability of the relative difference between GB and SB UVI is observed for both AS and CS conditions for GOME-2, but it seems to be related to the seasonality of the cloudiness rather than to the surface albedo seasonality (not shown).

As at the two other sites, though both satellite instruments overpass at times very far from noon, no correlation between the relative difference and the time difference is observed for AS and CS conditions (not shown).

Radiative transfer calculation results for cloud-free conditions, using tropical ozone, temperature and pressure profiles, are reported in Fig. 5. Figure 5g shows that GB UVI is 3.3 % smaller than simulated UVI, and Fig. 5h shows that OMI UVI is 3.6 % larger, with each bias being smaller than GB and OMI uncertainty, respectively. As at the other sites, this overestimation of OMI UVI is due to differences between the input parameters other than TOC (aerosol parameters (though the aerosol load is small), surface albedo (though this parameter value is small), etc.) and between the two RT models used. Even though the underestimation of GB UVI is within the GB measurement uncertainty, it can be explained since at this site the TOC derived from the GB spectra is also often larger than OMTO3. Figure 5i shows the UVI relative difference between the computed and the GB UVI vs. the TOC relative difference. GB UVI is often smaller than the computed UVI for a negative TOC relative difference, justifying the 3.3 % bias.

Part of the observed positive biases between SB and GB UVI for cloud-free and cloudy conditions can be explained by OMTO3 and GOME-2 TOC underestimation (according to Antón and Loyola, 2011). At this site, aerosol climatology (Kinne et al., 2013) could not contribute much to the biases, since the aerosol load is small. Surface albedo climatology (Tanskanen, 2004) might contribute. According to Kazadzis et al. (2009b), due to the particular situation of SDR (coastal site on a small mountainous island) the cloud cover spatial variability in the satellite pixel should be the main contributor to the SB–GB UVI bias.

The study performed for distances (GB station – CTP/grid cell centre point) smaller than or equal to 10 km gives results similar to that at the two other sites (Tables 3 and 4). For AS conditions, OMI statistics parameters show a slightly better agreement with GB data compared to the 100 km distance case. The median relative bias is about 2 % lower, p90 is much smaller (by about 20 %), though p10, correlation between SB and GB UVI and regression line are nearly the same. For GOME-2, statistics parameters show a worse agreement between SB and GB UVI compared to those for 100 km distance. The median relative bias is unchanged but p90 is larger (by about 15 %), p10 is lower (by about 2 %), correlation is weaker (r∼0.60) and the slope of the regression line is much smaller (0.62±0.09). Finally, for AS conditions, the filter on the distance has removed cases of large SB UVI overestimations for OMI, and for GOME-2, the overestimation has become larger.

For CS conditions, OMI data compare slightly better with GB data than for the 100 km case. The median relative bias is nearly unchanged, the overestimation is lower (p90 lower by about 3 %) and underestimation is stronger (p10 lower by about 5 %). GOME-2 statistics parameters are little changed compared to 100 km distance, the GB and SB UVI data show the same strong correlation and the regression line slope 0.90±0.07 is much smaller than for 100 km distance but the large uncertainty limits the significance of the difference. This latter case is statistically weakly robust because only 16 UVI pairs are available.

Thus, the comparison of surface UVI from OMI is little improved when smaller distance between the satellite CTP and the GB instrument is considered. For GOME-2, the comparison is worse for AS conditions.

Réunion Island is very mountainous so the effect of surface altitude may be evident in OMI comparison. Tables 3 and 4 show the results accounting for CTP whose altitude is within the sea level and +250 m above the site altitude. Whether for AS or CS conditions the statistics parameters are slightly worse, but not significantly, and correlation between SB and GB UVI data is similar. As for OHP site, an altitude selection does not lead to significant changes that is also likely due to the cloudiness estimate and to OMI spatial resolution.

Finally, for AS conditions, about 62 % of both OMI and GOME-2 UVI data agree with GB data in the [-20, 20 %] relative difference interval (40 % agree in [-10, 10 %]). For CS conditions, about 97 % of both OMI and GOME-2 data agree with GB data in the interval [-20, 20 %] (70 and 80 % for OMI and GOME-2, respectively, in [-10, 10 %]).

The statistical comparisons restricted to the same dates for both OMI and GOME-2 are reported in Tables 5 and 6. For AS conditions, the correlation between GB and SB UVI data is not very strong but it is better for OMI (r∼0.74 and r∼0.70 for OMI and GOME-2, respectively). The slopes of the regression lines are much smaller than 1, OMI slope being significantly closer to 1 (0.91±0.03 for OMI, 0.78±0.03 for GOME-2) and intercepts are both large and positive (larger for GOME-2). Median relative bias and absolute bias between GB and SB data are slightly smaller for OMI than for GOME-2. For CS conditions, the correlations are strong and the same for both instruments r∼0.96, both regression line slopes are close to 1 (OMI slope > 1, GOME-2 slope < 1 with a positive intercept), but accounting for the uncertainties the difference is not significant. Median relative biases and absolute bias are slightly larger for OMI than for GOME-2. OMI overestimation is smaller than the GOME-2 overestimation for AS conditions (p90); OMI underestimation is stronger than the GOME-2 overestimation (p10). For CS conditions, OMI and GOME-2 p90 and p10 are very close.

As mentioned previously, GOME-2 relative differences between GB and SB UVI data show a seasonal variability related to cloud presence, while there is no variability for OMI.

The validation of previous OMI v1.2 UVI data with GB data does not show significant differences, as observed in Tables 1 and 2. Indeed, for AS conditions, the correlation between GB and SB UVI data is slightly weaker and the regression line slope is slightly worse than for v1.3 data, but the other statistics parameters are very similar for both versions. For CS conditions, the correlation between GB and SB UVI data is slightly weaker and the regression line slope is nearly the same as for v1.3 data, but the other statistics parameters are worse. Overall, these changes are weak and not significant. The small difference between the v1.2 and v1.3 data sets is due to the small AOD (Fig. 6a, blue dashed line) and large SSA (Fig. 6b). Thus, the correction factor at SDR is close to unity (Fig. 6c).