# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 3 Results
### Antarctica Data  
The time series plots of the four parameters at SNA0P station on 2 April, 13 October 2016 and 9 May 2016 are shown respectively in Figure 11.  
As shown in Figure 11, S4 does not have any high values on all three days whereas \(\sigma_{\varphi}\) still fluctuates in accordance with the feature of scintillation occurrence observed at high latitudes. Therefore, S4 is not considered for comparison in the following paragraphs. Furthermore, it can be seen that all the other three parameters are mainly noisy during the last three hours on 2 April 2016, between 18 and 21 UT on 13 October 2016 and first two hours on 9 May 2016. Different from the data of SA00P station (Figure 3), the number of satellites affected by scintillation and MP is similar. Figure 12a-c shows the time series plots of the four parameters obtained from a single satellite at SNA0P station respectively on 2 April, 13 October 2016 and 9 May 2016. As shown in Figure 12, a similar high value period can be seen in plots of four parameters on 2 April 2016 and 9 May 2016 though a minor delay exists on 13 October 2016, which supports the temporal relationship between MP, ROTI and \(\sigma_{\varphi}\). As shown in Figure 13, the mean value maps are not as distinct as those in Brazil which can be caused by the sparse distribution of data at the high latitude area. Therefore, the occurrence percentage maps are more useful to this dataset in order to show more details. However, it can still be seen that the high value areas of \(\sigma_{\varphi}\) maps include those of MP and ROTI maps. The SSIM and CC values between the parameters shown in Figure 13a-c are illustrated in Tables 12-14 respectively. As shown in Tables 12-14, SSIM values between MP&ROTI and \(\sigma_{\varphi}\) are all higher than 0.6, where most exceed 0.7. CC values in Tables 12-14 show low correlation between MP and \(\sigma_{\varphi}\), the correlations between ROTI and \(\sigma_{\varphi}\) are high. Figure 11: Time series plots of four parameters at SNA0P station: (**a**) during 18:00:00–24:00:00 UTC on 2 April 2016; (**b**) during 18:00:00–24:00:00 UTC on 13 October 2016; (**c**) during 00:00:00–06:00:00 UTC on 9 May 2016. Figure 12: Time series plots of four parameters at SNA0P station on: (**a**) during 18:00:00–24:00:00 UTC on 2 April 2016 for PRN03; (**b**) during 18:00:00–24:00:00 UTC on 13 October 2016 for PRN17; (**c**) during 00:00:00–06:00:00 UTC on 9 May 2016 for PRN08. Figure 13 shows the mean value maps as a function of IPP latitude and longitude of the three parameters. Figure 14 shows the variograms and cross variograms for the results presented in Figure 13. The variograms and cross-variograms do show evidence of a common spatial structure in the three parameters; however, this is less clear than those for the Brazil data. In particular there is no evidence of spatial correlation for the data from 2016 April 2 (Figure 13a). For 2016 October 13 the variograms and cross-variograms show a clear common spatial structure with a range of approximately 3.5\({}^{\circ}\). For the dataset from 2016 May 9 there is weak evidence of spatial structure. Figure 15 shows the occurrence percentage maps as a function of IPP latitude and longitude for the three parameters respectively on 2 April 2016, 13 October 2016 and 9 May 2016. \begin{table}
\begin{tabular}{c c c c c} \hline  & & **MP** & & **ROTI** \\ \hline Criteria & SSIM & CC (\(p\)-value) & SSIM & CC (\(p\)-value) \\ \hline \(\sigma_{\varphi}\) & 0.61 & 0.42 (\(<\)0.01) & 0.79 & 0.69 (\(<\)0.01) \\ \hline \end{tabular}
\end{table} 
Table 14: SSIM and CC between the maps shown in Figure 13c. Figure 13: Mean value maps of three parameters at SNA0P station: (**a**) during 18:00:00–24:00:00 UTC on 2 April 2016; (**b**) during 18:00:00–24:00:00 UTC on 13 October 2016; (**c**) during 00:00:00–06:00:00 UTC on 9 May 2016. Figure 14: Figure 14a–c shows the variograms and cross-variograms associated with Figure 13a–c.