# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 4 Results of the Tilt Estimation Method  
Figure 3 shows the positional offsets (radar recorded position minus solar known position) of the identified solar interference observations from the RAiN-E project through time, with the azimuth record shaded by solar elevation in Figure 3 (Panel A) and the elevation record conversely shaded by solar azimuth in Figure 3 (Panel B) to highlight any potential dependencies through time. For an accurately pointing radar with zero tilt, the identified solar interference would vary around zero offset in both azimuth and elevation as the radar traverses the solar disk. However, these results show significant variation in both directions. The elevation time series consistently shows a positive offset, which increases in magnitude from July 2019 and then splits in summer 2020 with observations of low/high azimuths displaying increasing/decreasing trends, respectively. These initial findings indicate significant errors (\(>\)0.2\({}^{\circ}\)) in the radar pointing accuracy, which can potentially be attributed to changes in NXPol-1's tilt. It is also noticeable that solar hits are observed more frequently after July 2019, when the radar receiver board was replaced due to a fault, which also coincides with a change in the relative offset between the radar-observed solar flux and the equivalent observations from DRAO, as shown in Figure 3 (Panel C). Also shown in Figure 3 are the site visits detailed in Table 1. These interventions subdivide the time period into the nine periods shown in Table 2, which vary in length from 7 to 344 days, with only the longest sector (Period 8) exceeding [PERSON] et al. (2016)'s recommended length of 16 weeks for a stable diagnosis of antenna tilt. Table 2 also indicates that NXPol-1 observed significantly fewer (fewer than half as many per day) solar interference spikes than other systems outlined in the literature, particularly during the first eight months of the deployment, as a result of the reduced sensitivity of NXPol-1 and the scanning geometry. To analyse each of these periods, the combined methodology outlined above is applied to each separately. Figure 4 is an example of this approach for Period 8, while Table 3 summarises the results for all of the time periods. To determine the correction for each time period, an informed assessment of the consistency of these results is undertaken. To demonstrate this, we here take Period 8 (Figure 4) as a detailed example, from which it can be observed that: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Period** & **Start Date** & **End Date** & **Length (Days)** & **N** & **N/Day** \\ \hline
1 & 1 November 2018 & 8 November 2018 & 7 & 40 & 5.7 \\
2 & 8 November 2018 & 13 December 2018 & 35 & 75 & 2.1 \\
3 & 13 December 2018 & 14 February 2019 & 63 & 130 & 2.1 \\
4 & 14 February 2019 & 15 April 2019 & 60 & 48 & 0.8 \\
5 & 17 April 2019 & 30 April 2019 & 13 & 39 & 3.0 \\
6 & 4 July 2019 & 25 September 2019 & 83 & 285 & 3.4 \\
7 & 25 September 2019 & 29 October 2019 & 34 & 97 & 2.9 \\
8 & 5 November 2019 & 14 October 2020 & 344 & 1607 & 4.7 \\
9 & 14 October 2020 & 18 December 2020 & 65 & 579 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time periods analysed during the project. Each is separated by a maintenance visit (Table 1). The number of solar hits identified during each period is shown along with the average number per day. * A significant number (>40%) of points are excluded between the first and second fit of the bullseye model, which is accompanied by a large reduction in the RMSE from 1.34 to 0.50 (seen on the lower-right panel of Figure 4). This suggests that a Gaussian model with fixed offsets does not model the observations well. * The estimated solar power offset of 1.64 dBfu exceeds expectations based on Figure 3, again indicating the fitted model is not characterising the observations.