# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 3 Solar Interference Assessment Methodology
### Combined Methodology  
Both the bullseye (2D Gaussian) analysis and the tilt analysis provide complementary information that can inform the most probable correction required for already collected data or the likelihood that corrective maintenance is required when used in real-time. To analyse historical data, the dates of site visits are treated as boundary conditions, where each intervening period is considered individually unless the analysis for consecutive intervals suggests they can be combined. Key factors to consider when determining the correction required are the RMSE of the bullseye fits along with the number of points removed as outliers, the magnitude and direction of the modelled tilt, and the consistency between the fixed offset between the two methods. Another necessary factor to consider is the quality of the radar data as a whole, particularly whether the wider radar observations are consistent with the modelled results. For example, where the results indicate a significant tilt, the melting layer signal observed in a PPI would be more oblate in shape, while patterns would also be visible in ground clutter returns and beam blockage at low elevations. Considering each of these factors in turn, high original RMSE's along with large reductions in RMSE between the two fits and removal of numerous points by the 1 dB filter are indicative of potential tilt, where the bullseye model fails to fit the observations well due to the points having a bi-modal distribution. This leads to the 2D Gaussian being fit to pass through the two modes with a peak between them, thus also overestimating the solar power delta and leading to filtering of points that lie between the two modes. The declination of the tilt also needs careful consideration, as declination results aligned near north could be due to the semi-sinusoidal path of the sun through the sky, which becomes particularly evident at higher latitudes. In this case, the slow procession of the sun through the sky at relatively low elevation angles creates a sinusoidal track in the offset results, which can lead to the misdiagnosis of tilt aligned to north, particularly as the solar power delta of these hits is not considered when assessing radar axis tilt. The magnitude of the inclination is easier to consider, with high magnitudes less likely to occur and easier to visually confirm with inspection of PPIs at low elevation. For example, visual inspection of PPIs during widespread stratiform precipitation can indicate whether beam blockage has increasedpreferentially in a certain direction, which could be an indicator of tilt. This may also be visible as an elliptical or oblate melting-layer signal within the PPIs, particularly when viewing a field such as the co-polar correlation coefficient as the sweep no longer cuts through the melting layer at a constant range for each azimuth (care must be taken that this is not a function of heterogeneous temperature profiles though). Through consideration of these factors, it is possible to determine a pointing correction for historical data even when time periods are data sparse; this leads to improvements in radar data quality, as will be discussed in the following two sections.