6 Summary and Conclusion

Calibration and methods to improve calibration bias estimates serve as the main thread of the three papers comprising this thesis. This work tells the story of how the calibration bias was first noticed for the Subic radar, to the attempts at using spaceborne radars as reference for bias estimation for two overlapping radars (Subic and Tagaytay) experiencing different error sources, to investigating whether miscalibration of archived radar data can be corrected to a certain extent.

The first paper established the presence of miscalibration for the Subic radar, while also giving a first look at the unique rainfall distribution following an intense rainfall caused by typhoon-enhanced monsoon. In this chapter, we have shown that most of the rainfall during the Habagat 2012 actually precipitated over Manila Bay, and not in Quezon City, as the rain gauges recorded. The gap in the spatial distribution of the rain gauges was supplemented by the high-spatial resolution data of the radars.

An approach by Schwaller and Morris (2011) to compare spaceborne radars (SR) and ground radars (GR) for calibration bias estimation was extended in the second paper. A framework for data quality and quality-weighted averaging was introduced, where a quality index based on beam blockage fraction (where \(Q_{BBF}=0\) for total beam blockage and \(Q_{BBF}=1\) for absence of any beam blockage along the beam) was used as weights in calculating the weighted mean difference between SR and GR measurements. The use of quality-weighted averaging, as opposed to simple averaging, decreased the standard deviation of the mean differences between SR and GR reflectivities, thereby increasing the consistency between the two radars. This decrease in standard deviation means that the estimation of the bias is more reliable. Based on the map of the matched SR–GR bins and the scatter plot of the differences between SR and GR reflectivities colored based on \(Q_{BBF}\), the points on the scatter plot lying far from the perfect match come from the areas affected by beam blockage.

We took this concept of quality-based calibration bias estimation further in the third paper and applied it to the neighboring Tagaytay radar (TAG) which overlaps with the Subic radar (SUB). For days when data for all three radars (SR, TAG, SUB) were present, we showed that the agreement of the reflectivity measurements in the overlapping area between the two ground radars increased after bias correction.

Furthermore, a moving average interpolation of the SR-derived biases was able to fill in the gaps in the radar calibration time series where no SR overpasses were available. We demonstrate that taking a moving average (therefore assuming that the bias drifts slowly in time) of the bias to correct for days without an SR overpass produces better results than simply taking the seasonal average and using that to correct all days within that season.

In support of reproducibility and transparency in the atmospheric sciences, all software used in this thesis are reported in the text, and workflow scripts and sample data are made available in publicly available Github repositories.

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