Parameter estimation of a dual-pol radar rain rate estimator with truncated paired data

Abstract

This study proposes a new method for estimating the parameters of a radar rain rate estimator, particularly of the dual-pol radar. The proposed method is similar to the probability matching method (PMM), except for being based on truncated data pairs. A truncation value is introduced to the log-transformed data in order to remove those in the low rain rate zone as well as to introduce Gaussian distribution. The parameters are then estimated by comparing the first- and second-order moments. The proposed method is applied to a total of six rainfall events observed by the Beaslesan Radar in Korea from 2014 to 2017. Summarizing the results, first, the truncation value should be applied to the horizontal reflectivity (dBZ(h)) data. In this case only, the other two data, the rain rate (dBR) and the differential reflectivity (dBZ(dr)), follow the Gaussian distribution well. It is also important to consider rather severe rainfall events for the parameter estimation. The parameters for only the severe rainfall events are all estimated rather reasonably. On the other hand, for the other moderate and light rainfall events, the parameters are estimated as being rather far from their normal ranges. This is mainly due to the relatively small variance of dBR compared to that of dBZ(h). That is, the variance of dBR is found to be greatly dependent on the peak rain rate, but the variance of dBZ(h) remains almost unchanged, regardless of the peak rain rate. As a result, the peak rain rate plays a dominant role in the reasonable parameter estimation. These findings are also consistent with many previous studies.