Tests of quantitative precipitation estimates using national weather service dual-polarization radar in Missouri

Abstract

Flash flooding is the most common and widespread threat associated with severe weather. Therefore, it is essential for forecasters to be able to properly assess the risk of flash flooding in order to issue watches and warnings. The underestimation of rainfall accumulation by radar algorithms often leads to undiagnosed flash flooding, so it is necessary to determine which type of quantitative precipitation estimation (QPE) equation best assesses the actual amount of rain that has fallen at a given location (Ryzhkov et al. 2005). By comparing the radar estimated rainfall to the accumulated precipitation measured by rain gauges, the bias and error of the QPE algorithms can be assessed. In the following study, these measurements will be compared for significant rainfall events that occurred across the state of Missouri in 2014. The data from twelve individual rain gauge sites, which are considered to provide the "ground truth" rainfall quantities (Kitchen and Blackall, 1992), are measured against the estimated rainfall calculated by the three National Weather Service radars. Also included is an analysis of whether gauge distance from the radar location has an effect on the error and bias. Various quality control (QC) methods are applied to the radar parameters in order to determine whether or not they enhance the outcomes of the statistical testing applied to the radar data. The results show that R(Z, ZDR) type equations produce the best data, as they give error and bias calculations closest to zero.