Application of raindrop size distribution data from a disdrometer network to quantitative precipitation estimation

The objective of this study is to investigate the possibility of using drop size distribution data to assess the bias of radar echo intensity and to estimate precipitation quantitatively by fitting Z-R relationship in real time. Taking three precipitation processes occurred in the south of Jiangsu Province as examples, we have analyzed the consistency among the raindrop size distribution data, radar data and the rain gauge data, and then estimated precipitation by using disdrometer network method and traditional method, respectively, with comparing the precipitation estimation's accuracy of the two methods. The results show that the echo intensity observed by radar and disdrometers have a good consistency. While the averaged rainfall intensities calculated by rain gauge data and disdrometers data have some differences in magnitude, their trends showed a general consistency. Error of the two methods is bigger for convective than for the stratiform precipitation estimates. The disdrometer network method has a clear advantage with respect to the traditional method when the convective precipitation is estimated. But when the stratiform precipitation is estimated, the disdrometer network method is slightly better than the traditional method, whose bias and relative error are smaller and the estimated precipitation total is closer to rain gauge observation. From overall evaluation results, the skill of precipitation estimation by using disdrometer network method is better than the traditional method.