Abstract: Wind profiler radar (WPR) is a new type of wind measuring radar, which has advantages of high spatial resolution, continuity and good instantaneity. With the increase of wind profile radar year by year, it is meaningful to apply this kind of wind field observations to the numerical model to improve the model prediction ability. The meso-scale numerical prediction model WRF and the assimilation system ADAS developed by Center for Analysis and Prediction of Storms, University of Oklahoma, is used to study effects of assimilating observations of 35 wind profiler radars in eastern China on precipitation prediction over Zhejiang. Prior to assimilation, 1 h average sampling product data are subjected to climate extreme inspection, consistency check and vertical thinning for quality control. A spring rainstorm process on 16-17 May 2014 is selected as an example to evaluate effects of WPR data assimilation on the quality of precipitation forecast in detail. And effects of WPR data are also verified by batch experiments starting from 0000 UTC and 1200 UTC during the whole June of 2015. Results show that the model precipitation TS and ETS scores are improved, especially for heavy rainfalls. At the same time, the false alarm ratio (FAR) and frequency of misses (FOM) for heavy and torrential rain decrease after WPR data assimilation, but the FAR of moderate rain increase. The case study shows that WPR data assimilation can adjust the initial field of low layer wind field, increase small scale weather information, and improve the horizontal wind prediction on the whole layers. For 12 h wind forecast field, the result of assimilation of WPR is obviously better than that without the assimilation. In addition, the improvement of the zonal wind is more obvious than that of the meridional wind after WPR data assimilation. The case study shows that 850 hPa wind speed is enhanced by 20%-30%, water vapor flux is increased by 30%-50%, and the atmospheric instability in the rainstorm area and its upstream region is also enhanced after WPR data assimilation. As a result, TS of light rain and heavy rain is increased by 0.06-0.07, and FAR and FOM of rainstorm is reduced by 0.04-0.05. Although the assimilation of wind profiler data can improve the precipitation prediction quality, there are still some problems, such as an unexplained overestimation of regional average precipitation, which needs further investigation.