Abstract: Wind profile radar(WPR), taking atmospheric turbulence of clear air as main detecting object, is the main reference tool currently for short-time forecast because of its high spatial and temporal resolution. In the past few decades, WPR spectral data processing mainly focuses on the wind spectrum estimation. In recent years, with the use of WPR data expansion, there are increasingly high demand for WPR data accuracy, but because of ground clutter and external noise, flying objects, the presence of disturbances such as precipitation and limitations of Fourier Transform method itself, there are often multiple overlapped peaks, which makes it difficult to judge the spectral meaning, resulting in large error detection products. WPR has a large dynamic reception range, so it can receive the echo of scattering of atmosphere turbulence and precipitation particles simultaneously. However, the superimposed spectrum of atmosphere and precipitation cannot be separated effectively. In the meantime, the wind field calculation is based on the hypothesis of local uniform and isotropy, which cannot be met during precipitation with great spatial variability and leads to data of WPR serious deficiency or distortion. It's of great importance to establish an effective spectral extraction programs under different weather conditions to improve the accuracy of spectral estimation for wind field data after the inversion, thereby enhancing the wind profile accuracy of radar detection.A method of WPR-HW is developed for the case of precipitation according to the principle of WPR detection and the feature of spectrum, and then the effectiveness of the method is tested using ECMWF ERA Interim data. 10 precipitation cases in Tianjin are investigated to verify the significance of wind field data processed by WPR-HW in severe convection prediction. Results show that the WPR-HW has significant advantage compared with the recent WIND method (the universal method of wind field inversion from WPR) in integrity and reliability. For the wind field data in 10 precipitation cases, the leakage rate of WIND is 25.4% while that of WPR-HW is 0. The root mean square error in wind speed of WPR-HW is 1.6 m·s^-1 while that of WIND is 2.3 m·s^-1. The RMSE in wind direction of WPR-HW is 22° while that of WIND is 45°. The wind field processed by WPR-HW is able to make up for the deficiency and distortion of WPR data effectively in precipitation, which thus benefit to improve the timeliness and accuracy in strong convective weather forecasting.