Short-Term Dynamic Radar Quantitative Precipitation Estimation Based on Wavelet Transform and Support Vector Machine

Currently, Doppler weather radar in China is generally used for quantitative precipitation estimation (QPE) based on the Z–R relationship. However, the estimation error for mixed precipitation is very large. In order to improve the accuracy of radar QPE, we propose a dynamic radar QPE algorithm with a 6-min interval that uses the reflectivity data of Doppler radar Z9002 in the Shanghai Qingpu District and the precipitation data at automatic weather stations (AWSs) in East China. Considering the time dependence and abrupt changes of precipitation, the data during the previous 30-min period were selected as the training data. To reduce the complexity of radar QPE, we transformed the weather data into the wavelet domain by means of the stationary wavelet transform (SWT) in order to extract high and low-frequency reflectivity and precipitation information. Using the wavelet coefficients, we constructed a support vector machine (SVM) at all scales to estimate the wavelet coefficient of precipitation. Ultimately, via inverse wavelet transformation, we obtained the estimated rainfall. By comparing the results of the proposed method (SWT-SVM) with those of Z = 300 × R^1.4, linear regression (LR), and SVM, we determined that the root mean square error (RMSE) of the SWT-SVM method was 0.54 mm per 6 min and the average Threat Score (TS) could exceed 40% with the exception of the downpour category, thus remaining at a high level. Generally speaking, the SWT-SVM method can effectively improve the accuracy of radar QPE and provide an auxiliary reference for actual meteorological operational forecasting.