Quantitative precipitation estimation method using S-band dual polarization radar under convective scale ensemble simulation - Wang Bo; Liu Xiaolin; Hua Shenbing; Jin Shuanglong (Discover Applied Sciences)
Abstract S-band radar beams are easily obstructed by terrain during propagation. After the beam is blocked, the radar cannot receive the echo signal of the target area, forming a data blind spot. Traditional methods cannot obtain a complete precipitation` dataset, which increases the difficulty of precipitation estimation and leads to errors in precipitation estimation results, resulting in lower scores. This study conducted quantitative precipitation estimation using S-band dual-polarization radar under a convective scale ensemble simulation. Firstly, a certain region in Hebei Province was selected as the research object to conduct convective scale ensemble simulation to obtain more precipitation datasets. Then, the precipitation data was smoothed and used to invert the radar precipitation intensity every 6 min. The estimated hourly rainfall of the radar was matched with the hourly rainfall measurement of a single-point rainfall station. Finally, based on deep learning theory, a quantitative precipitation estimation model for S-band dual-polarization radar was constructed. The experimental results show that using the proposed method, the root mean square error (RMSE) value is less than 0.372, the mean absolute error (MAE) is less than 0.247, the correlation coefficient (CC) value is higher than 94.7%, the TS score is higher than 95.1%, and the quantitative precipitation estimation effect is good.