Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate (R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-Rrelationship (Z=300R1.4), the optimal Z-Rrelationship (Z=79R1.68) and the GRU neural network with only Zas the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-Rrelationship performs the worst. The performances of the rest two methods are similar.