Radar Reflectivity and Meteorological Factors Merging-Based Precipitation Estimation Neural Network

Abstract

The meteorological factors are important determinants of the surface rainfall. However, in studies of quantitative precipitation estimation (QPE) based on Doppler radar data, meteorological elements are usually used as the weighting factors to correct precipitation, and the active role of meteorological factors in determining rainfall is neglected, which limits the improvement of radar QPE accuracy. In this study, the effectiveness of applying one-dimensional convolutional neural network together with radar data and meteorological factor data to estimate precipitation is explored. Various combinations of meteorological factors were tested for the set of input variables. The proposed model performance was evaluated over the Shijiazhuang area at the spatial resolution of 0.01° and at the 6-min time scale. The results indicates that the proposed model (RM-1DCNN) provides more accurate precipitation estimation compared to the Ordinary Kriging interpolation, two Z-R relationships, and Back Propagation Neural Network. The root mean square error of the RM-1DCNN with temperature was 0.642 mm per 6 min and the average Threat Score exceed 55%, which was the best among all schemes.