Precipitation Nowcasting Based on an Optimized Deep Learning Model Trained with Heterogeneous Weather Data

    Due to the threat of extreme rainfall associated with mesoscale convective systems and summer afternoon thunderstorms, very short-term quantitative precipitation forecasting during 0−3 h is critical in Taiwan. In this study, deep learning models are developed for high-resolution quantitative precipitation nowcasting in Taiwan up to 3 h ahead. The baseline model based on the convolutional recurrent neural network is trained with a dataset containing radar reflectivity and rain rates at a granularity of 10 min. As previous works tend to produce overprediction in low-rainfall regions, the currently proposed model is improved and further driven by highly related heterogeneous weather data, including visible channel satellite observation, environmental winds, and environmental thermo-dynamical profiles. Note that an innovative “PONI module” is added to the deep learning model to integrate a variety of heterogeneous data with various spatial and temporal characteristics. Moreover, model performance is evaluated from statistical and spatial rescaling perspectives represented by R =  R_avg + R'_, where R denotes original rainfall, R_avg and R' are spatial moving averages and the values deviated from R_avg, respectively. Statistical verification shows that the R_avg of the new model outperforms the previous model, while the performance of R' is comparable. The new model integrated with heterogeneous data selected upon domain knowledge can restrain the nowcasts that overestimate in low-rainfall regions. Last but not least, quasi-operational verifications against other state-of-the-art techniques in Taiwan Central Weather Bureau are presented as follows: (1) the CSI of the first-hour prediction from the deep learning model is comparable with QPESUMS-QPF and better than RWRF and iTeen. (2) 3h ahead prediction CSI of RWRF and iTeen are inferior to the performance of deep learning model owing to their misprediction of rainfall regions. The deep learning model can accurately predict medium and extreme amounts of precipitation at a fraction of the computational cost.