Multisource Precipitation Data Merging Using a Dual-Layer ConvLSTM Model

Precipitation is a key component of the water cycle. Different precipitation data sources have strengths and weaknesses. To combine these strengths and achieve accurate precipitation data, this study introduces a dual-layer neural network (D-ConvLSTM) based on a convolutional long short-term memory neural network (ConvLSTM) that integrates ground station data (1 h interval) and grid precipitation data generated by the China Meteorological Administration Multi-source merged Precipitation Analysis System (CMPAS, 1 h interval, 0.05° × 0.05°) through a two-layer network for precipitation identification and correction. To evaluate the performance of the proposed model, D-ConvLSTM, optimal interpolation (OI), and a single-layer ConvLSTM model are evaluated in the Dadu River Basin, China. The results show that D-ConvLSTM outperforms the CMPAS in all the metrics compared with the OI and ConvLSTM, with improvements of 18.9% and 19.8% in the critical success index (CSI) and Kling–Gupta efficiency (KGE), respectively. D-ConvLSTM enhances gridded precipitation under various conditions, including areas without station data, different intensities, and regions. Furthermore, this study analyzes the impact of training data distribution on the performance of the D-ConvLSTM model and enhances model performance by adjusting the training data distribution. The analysis reveals that the ratio of dry to wet data in the training set affects the model’s identification performance. The ratio of overestimation to underestimation of gridded data compared with station observations influences value correction. This study offers a new model for merging station and gridded precipitation data and provides insights for enhancing the accuracy of neural network merging.