A study of regional precipitation data fusion model based on BP-LSTM in Qinghai province

Since Qinghai is located in the high-altitude Qinghai-Tibet Plateau region, the geomorphological types are complex and diverse, and the distribution of ground precipitation observation stations is sparse, improving the accuracy of precipitation data is critical for studying regional ecological change over time. In the paper, we study and construct a multi-source precipitation data fusion model based on neural networks, which consists of back propagation neural network (BPNN) and long short-term memory network (LSTM). The global precipitation measurement (GPM), fifth generation ECMWF atmospheric reanalysis (ERA5), digital elevation model (DEM), and normalized difference vegetation index (NDVI) data are selected as feature data and ground observation station data as label data for model training. The results show that the fused data generated by the BP-LSTM model reduces the root mean square error to 2.48mm and the overall relative bias to 0.25% compared with the original GPM, which is better than ERA5 on data accuracy. The precipitation event capture capability is improved, which is very close to the ERA5 data with strong precipitation event capture capability, and the probability of detection, false alarm rate, and missing event rate are 0.95, 0.53, and 0.04 respectively. Finally, the regional precipitation data is generated by the fusion model with resolution of 0.01°, 1h. The model proposed in the paper incorporates topographic factors and seasonal characteristics to solve the temporal and spatial correlation of precipitation data in Qinghai Province improve the accuracy of precipitation data, and provide reliable data support for the study of regional hydro-ecological spatial and temporal variation patterns.