{'fraction_non_alphanumeric': 0.025160335471139616, 'fraction_numerical': 0.0059200789343857915, 'mean_word_length': 5.693069306930693, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

Heavy precipitation tends to cause mountain torrents, urban waterlogging and other disasters. It poses a serious threat to people’s life and property safety. Therefore, real-time quantitative precipitation estimation is especially important to keep track of precipitation changes and reduce negative impacts. However, high-resolution and high-accuracy quantitative precipitation estimation is a challenging task due to the complex spatial and temporal variability of microphysics in precipitation processes. Previous studies have focused only on small-scale radar reflectivity factors above rain gauges and did not pay enough attention to the contribution of covariates to model performance. Meteorological and geographical factors play an important role in rain process, so these factors are taken into account during our research. In this study, a quantitative precipitation estimation model that can employ multi-scale radar reflectivity factors and fuse meteorological and geographical factors is proposed to further improve precipitation accuracy. In addition, we propose the muti-scale self-attention (MS-SA) module that can further utilize information at multiple spatial scales to improve the accurate precipitation estimation. The proposed model reduced the root mean square error of precipitation estimation by 83.8% compared to the conventional Z-R relationship that correlates the rainfall and radar reflectivity factors, i . e . , Z = a R b , and by 43.7, 24.6, and 22.7% compared to the back propagation neural network (BPNN), convolutional neural network (CNN), and convolutional neural network with the addition of meteorological factors and geographical factors as covariates in the proposed model, respectively. Therefore, we can conclude that multi-scale radar reflectivity factors fused with meteorological and geographical factors can produce more accurate precipitation estimation.