A neural network-based approach for the detection of heavy precipitation using GNSS observations and surface meteorological data. (15th November 2021) - Li, Haobo; Wang, Xiaoming; Zhang, Kefei; Wu, Suqin; Xu, Ying; Liu, Yang; Qiu, Cong; Zhang, Jinglei; Fu, Erjiang; Li, Li
Abstract: Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. In this study, a neural network-based (NN-based) approach taking seven meteorological variables as input data was developed based on the back propagation (BP) algorithm for detecting heavy precipitation. Apart from the surface meteorological variables of temperature, pressure and relative humidity, the model has also adopted other information such as day-of-year, hour-of-day and GNSS-derived zenith total delay and precipitable water vapor (PWV) as input variables. The feasibility of using these variables for developing the BP-NN-based model was elaborated by conducting the feature analysis of the seven input variables. In addition, the criterion for selecting a proper size of training sample was also briefly investigated by studying the impact and sensibility of the sample lengths in the model. The proposed model was developed using a sample size of an 8-year (2010–2017) period in the summer at a pair of co-located GNSS/weather stations−HKSC-KP in Hong Kong. The use of a long-term data is to "reliably" capture the characteristics of the selected variables. The detection results for the summer months in 2018 and 2019 were then compared against corresponding precipitation records to valid the effectiveness of the newly proposed model. Results of the correct detection and false alarm rates were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models. Graphical abstract: Image 1 Highlights: Recent years have witnessed a growing interest in using GNSS observations to detect heavy precipitation. A neural network-based approach taking seven meteorological variables as input data was developed based on the back propagation algorithm for detecting heavy precipitation. The rationality of using those variables for developing the new model was elaborated by conducting a preliminary feature analysis. The criterion for selecting a proper size of training sample was briefly investigated by studying the impact and sensibility of the sample lengths in the new model. Results of the correct detection and false alarms were 94.5 % and 20.8 %, respectively, which were significant improvements compared with the existing models.