Identification of the Reflected Signal in the COSMIC-2 Occultation Signal Using the Improved GoogLeNet Deep Learning Model

As an advanced atmospheric detection method, GPS (Global Positioning System) occultation detection technology has been widely used in numerical weather forecasting, climate, and space weather research. One of the problems in occultation detection is that it is easily interfered with by the reflected signals on the surface of the earth. Identifying and separating the reflected signals in the occultation detection signal helps assimilate the occultation data into the numerical weather prediction system, which has considerable importance. This study proposes a deep learning model based on improved GoogLenet (Im-GNet) model and applies it to COSMIC-2 occultation detection data to identify reflected signals. This study selects the COSMIC-2 occultation data (conPhs file) from 1 January to 9 January 2020. After quality control, the radio holography method is used to obtain the spatial spectrum image of the occultation signal, and the Im-GNet deep learning model is trained. The accuracy rate of the Im-GNet model test reached 96.4%, which is significantly higher than the result of the support vector machine method. This study also analyzes the impact of reflected signals on occultation data. The geographic distribution of occultation events and the refractivity comparison between the occultation inversion data (atmPrf file) and the NCEP (National Centers for Environmental Prediction) 12-h forecast files (avnPrf file) shows that the quality of the occultation event data with reflection signals is better, and the atmospheric information contained is richer.